Method, device, equipment, medium and product for predicting sequence cleavage position

Abstract

The application discloses a sequence cutting position prediction method, device, equipment, medium and product. The method comprises the following steps: acquiring a biomolecule sequence corresponding to a first organism; acquiring a molecular acting substance; and predicting a sequence cutting position corresponding to the biomolecule sequence based on a relative position relationship between the molecular acting substance and the biomolecule sequence. The above method can analyze the sequence cutting position that is most likely to be cut when the molecular acting substance acts on the biomolecule sequence, and is helpful for more accurately analyzing the disease risk of the first organism.

Description

Technical Field

[0001] This application relates to the field of bioinformatics, and in particular to a method, apparatus, device, medium, and product for predicting sequence cutting positions. Background Technology

[0002] The rapid development of biotechnology has greatly contributed to the advancement of medicine. Accurate analysis of changes in each stage of the cell life cycle is of great significance for disease prevention and early diagnosis.

[0003] Research has found that cell-free deoxyribonucleotide (cfDNA) is a fragment of DNA released from cells into the bloodstream, typically when cells die or are damaged. For example, when cellular DNA is cleaved by enzymes under normal or pathological conditions, the resulting cfDNA enters the bloodstream. Related techniques usually identify the DNA nick at the time of cfDNA release and analyze the base frequency around the nick to reveal the state of the cfDNA fragment, thus analyzing the cause of cell death, such as determining whether a disease exists or its type.

[0004] However, the above process is still limited to analyzing individual cfDNA fragments in an individual's blood, resulting in poor comprehensiveness of the analysis and making it difficult to achieve the goals of disease prevention and early diagnosis. Summary of the Invention

[0005] This application provides a method, apparatus, device, medium, and product for predicting sequence cleavage positions. It can analyze the most likely cleavage positions of the biomolecular sequences by using the biomolecular sequences associated with a first organism and additionally provided molecularly active substances, thus helping to more accurately analyze the disease risk of the first organism. The technical solution is as follows.

[0006] On the one hand, a method for predicting sequence cutting positions is provided, the method comprising:

[0007] Obtain the biomolecular sequence corresponding to the first organism, wherein the biomolecular sequence includes at least two molecular sites;

[0008] Obtain a molecularly active substance, which is used to cleave the biomolecule sequence;

[0009] Based on the relative positional relationship between the molecular active substance and the biomolecule sequence in the cell structure data, the sequence cleavage position corresponding to the biomolecule sequence is predicted, and the sequence cleavage position is used to characterize the cleavage position of the molecular active substance in the at least two molecular sites.

[0010] On the other hand, a device for predicting sequence cutting positions is provided, the device comprising:

[0011] An acquisition module is used to acquire the biomolecular sequence corresponding to the first organism, wherein the biomolecular sequence includes at least two molecular sites;

[0012] The acquisition module is also used to acquire molecularly active substances, which are used to cleave the biomolecule sequence;

[0013] The prediction module is used to predict the sequence cut position corresponding to the biomolecule sequence based on the relative positional relationship between the molecular active substance and the biomolecule sequence in the cell structure data. The sequence cut position is used to characterize the cut position of the molecular active substance in at least two molecular sites.

[0014] On the other hand, a computer device is provided, the computer device including a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, the at least one program, the code set or instruction set being loaded and executed by the processor to implement the sequence cutting position prediction method as described in any of the embodiments of this application above.

[0015] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction, at least one program, code set, or instruction set is stored therein, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the sequence cutting position prediction method as described in any of the embodiments of this application above.

[0016] On the other hand, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the sequence cutting position prediction method described in any of the above embodiments.

[0017] The beneficial effects of the technical solutions provided in this application include at least the following:

[0018] Based on the relative positional relationship between the molecularly acting substance and the biomolecular sequence, this study predicts the most likely sequence cut site when the molecularly acting substance interacts with the biomolecular sequence. This allows for the prediction of biological abnormalities in a first organism through the sequence cut site. Using the biomolecular sequence associated with the first organism and additionally provided molecularly acting substances, the study analyzes the most likely sequence cut sites on the biomolecular sequence. Since biological information such as diseases and genetic mutations is often reflected through sequence cut sites, predicting sequence cut sites as comprehensively and accurately as possible helps to more accurately analyze the disease risk of the first organism, serving as an early detection basis for disease identification. This can better assist in disease avoidance and also contribute to the development of more comprehensive diagnostic methods and treatment strategies. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a structural block diagram of a prediction system provided in an exemplary embodiment of this application;

[0021] Figure 2 This is a flowchart of a sequence cutting position prediction method provided in an exemplary embodiment of this application;

[0022] Figure 3 This is a flowchart of a method for predicting sequence cutting positions provided in another exemplary embodiment of this application;

[0023] Figure 4 This is a flowchart of a method for predicting sequence cutting positions provided in another exemplary embodiment of this application;

[0024] Figure 5 This is a schematic diagram of cell structure data provided in an exemplary embodiment of this application;

[0025] Figure 6 This is a schematic diagram of cell structure data provided in yet another exemplary embodiment of this application;

[0026] Figure 7 This is a schematic diagram of cell structure data provided in another exemplary embodiment of this application;

[0027] Figure 8 This is a flowchart of a method for predicting sequence cutting positions provided in yet another exemplary embodiment of this application;

[0028] Figure 9 This is a schematic diagram of the overall architecture for generating, filtering, and predicting cell structure data provided in an exemplary embodiment of this application;

[0029] Figure 10 This is a schematic diagram of a cutting site distribution table provided in an exemplary embodiment of this application;

[0030] Figure 11 This is a flowchart illustrating the prediction of sequence cutting positions provided in an exemplary embodiment of this application;

[0031] Figure 12 This is a structural block diagram of a sequence cutting position prediction device provided in an exemplary embodiment of this application;

[0032] Figure 13 This is a schematic diagram of the structure of a server provided in an exemplary embodiment of this application. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0034] First, a brief introduction to the terms used in the embodiments of this application will be given.

[0035] Circulating Free DNA (cfDNA): cfDNA refers to DNA fragments that exist free in blood, urine, or other bodily fluids. These fragments typically originate from apoptosis, necrosis, or active release, and their length and sequence characteristics can reflect the state of the cell. In cancer research, cfDNA analysis has become an important tool for early non-invasive diagnosis.

[0036] Deoxyribonuclease I (DNASE1): DNASE1 is an endonuclease that non-specifically cleaves DNA strands to produce short fragments with 5'-phosphate groups. It plays an important role in apoptosis, DNA degradation, and immune regulation.

[0037] Deoxyribonuclease I Like 3 (DNASE1L3): DNASE1L3 is a homolog of DNASE1 and is mainly involved in DNA degradation during apoptosis. Compared with DNASE1, DNASE1L3 has higher cleavage specificity for specific DNA sequences or structures.

[0038] DNA Fragmentation Factor Subunit Beta (DFFB): DFFB is one of the key enzymes in DNA fragmentation during apoptosis. It forms a complex with DNA Fragmentation Factor Alpha (DFFA). Upon activation by cysteine-aspartic protease (caspase), DFFB is released and cleaves DNA to generate typical DNA ladder-like fragments.

[0039] Cleavage pattern: A cleavage pattern refers to the distribution and sequence characteristics of cleavage sites on the DNA strand where a cleavage enzyme makes cuts. Analyzing cleavage patterns can reveal the cleavage specificity of cleavage enzymes and the mechanisms by which they interact with DNA.

[0040] In related technologies, research has found that cell-free deoxyribonucleic acid (cfDNA) is a DNA fragment released from cells into the bloodstream, typically when cells die or are damaged. For example, when cellular DNA is cleaved by enzymes under normal or pathological conditions, the resulting cfDNA enters the bloodstream. Related technologies usually identify the DNA nick at the time of cfDNA release and analyze the base frequency around the nick to reveal the state of the cfDNA fragment, thus analyzing the cause of cell death, such as determining whether a disease exists or its type. However, the above process is still limited to analyzing individual cfDNA fragments in an individual's blood, resulting in poor comprehensiveness and hindering the achievement of effective disease prevention and early diagnosis.

[0041] This application presents a method for predicting sequence cleavage sites. This method, using the biomolecular sequence associated with a first organism and additionally provided molecularly active substances, analyzes the most likely cleavage site on the biomolecular sequence by the molecularly active substances, thus aiding in a more accurate analysis of the disease risk of the first organism. The sequence cleavage site prediction method proposed in this application can be applied to various cell-based analysis scenarios, such as early cancer screening, diagnosis and prediction of genetic diseases, prevention and treatment of immune system disorders, stem cell research, and regenerative medicine, and is not limited thereto.

[0042] It should be noted that all information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant regions. For example, the biomolecular sequences and molecularly active substances involved in this application were obtained with full authorization.

[0043] Secondly, the prediction system involved in the embodiments of this application will be described. The method for predicting the sequence cutting position provided in the embodiments of this application can be implemented by the terminal alone, by the server, or by the terminal and the server through data interaction. The embodiments of this application do not limit this. Optionally, the method for predicting the sequence cutting position by interaction between the terminal and the server will be used as an example for description.

[0044] This is illustrative; please refer to it. Figure 1 The prediction system involves a terminal 110 and a server 120, which are connected via a communication network 130.

[0045] In some embodiments, terminal 110 has a data acquisition function for acquiring the molecularly active substance and the biomolecular sequence corresponding to the first organism. The molecularly active substance is used to cut the biomolecular sequence, and the biomolecular sequence includes at least two molecular sites. Illustratively, the molecularly active substance is a cutting enzyme, and the biomolecular sequence is a DNA sequence associated with the first organism, such as obtaining cfDNA from blood collected from the first organism and then reversing the cfDNA to obtain the DNA sequence.

[0046] Optionally, the terminal 110 sends the molecular interacting substance and biomolecule sequence to the server 120 via the communication network 130, and the server 120 performs the prediction task; or, the terminal 110 performs the prediction task itself, etc.

[0047] In some embodiments, taking the server 120 performing a prediction task based on molecular interacting substances and biomolecular sequences as an example, the server 120 predicts the sequence cutting position corresponding to the biomolecular sequence based on the relative positional relationship between the molecular interacting substances and the biomolecular sequence.

[0048] The sequence cleavage position is used to characterize the cleavage position of the molecularly active substance in the at least two molecular sites.

[0049] Optionally, the sequence cut position can help predict the biological abnormality status of the first organism. For example, if the sequence cut position predicted based on the first organism is verified to be a common cut position for disease A, the output biological abnormality status would be "suspected of having disease A," etc., without limitation.

[0050] Optionally, the server 120 sends the abnormal biological state to the terminal 110 via the communication network 130, so as to render and display the abnormal biological state corresponding to the first organism on the terminal 110.

[0051] It is worth noting that the aforementioned terminals include, but are not limited to, mobile terminals such as mobile phones, tablets, portable laptops, smart voice interaction devices, smart home appliances, and in-vehicle terminals, and can also be desktop computers, etc.; the aforementioned servers can be independent physical servers, server clusters or distributed systems composed of multiple physical servers, or cloud servers.

[0052] Based on the above introduction to terminology and application scenarios, the method for predicting sequence cutting positions provided in this application will be explained, taking the application of this method to a server as an example. Figure 2 As shown, the method includes the following steps 210 to 230.

[0053] Step 210: Obtain the biomolecular sequence corresponding to the first organism.

[0054] In illustrative terms, the first organism is any organism. An organism is an individual with life characteristics composed of one or more cells. An organism maintains its life activities through basic life phenomena such as cellular metabolism, growth, reproduction, heredity, and adaptation to the environment.

[0055] Biomolecular sequences provide information that guides biological activities. For example, a biomolecular sequence might be DNA; or, alternatively, RNA.

[0056] To illustrate, a biomolecular sequence includes at least two molecular sites, each storing a biomolecule (such as a nucleotide). A biomolecular sequence is a sequence composed of a series of biomolecules arranged in a specific order. The arrangement and combination of molecular sites determine the function and activity of cells and the genetic characteristics of organisms.

[0057] Step 220: Obtain the molecular interaction substance.

[0058] Among them, molecularly interacting substances are used to cleave biomolecule sequences.

[0059] Molecularly acting substances are used to cleave biomolecular sequences. Illustratively, molecularly acting substances are biological enzymes present within the cell; or, they may be additionally provided biological enzymes, etc., used to influence biomolecular sequences to alter their current state.

[0060] Optionally, the molecular agents include cleavage enzymes, which are enzymes that can cleave specific chemical bonds in macromolecules (such as DNA, RNA, and proteins). Examples of cleavage enzymes include restriction endonucleases (which recognize specific DNA sequences and cleave double-stranded DNA at those locations) and nucleic acid cleavage enzymes (which specifically cleave DNA or RNA nucleic acid molecules). Endonucleases can cleave the strand at specific locations within the molecule, forming internal cleavage sites; restriction endonucleases belong to the endonuclease class. Exonucleases cleave stepwise from the ends of the molecule and typically act on single-stranded DNA or RNA.

[0061] In some embodiments, when analyzing a first organism, the biomolecular sequence corresponding to the first organism is obtained, and additionally, the molecular interacting substance is obtained; the interaction relationship between the molecular interacting substance and the biomolecular sequence is simulated to obtain at least two cell structure data.

[0062] Indicatively, different cell structure data include the same molecular active substances and bioanalytical sequences, but the relative positional relationships between the molecular active substances and bioanalytical sequences differ in different cell structure data. By integrating different cell structure data, the analysis of the first organism can be achieved.

[0063] Step 230: Based on the relative positional relationship between the molecular interacting substance and the biomolecular sequence, predict the sequence cutting position corresponding to the biomolecular sequence.

[0064] The sequence cleavage position is used to characterize the cleavage position of the molecularly active substance in at least two molecular sites.

[0065] To illustrate, since a biomolecular sequence includes at least two molecular sites, when a molecularly active substance acts on the biomolecular sequence, the corresponding effect is achieved. Taking a cleaving enzyme used to cut a biomolecular sequence as an example, when the cleaving enzyme acts on the biomolecular sequence, there are sites in the biomolecular sequence that are acted upon by the cleaving enzyme; these sites are the sequence cleavage sites.

[0066] Therefore, by analyzing the relative positional relationship between the molecularly acting substance and the biomolecular sequence, it is possible to predict whether the molecularly acting substance can cleave the biomolecular sequence. If it is predicted that the molecularly acting substance can cleave the biomolecular sequence, then it is necessary to predict the sequence cleavage position of the molecularly acting substance on the biomolecular sequence.

[0067] The sequence cut position is the predicted location where a biomolecule sequence will be cut; if a biomolecule sequence is cut based on the sequence cut position, it will break into at least two biomolecule fragments at the sequence cut position.

[0068] In an optional embodiment, structural prediction is performed on the biomolecular sequence and the molecular interacting substance to generate at least two cell structure data corresponding to the free molecular sequence; based on the relative positional relationship between the molecular interacting substance and the biomolecular sequence within the cell structure data, the sequence cutting position corresponding to the biomolecular sequence within the cell structure data is predicted to obtain the sequence cutting positions corresponding to the at least two cell structure data respectively.

[0069] As an illustration, since the relative positional relationship between the molecular interacting substances and the biomolecular sequence differs in different cell structure data, for each cell structure data, the relative positional relationship between the molecular interacting substances and the molecular sequence in the cell structure data can be analyzed to determine the sequence cutting position corresponding to each cell structure data. That is, it is possible to analyze at least two cell structure data separately to determine the sequence cutting position corresponding to each cell structure data.

[0070] Optionally, for each cell structure data, at least one sequence cleavage position corresponding to each cell structure data can be determined by the relative positional relationship between the molecular interacting substance and the biomolecular sequence. When one sequence cleavage position is predicted, the fragmentation is predicted to be two biomolecular fragments; when two sequence cleavage positions are predicted, the fragmentation is predicted to be three biomolecular fragments, and so on, without limitation here.

[0071] It is worth noting that the above are merely illustrative examples, and the embodiments of this application are not limited thereto.

[0072] In summary, by utilizing the biomolecular sequences associated with the first organism and additional molecularly active substances, we analyzed the most likely sequence cut sites on the biomolecular sequences. Since biological information such as diseases and genetic mutations is often reflected through sequence cut sites, predicting these sites as comprehensively and accurately as possible helps to more accurately analyze the disease risk of the first organism, serving as an early detection basis for disease identification. This can better assist in disease avoidance and also contribute to the development of more sophisticated diagnostic methods and treatment strategies.

[0073] In an optional embodiment, the sequence cleavage position corresponding to the biomolecule sequence is determined based on the relative positional relationship between the molecularly interacting substance and the biomolecule sequence. (Illustrative example, such as...) Figure 3 As shown above, Figure 2Step 220 shown can also be implemented as steps 310 to 330.

[0074] Step 310: Determine the first key molecule from the molecular interaction substance.

[0075] In illustrative terms, the first key molecular substance is a key residue in the molecularly active substance that participates in the catalytic cleavage process. It can be a pre-determined position based on the characteristics of the molecularly active substance. For example, if the molecularly active substance is the cleavage enzyme DNASE1L3, the first key molecular substance in the molecularly active substance is the key residue 138, etc. This is only an illustrative example and is not intended to limit the scope.

[0076] In some embodiments, the molecularly acting substance and the biomolecular sequence of the first organism to be analyzed are combined to obtain at least two cell structure data, and each cell structure data is analyzed separately; taking the analysis of the first cell structure data as an example, the first key molecular substance on the molecularly acting substance in the first cell structure data is determined.

[0077] Indicatively, the first cell structure data is any one of at least two cell structure data; taking the analysis of the first cell structure data as an example, the first key component is determined from the molecular interacting substances included in the first cell structure data. The first key component is the component of the molecular interacting substances that participates in the catalytic splitting process.

[0078] Step 320: Based on the relative positional relationship between the molecular interacting substance and the biomolecular sequence, determine the first molecular site in the biomolecular sequence that is closest to the first key molecular substance.

[0079] Schematic, by means of the relative positional relationship between the molecular interacting substance and the biomolecular sequence, the molecular site closest to the first molecular substance is determined as the first molecular site from a biomolecular sequence including at least two molecular sites.

[0080] Molecular sites are used to store biomolecules.

[0081] Indicatively, a biomolecular sequence comprises at least two biomolecules, with each molecular site storing the biomolecules that make up the biomolecular sequence.

[0082] Optionally, taking DNA as an example of a biomolecular sequence, the biomolecules in the biomolecular sequence, such as the nucleotides that make up DNA, and the molecular sites, which are the nucleotide positions on the DNA that store each nucleotide, are not limited here.

[0083] In an optional embodiment, the biomolecule includes at least one biological atom; the first biological atom closest to the first molecular substance is determined based on the relative positional relationship between the molecularly acting substance and the biomolecule sequence.

[0084] Indicatively, a biomolecule includes at least one biological atom; biological atoms include: carbon (C) atoms present in deoxyribose and bases, which form the molecular backbone; hydrogen (H) atoms bonded to carbon atoms and constituting the organic part of the molecule; oxygen (O) atoms present in sugar rings and phosphate groups, which help maintain the structure of DNA molecules; nitrogen (N) atoms present in bases (adenine A, guanine G, cytosine C, thymine T), which participate in hydrogen bonding of base pairs; and phosphorus (P) atoms present in phosphate groups of DNA to form the backbone, etc.

[0085] For example, taking DNA as a biomolecule sequence, the biomolecule is the nucleotide that makes up DNA. A nucleotide includes deoxyribose, a phosphate group and a base (adenine A or guanine G or cytosine C or thymine T), etc., without being limited here.

[0086] Schematic, based on the relative positional relationship between the molecularly acting substance and the biomolecular sequence within the first cell structure data, the first biological atom closest to the first key molecular substance is determined from the biomolecules included in the biomolecular sequence.

[0087] Schematic, the biomolecular sequence includes at least two biomolecules, each biomolecule including multiple bioatoms, and the bioatom that is spatially closest to the first point molecular substance is identified from a large number of bioatoms as the first bioatom.

[0088] In an optional embodiment, a first biomolecule comprising a first biological atom is determined from the biomolecule sequence, and a first molecular site at which the first biomolecule is located is determined.

[0089] Indicatively, the biomolecule to which the nearest first biological atom belongs is called the first biomolecule, and the molecular site where the first biomolecule is located is called the first molecular site.

[0090] In some embodiments, taking the analysis of the relative positional relationship between the molecular active substance and the biomolecular sequence in each cell structure data as an example, for the first cell structure data, the first molecular site closest to the first molecular substance in the biomolecular sequence is determined from the biomolecular sequence by the relative positional relationship between the molecular active substance and the biomolecular sequence in the first cell structure data.

[0091] Step 330: Based on the first molecular site, predict the sequence cut position corresponding to the biomolecule sequence.

[0092] To illustrate, after determining the first molecular site from at least two molecular sites, it is necessary to determine the sequence cleavage position based on the first molecular site. The sequence cleavage position is the cleavage position formed by cutting two adjacent molecular sites.

[0093] In an optional embodiment, at least one adjacent molecular site adjacent to the first molecular site is determined from the biomolecular sequence.

[0094] Indicatively, a biomolecular sequence is a sequence presented in chain form. Molecular sites adjacent to the first molecular site are determined from the biomolecular sequence as adjacent molecular sites. When the first molecular site is a molecular site in the middle of the biomolecular sequence, two adjacent molecular sites can be determined based on the first molecular site. When the first molecular site is a molecular site at the beginning or end of the biomolecular sequence, one adjacent molecular site can be determined based on the first molecular site.

[0095] In an optional embodiment, a second molecular site is determined from at least one adjacent molecular site based on the distance relationship between the first biological atom and at least one adjacent molecular site.

[0096] Indicatively, after determining the first biological atom based on the first key molecular substance, the distance relationship between the first biological atom and at least one adjacent molecular site is analyzed, thereby determining the second molecular site from at least one adjacent molecular site.

[0097] Optionally, the adjacent molecular site closest to the first biological atom is used as the second molecular site, so that the first biological atom is not only in the first biological molecule corresponding to the first molecular site, but also closer to the second molecular site.

[0098] In an optional embodiment, a sequence cut position corresponding to the biomolecule sequence is determined between the first molecular site and the second molecular site.

[0099] Indicatively, the position between the first molecular site and the adjacent second molecular site is taken as the sequence cutting position corresponding to the biomolecule sequence, that is, the predicted molecular action substance will be cut at the position between the first molecular site and the second molecular site.

[0100] For example: biomolecular sequences are represented as The first molecular site is indicated in bold. The second molecular site is indicated by the underline. C ", then the predicted sequence cut position is at Between, therefore, can be represented as etc., without limitation here.

[0101] In some embodiments, taking the formation of multiple cell structure data by molecular active substances and biomolecular sequences as an example, and analyzing the relative positional relationship between molecular active substances and biomolecular sequences in each cell structure data, for the first cell structure data, the first biological atom closest to the first molecular active substance is determined from the biomolecular sequence by the relative positional relationship between molecular active substances and biomolecular sequences in the first cell structure data, thereby using the molecular site of the first biomolecule containing the first biological atom as the first molecular site to determine the sequence cutting position; thereby, the sequence cutting positions corresponding to at least two cell structure data can be determined respectively.

[0102] It is worth noting that the above are merely illustrative examples, and the embodiments of this application are not limited thereto.

[0103] In summary, by utilizing the biomolecular sequences associated with the first organism and additional molecularly active substances, we analyzed the most likely sequence cut sites on the biomolecular sequences. Since biological information such as diseases and genetic mutations is often reflected through sequence cut sites, predicting these sites as comprehensively and accurately as possible helps to more accurately analyze the disease risk of the first organism, serving as an early detection basis for disease identification. This can better assist in disease avoidance and also contribute to the development of more sophisticated diagnostic methods and treatment strategies.

[0104] This application describes the prediction of sequence cleavage positions based on relative positional relationships. By analyzing the relative positional relationship between the first key molecular substance, which mainly participates in the catalytic cleavage process, and the biomolecular sequence, the first molecular site can be specifically determined from the biomolecular sequence, thereby more accurately predicting the potential cleavage positions within the biomolecular sequence. Specifically, considering that each biomolecule in the biomolecular sequence is composed of biological atoms, a finer-grained relationship between biological atoms and molecular reactants is used when determining the first molecular site. This allows the molecular site containing the first biological atom to be used as the first molecular site, further improving the accuracy of obtaining the first molecular site. Furthermore, while considering the first biological atom, the distance to adjacent analytical sites can be analyzed to determine a closer second molecular site based on the first molecular site. This makes the sequence cleavage position determined between the first and second molecular sites more accurate, thereby improving the prediction accuracy of the cleavage position.

[0105] In an optional embodiment, a biomolecular sequence corresponding to the free molecular sequence is obtained by collecting the free molecular sequence of a first organism. (Illustrative example, such as...) Figure 4 As shown above, Figure 2The illustrated embodiment can also be implemented as steps 410 to 440 as follows; wherein step 210 can also be implemented as steps 410 to 420 as follows.

[0106] Step 410: Collect the free molecular sequence corresponding to the first organism.

[0107] To illustrate, the first organism is the one that requires cellular state analysis. Information is collected from the first organism to obtain free molecular sequences.

[0108] Optionally, a blood sample of the first organism is obtained by drawing blood from the first organism, and a free molecular sequence existing in the blood is isolated from the blood sample. The free molecular sequence is called circulating free DNA (cfDNA).

[0109] In a schematic manner, blood samples are processed by centrifugation. For example, a first low-speed centrifugation precipitates blood cells to the bottom of the tube, separating the plasma; a second high-speed centrifugation removes any possible cellular residues or larger particles, ensuring pure plasma. The separated plasma or serum is then processed, and the free molecular sequence is extracted using a cfDNA extraction kit. Common extraction methods include column chromatography (using affinity technology, specialized columns and reagents to extract cfDNA from plasma) and magnetic bead chromatography (using magnetic particles to bind to DNA, separating the DNA through magnetic force).

[0110] Among them, free molecular sequences are fragments of biomolecular sequences that are free in the first organism.

[0111] Indicatively, free molecular sequences are DNA fragments present in bodily fluids such as blood, urine, and saliva. They primarily originate from DNA released during apoptosis or by living cells, and these free molecular sequences exist freely in bodily fluids. Apoptosis is divided into spontaneous apoptosis and aberrant apoptosis. Spontaneous apoptosis is a natural physiological process of cells, while aberrant apoptosis caused by disease is due to cell damage, stress, or pathological factors. In other words, spontaneous apoptosis (a natural physiological process) results in the presence of cfDNA in bodily fluids (such as human blood), and aberrant apoptosis caused by disease also results in the presence of cfDNA in bodily fluids.

[0112] Step 420: Determine the biomolecular sequence corresponding to the first organism based on the free molecular sequence.

[0113] In some embodiments, biological sequencing data corresponding to a first organism is determined based on the free molecular sequence as a biomolecular sequence.

[0114] As an illustration, since the free molecular sequence is a partially free DNA fragment of the first organism, the biological sequencing data of the first organism can be determined in reverse based on the free molecular sequence, that is, the DNA associated with the first organism can be determined.

[0115] Optionally, after extracting the free molecular sequence, high-throughput sequencing technology (such as Illumina sequencing) is used to sequence the free molecular sequence. This process converts the free molecular sequence into a large amount of DNA sequence data, i.e., biological sequencing data. Biological sequencing data contains partial information of various regions of the genome, representing short fragments of information from various regions in the first organism. By splicing and analyzing these short fragments, partial information of the genome of the first organism can be inferred, i.e., the biological sequencing data corresponding to the first organism can be obtained.

[0116] Biological sequencing data obtained based on free molecular sequences are considered as part of the biological molecular sequence. Therefore, the biological molecular sequence includes core histones, linker histones, and biological sequencing data.

[0117] As an illustration, since biological sequencing data is derived from sequences obtained by reverse deduction of free molecular sequences, biological sequencing data can be regarded as sequence information that includes free molecular sequences.

[0118] In some embodiments, biological sequencing data is used as the biomolecular sequence to be analyzed, thereby analyzing the effect of molecular agents on the biomolecular sequence, that is, analyzing the effect of molecular agents on biological sequencing data.

[0119] In an optional embodiment, publicly available cell data and molecularly active substances are obtained.

[0120] The publicly available cell data includes core histones and connective histones in biomolecular sequences.

[0121] To illustrate, publicly available cell data refers to cell data obtained from publicly available databases. Taking nucleosome data collected from the Research Collaboratory for Structural Bioinformatics (RCSB) website as an example, RCSB is one of the main management platforms for the Protein Data Bank (PDB). It is responsible for collecting, maintaining, and providing three-dimensional structural data of biological macromolecules such as proteins and nucleic acids, with user permission, for free use by researchers worldwide.

[0122] Nucleosome data describes the nucleosome, the basic unit of chromatin, which is composed of DNA wrapped around histone octamers. Structural analysis of nucleosomes is crucial for understanding gene regulation.

[0123] Optionally, any nucleosome data can be used as publicly available cell data. The publicly available cell data includes the core histone and linker histone in the above-mentioned biomolecular sequences. The core histone is a histone octamer formed by wrapping the DNA in the nucleosome. The linker histone is different from the histone octamer wrapped in DNA. The H1 histone is responsible for stabilizing the linker DNA between nucleosomes and helps chromatin compress into a higher-order structure.

[0124] Core histones and connective histones were extracted from publicly available cell data to serve as part of the biomolecular sequences used to obtain cell structure data.

[0125] In an optional embodiment, the core histone, the linker histone, and the biological sequencing data are wound together according to the sequence generation rules to obtain the biomolecular sequence corresponding to the first organism.

[0126] In illustrative terms, sequence generation rules are preset rules used to entangle and combine core histones, linker histones, and biological sequencing data. Optionally, in real cell analysis, biological sequencing data (DNA) does not exist independently but is entangled with important proteins (such as core histones and linker histones). While analyzing the cutting relationship between molecular interacting substances and biological sequencing data is important, considering the influence of proteins entangled with biological sequencing data on the cutting process leads to higher accuracy in cutting prediction and better reflects the research and analysis process in real-world scenarios.

[0127] The illustrative, pre-defined sequence generation rules are as follows: biological sequencing data wraps around core histones to form nucleosomes, and the linking regions of the biological sequencing data between the linking histones stabilize the higher-order chromatin structure.

[0128] Among them, the core histones and connective histones in the publicly available cell data are obtained by simulating the process of winding biological sequencing data to obtain a more realistic nucleosome of the first organism, based on the biological sequencing data determined by the genome; using the nucleosome as the smallest segmentation object for the cutting position, the cutting position can be accurately predicted in a way that is closer to the in vivo environment.

[0129] It is worth noting that the above are merely illustrative examples, and the embodiments of this application are not limited thereto.

[0130] Step 430: Obtain the molecular interaction substance.

[0131] Among them, molecularly interacting substances are used to cleave biomolecule sequences.

[0132] This is illustrative of how molecular agents act on biomolecular sequences, primarily on biological sequencing data within those sequences; however, considering the entanglement of biological sequencing data with important proteins within organisms, the cleavage is considered in terms of the biomolecular sequence as a whole.

[0133] Step 440: Based on the relative positional relationship between the molecular interacting substance and the biomolecular sequence, predict the sequence cutting position corresponding to the biomolecular sequence.

[0134] In an optional embodiment, structural prediction is performed on the biomolecular sequence and the molecularly active substance to generate at least two cell structure data corresponding to the free molecular sequence.

[0135] To illustrate, although the sequence information of biomolecules and the molecules themselves are known, the interactions between biomolecules and their molecules, as well as the interactions between core histones, linker histones, and sequencing data within biomolecules, may still exist in various forms.

[0136] In some embodiments, structural prediction is performed on biomolecular sequences and molecularly interacting substances to generate at least two predicted structural data corresponding to the free molecular sequence.

[0137] Among them, the predicted structural data is structural data obtained by combining biomolecular sequences and molecular interacting substances.

[0138] Indicatively, the molecular interacting substance is an additional biological enzyme provided to simulate the process of cutting biomolecular sequences; the purpose of structural prediction of biomolecular sequences and molecular interacting substances is to generate simulated conformations in multiple combinations, that is, to generate at least two predicted structural data corresponding to the free molecular sequence, each predicted structural data representing a combination conformation.

[0139] Optionally, a pre-trained structure prediction model is obtained, and the structure prediction model is used to perform structure prediction on biomolecular sequences and molecular interacting substances to generate at least two predicted structure data corresponding to the free molecular sequence.

[0140] In illustrative terms, structural prediction models are tools and methods that use computational methods to predict the three-dimensional structure of biological macromolecules such as molecules and proteins. The structural data predicted based on structural prediction models represent the three-dimensional structure of biological macromolecules.

[0141] In fields such as biology, chemistry, and pharmacology, understanding the three-dimensional structure of molecules is crucial for comprehending their functions. However, obtaining these molecular structures experimentally is often time-consuming and costly. Therefore, structural prediction models offer an efficient and low-cost alternative, helping researchers infer the spatial configuration of molecules.

[0142] To illustrate, structure prediction models such as AlphaFold Protein Structure Prediction (AlphaFold), Proteinix, Chai-1, and Rosetta are models that predict structural data by simulating the protein folding process. AlphaFold, for example, is a pre-trained neural network model using deep learning technology, capable of accurately predicting the three-dimensional structure of proteins in a very short time. Furthermore, large-scale predictions can be generated by inputting biomolecular sequences and interacting molecules into the structure prediction model, producing multiple predicted structural datasets.

[0143] In some embodiments, predictive structural data that meet the structural screening criteria are selected from at least two predicted structural data as cell structural data.

[0144] Among them, the structure screening conditions are based on the spatial relationship between biomolecular sequences and molecular interacting substances to screen predicted structure data.

[0145] As an illustration, considering that some predicted structural data from multiple predicted structural data sets may not fold well into the correct nucleosome complex structure, a structural filtering algorithm is introduced after generating multiple predicted structural data sets. This algorithm can be viewed as a conditional rule used to filter the predicted structural data. The purpose of structural filtering is to filter out problematic predicted structural data from the multiple predicted structural data sets according to the structural filtering algorithm, thereby retaining the structural data that fold well into the correct nucleosome complex structure, which will then be used as the cellular structural data for segmentation site analysis.

[0146] Among them, structural data for folding to the correct nucleosome complex means that enabling histones and DNA sequences to fold correctly through structural prediction models requires not only matching in spatial conformation, but also conforming to the nucleosome behavior in real organisms in terms of the strength, stability and function of their interactions.

[0147] Optionally, the implementation of the structural screening conditions is illustrated below.

[0148] In some embodiments, predicted structural data from at least two predicted structural data sets that do not bind to core histones are selected as cellular structural data.

[0149] Indicatively, biomolecular sequences include core histones, linker histones, and biological sequencing data determined by reverse derivation based on free molecular sequences. The purpose of structure prediction is to study how molecular agents cleave biological sequencing data. Although it simulates the entanglement relationship between core histones and biological sequencing data, the cleaving enzymes should not be bound to the core histones, but rather there should be a cleaving relationship between them and the biological sequencing data.

[0150] Therefore, for at least two generated predicted structural data sets, it is necessary to discard the predicted structural data sets in which the molecular active substances are bound to the core histones, and retain the predicted structural data sets in which the molecular active substances are not bound to the core histones as cellular structural data.

[0151] Optionally, when it is determined that the molecularly active substance is not bound to the core histone, the determination is based on the distance between the molecularly active substance and the biological sequencing data.

[0152] As an illustration, for the generated predicted structural data, there may be some predicted structural data that characterizes the reaction pockets of the molecular active substances but is not combined with the biological sequencing data. In other words, there may be some predicted structural data that characterizes the molecular active substances but is not bound to the biological sequencing data. Therefore, it cannot be used as cell structural data for predicting cleavage sites.

[0153] Taking the cleavage enzyme as an example, the reaction pocket of the cleavage enzyme is the active site or catalytic site of the cleavage enzyme. It is usually a specific region on the cleavage enzyme that is specifically used to bind to the substrate molecule being cleaved (i.e., biological sequencing data) and carry out a chemical reaction. Therefore, for the cleavage enzyme, the reaction pocket is the region on the cleavage enzyme that is used to recognize and cleave specific molecules.

[0154] In some embodiments, for a first predicted structural data in at least two predicted structural data, a first key residue corresponding to a molecularly active substance in the first predicted structural data is determined, and a first atom in the biological sequencing data is determined; a first distance between the first key residue and the first atom is obtained; if the first distance is less than a first preset threshold, the first predicted structural data is used as cell structural data.

[0155] Wherein, the first predicted structural data is any predicted structural data; the molecular interacting substance is not bound to the core histone, including the first distance being less than the first preset threshold.

[0156] Optionally, for any predicted structural data, a first distance is calculated between the first key residue in the molecularly interacting substance and any atom in the biological sequencing data (e.g., the first atom is any atom); if the first distance is less than a first preset threshold, it indicates that the molecularly interacting substance is not bound to the core histone. That is, the predicted structural data is retained as cellular structural data.

[0157] For example: the first critical residue in the reaction pocket of the DNASE1L3 cleavage enzyme is 78, with a pre-set first preset threshold. (E) is a unit of measurement. Taking 0.1nm as an example, an arbitrary atom is an atom arbitrarily selected from the DNA molecule (DNA molecule in biological sequencing data) (such as a randomly selected atom or the nearest atom); calculate the first distance between the first key residue 78 and the selected arbitrary atom, such as the first distance, which can also be called the interstrand distance; if the first distance corresponding to the predicted structure data 1 is less than This indicates that the predicted structural data 1 represents the correct capture of biological sequencing data by the cleavage enzyme. At this point, it is considered that there is a binding between the molecular action substance and the biological sequencing data, and the predicted structural data 1 is used as the cell structural data.

[0158] like Figure 5 The diagram illustrates predicted structural data where the molecularly active ingredient is bound to a core histone but not to biological sequencing data. The molecularly active ingredient shown is the DNASE1L3 cleavage enzyme 510, and it also includes biological sequencing data (DNA) 520, core histone 530, and linker histone 540; Figure 5 As shown, the predicted structural data where the cleavage enzyme 510 is bound to the core histone 530 but not to the biological sequencing data 520 (indicating that the cleavage enzyme 510 is closer to the core histone and farther from the biological sequencing data 520) needs to be discarded. Figure 5 The predicted structural data shown does not meet the structural screening criteria and cannot be used as cell structural data for subsequent analysis.

[0159] In an optional embodiment, predictive structural data that connects histones within biological sequencing data is selected from at least two predicted structural data sets as cellular structural data.

[0160] To illustrate, the correct location of the linker histone should be at the linker DNA in the biological sequencing data. The linker DNA is a portion of the DNA in the biological sequencing data. The linker DNA is usually relatively long and does not have a structure directly surrounding the core histone.

[0161] Therefore, if the linker histone is located in the linker DNA, it means that the linker histone is in the correct position, and the predicted structural data of the linker histone at the linker DNA is retained as cellular structural data; if the linker histone is not located in the linker DNA, it means that the linker histone is not in the correct position, and the structural data of the H1 histone not being located in the linker DNA is discarded.

[0162] As an illustration, for the generated predicted structural data, some predicted structural data may incorrectly characterize the location of the connecting histone (e.g., not located at the connecting DNA).

[0163] In some embodiments, for a second predicted structural data in at least two predicted structural data, a first central residue in the linker histone in the second predicted structural data is determined, and a second central residue in the linker histone corresponding to the first central residue is determined from publicly available cell data; the residue distance between the first central residue and the second central residue is calculated; if the residue distance is less than a second preset threshold, the second predicted structural data is used as cell structural data.

[0164] The second predicted structural data is any predicted structural data; the linker histone is within the biological sequencing data including residues with a distance less than the second preset threshold.

[0165] Optionally, the predicted structure data is generated based on nucleosome data. The connector histones included in the nucleosome data are the same as those included in the predicted structure data, but the spatial locations of the connector histones in the nucleosome data and the connector histones in the structure data may differ.

[0166] Schematic, a first central residue is determined from the linker histones included in the nucleosome data, and a second central residue identical to the first central residue is determined from the linker histones included in the predicted structure data, but the spatial positions of the first and second central residues differ; the residue distance between the first and second central residues is calculated; if the residue distance is less than a second preset threshold, it indicates that the linker histone is correctly positioned. For example, the second preset threshold is... If the residue distance corresponding to predicted structural data 2 is less than If the position of the connecting histone in predicted structural data 2 is correct, then predicted structural data 2 will be used as cell structural data.

[0167] like Figure 6 The diagram illustrates predicted structural data for linker histones located outside of biological sequencing data. It shows linker histone (H1 histone) 610, biological sequencing data (DNA) 620, which includes nucleosome DNA intertwined with the core histone 630, and linker DNA 621. Figure 6As shown, linker histone 610 is not located within linker DNA 621, meaning it is considered outside of biological sequencing data 620 and therefore needs to be discarded. Figure 6 The predicted structural data shown does not meet the structural screening criteria and cannot be used as cell structural data for subsequent analysis.

[0168] In an optional embodiment, predictive structural data in which free molecular sequences surround core histones and connective histones are selected from at least two predicted structural data sets as cellular structural data.

[0169] This is illustrative of how biological sequencing data is back-engineered from free molecular sequences to obtain predicted structural data, which includes the free molecular sequences. Although the free molecular sequences are free, they may still carry some information about histone binding, because DNA and histones are not completely separated during cell death, and the free molecular sequences still retain traces of interactions with core histones and linker histones.

[0170] Therefore, studying the free molecular sequences that correctly surround core and linker histones can allow for the analysis and even prediction of richer cellular biological information (such as modes of cell death). Thus, the goal is to retain predicted structural data where the free molecular sequences correctly surround core and linker histones, and discard those where they do not, in order to preserve predictive structural data that more closely reflects the formation of real organisms as cellular structural data.

[0171] In some embodiments, for a third predicted structural data in at least two predicted structural data sets, at least two first sequences in the third predicted structural data set are determined, the at least two first sequences including biomolecular sequences and molecularly active substances in the third predicted structural data set; and at least two second sequences in the publicly available cell data set are determined, the at least two second sequences including biomolecular sequences and molecularly active substances in the publicly available cell data set; the first and second sequences representing the same information are aligned; and a root mean square deviation (RMSD) value between the third predicted structural data set and the publicly available cell data set is obtained based on the sequence alignment; if the RMS deviation value is less than a third preset threshold, the third predicted structural data set is used as cell structural data set.

[0172] Among them, the third predicted structural data is any predicted structural data; the free molecular sequence surrounding the core histone and the connecting histone includes a root mean square deviation value less than the third preset threshold.

[0173] Optionally, the predicted structure data is generated based on nucleosome data. The connective histones included in the nucleosome data are the same as those included in the predicted structure data, but the spatial locations of the connective histones in the nucleosome data (such as representing the second sequence in the publicly available cell data) and the connective histones in the predicted structure data (such as representing the first sequence in the publicly available cell data) may differ. Similarly, the core histones included in the nucleosome data are the same as those included in the predicted structure data, but the spatial locations of the core histones in the nucleosome data and the core histones in the predicted structure data may also differ, etc.

[0174] Indicatively, a structural alignment method is used to align the predicted structural data with the nucleosome data, that is, to align sequences representing the same information but located in different data sets within the predicted structural data and nucleosome data; and the root mean square deviation (RMSD) between the predicted structural data and the nucleosome data is calculated. If the RMSD is less than a third preset threshold, it indicates that the predicted structural data is similar to the given nucleosome data, and also represents that the predicted structural data is similar to the core of the correct nucleosome and the DNA winding pattern.

[0175] For example: the predicted structural data is represented as x, and the given nucleosome data is a PDB structure y (e.g., 8tb9, representing nucleosome data with sequence number 8tb9 in the PDB). The sequences in the predicted structural data and the corresponding sequences in the nucleosome data are aligned. The alignment tool in PyMOL can be used for this calculation, and the root mean square deviation (RMSD) between the two is calculated and represented as pymol_rmsd. If the third preset threshold is 3, then if the pymol_rmsd corresponding to predicted structural data 3 is less than 3, it indicates that the predicted structural data is similar to the given nucleosome data, and therefore predicted structural data 3 is retained as the cell structure data for subsequent analysis.

[0176] like Figure 7 The diagram illustrates predicted structural data where the free molecular sequence cannot surround the core histone or interact with the linker histone. The figure shows free molecular sequence 710, linker histone (H1 histone) 720, and core histone 730. Free molecular sequence 710 cannot surround core histone 730, nor can it interact with linker histone (H1 histone) 720. Therefore, it needs to be discarded. Figure 7 The predicted structural data shown does not meet the structural screening criteria and cannot be used as cell structural data for subsequent analysis.

[0177] In an optional embodiment, based on the relative positional relationship between molecular interacting substances and biomolecular sequences within the cell structure data, the sequence cutting positions corresponding to the biomolecular sequences within the cell structure data are predicted, thereby obtaining the sequence cutting positions corresponding to at least two cell structure data respectively.

[0178] To illustrate, for each cell structure data, the relative positional relationship between the molecular interacting substances and biomolecular sequences within the cell structure data is analyzed. Each cell structure data includes a biomolecular sequence, and the sequence cutting position corresponding to each biomolecular sequence is used as the sequence cutting position corresponding to the cell structure data, thereby obtaining the sequence cutting positions corresponding to at least two cell structure data respectively.

[0179] It is worth noting that the above are merely illustrative examples, and the embodiments of this application are not limited thereto.

[0180] In summary, by utilizing the biomolecular sequences associated with the first organism and additional molecularly active substances, we analyzed the most likely sequence cut sites on the biomolecular sequences. Since biological information such as diseases and genetic mutations is often reflected through sequence cut sites, predicting these sites as comprehensively and accurately as possible helps to more accurately analyze the disease risk of the first organism, serving as an early detection basis for disease identification. This can better assist in disease avoidance and also contribute to the development of more sophisticated diagnostic methods and treatment strategies.

[0181] This application describes a method for comprehensively predicting structural data and selecting cellular structural data based on publicly available cellular data, molecular agents, and the free molecular sequence of a first organism. When analyzing the first organism, the collected free molecular sequence is used as a basis to obtain the corresponding biological sequencing data through the genome. Considering the need to predict cleavage patterns, molecular agents are additionally introduced to predict the relationship between them and the biological sequencing data. The core histones and connective histones provided by the publicly available cellular data make the generated predicted structural data more consistent with the actual state, improving prediction accuracy and realism.

[0182] In addition to generating predicted structural data, at least one of the above three situations is introduced as a judgment criterion to achieve the purpose of screening predicted structural data, making the acquisition of cell structural data more realistic. This allows for the analysis of the cutting of biological sequencing data by molecular agents under cell structural data, making the determination of sequence cutting positions more accurate, and thus helping to improve the accuracy of disease prediction.

[0183] In an optional embodiment, the biological abnormality state of a first organism is predicted based on the amount of structural data at the sequence cut locations. (Illustrative example, such as...) Figure 8 As shown, taking the analysis of the interaction relationships between molecular agents and biomolecular sequences within cell structure data as an example, the corresponding sequence cleavage positions are determined for at least two cell structure datasets. The above... Figure 2 The illustrated embodiment can also be implemented as follows: steps 810 to 850.

[0184] Step 810: Obtain at least two cell structure data.

[0185] The cell structure data includes molecular action substances and the biomolecular sequences corresponding to the first organism. The molecular action substances are used to cleave the biomolecular sequences, which include at least two molecular sites.

[0186] Optionally, the molecularly active substances include cleaving enzymes, which are a class of enzymes that can cleave specific chemical bonds in macromolecules (such as DNA, RNA, proteins, etc.). Examples of cleaving enzymes include restriction endonucleases and nucleases.

[0187] In some embodiments, when analyzing a first organism, the biomolecular sequence corresponding to the first organism is obtained, and additionally, the molecular interacting substance is obtained; the interaction relationship between the molecular interacting substance and the biomolecular sequence is simulated to obtain at least two cell structure data.

[0188] Optionally, the free molecular sequence corresponding to the first organism is obtained, as well as the core histone and connective histone in the publicly available cell data, and the molecular action substance is obtained; the free molecular sequence, core histone, connective histone and molecular action substance are input into a pre-trained structure prediction model to obtain at least two predicted structure data.

[0189] like Figure 9 As shown, taking publicly available cell data 910 as an example, nucleosome data 8tb9 (nucleosome data with serial number 8tb9 collected from the Structural Bioinformatics Research Collaboration website); through the core histones and linker histones in publicly available cell data 910, additional molecular action substances and the corresponding free molecular sequences of the first organism, 2500 predicted structural data 920 are obtained using a structural prediction model (2500 here is only an illustrative example). Then, a structural filtering algorithm is used to select predicted structural data that meet at least one of the above three conditions from the 2500 predicted structural data 920 as cell structural data. For example, 34 predicted structural data are selected as cell structural data (shown in Table 930, which will be described in detail later).

[0190] As shown in cell structure data 931, represented as "8tb9-225-1 [Linker]", this represents a cell structure data 931 generated based on nucleosome data 8tb9, where the molecular agent 940 (cutting enzyme) in cell structure data 931 cuts at the linker DNA. The cutting pattern is represented as follows: The prediction is in bold. Bases and bolded The cleavage between bases indicates the predicted sequence cleavage location. "9 / 34" represents cell structure data 931, which is the 9th cell structure data point out of the 34 selected data points.

[0191] For example, cell structure data 932, represented as "8tb9-97-0[Core]", represents another cell structure data 932 generated based on nucleosome data 8tb9, where the molecular agent 940 (cutting enzyme) in cell structure data 932 cuts at the core DNA. The cutting pattern is represented as follows: The prediction is represented by two adjacent bolded characters. The cleavage between bases indicates the predicted sequence cleavage location. "5 / 34" represents cell structure data 932, which is the 5th cell structure data point out of the 34 selected data points.

[0192] In such Figure 9 The content shown introduces cleavage enzymes such as DNASE1L3; an overview describes a structure-based pipeline prediction of the cleavage site of the nucleosome complex DNASE1L3.

[0193] The predicted structure data generated by the structure prediction model is filtered by a structure filtering algorithm to obtain cell structure data. The structure filtering algorithm needs to ensure the following rules and conditions: (1) DNASE1L3 should not bind to the histone core; (2) H1 should be correctly placed; (3) cfDNA can correctly wrap the core and H1.

[0194] Step 820: Based on the relative positional relationship between molecular action substances and biomolecular sequences in the cell structure data, determine the sequence cutting positions corresponding to at least two cell structure data points respectively.

[0195] The sequence cleavage position is used to characterize the cleavage position of the molecularly active substance in at least two molecular sites.

[0196] As an illustration, since the relative positional relationship between molecular agents and biomolecular sequences differs in different cell structure data, for each cell structure data, the relative positional relationship between molecular agents and molecular sequences in the cell structure data can be analyzed to determine the sequence cutting position corresponding to each cell structure data; for each cell structure data, at least one sequence cutting position corresponding to each cell structure data can be determined by the relative positional relationship between molecular agents and biomolecular sequences.

[0197] Step 830: Determine at least one sequence cut position based on the sequence cut positions corresponding to at least two cell structure data.

[0198] As an illustration, each cell structure data corresponds to at least one sequence cut position. The sequence cut positions corresponding to different cell structure data may be the same or different. By summing up multiple sequence cut positions, at least one sequence cut position can be obtained.

[0199] In some embodiments, the predicted sequence cut locations are represented by a cut site distribution table, which includes various information related to the cut prediction.

[0200] Indicative, such as Figure 10 The image shown is a partial schematic diagram of a table illustrating the distribution of cut sites. Figure 9 [Prediction results of some cell structure data (e.g., 8) from 34 cell structure data obtained through screening], showing the results after performing cut position prediction on each cell structure data after generating and screening cell structure data based on nucleosome data 8tb9. The folder indicates the source of the cell structure data. During the prediction process, PyMOL root mean square error, H1 distance, and DNA strand distance are also calculated. In addition, confidence level and DNA strand (Z represents the first strand of double-stranded DNA, X represents the second strand) are also included. Cut mode 1010 indicates the biomolecule stored at the molecular site at the cut position, where "|" represents a cut. Sequence cut position 1020 is presented as a sequence number, representing the base position on the genome.

[0201] Step 840: For a first sequence cut position in at least one sequence cut position, determine the number of structural data of cell structural data having the first sequence cut position, and determine the number of structural data corresponding to each of the at least one sequence cut positions.

[0202] Schematic illustration: The first sequence cut position is any one of at least one sequence cut positions. Taking the first sequence cut position as an example, the number of structural data points of cell structural data with the first sequence cut position is determined. For example... Figure 10The table content shown shows that, taking the first sequence cut position as "184" as an example, the number of structural data of cell structure data with the first sequence cut position is "3"; similarly, taking the first sequence cut position as "183" as an example, the number of structural data of cell structure data with the first sequence cut position is "3"; similarly, taking the first sequence cut position as "77" as an example, the number of structural data of cell structure data with the first sequence cut position is "1"; similarly, taking the first sequence cut position as "194" as an example, the number of structural data of cell structure data with the first sequence cut position is "1".

[0203] Step 850: Based on the number of structural data corresponding to at least one sequence cutting position, predict the biological abnormality state of the first organism.

[0204] To illustrate, each sequence cut position corresponds to a number of structural data points, and the number of structural data points represents the number of cell structural data points with the same sequence cut position.

[0205] In an optional embodiment, at least one sequence cut position with the largest amount of structural data is used as the site detection information corresponding to the first organism.

[0206] Indicative, based on such Figure 10 The table shown contains a portion of the data. The number of structural data at sequence cut position "184" is "3", the number of structural data at sequence cut position "183" is "3", the number of structural data at sequence cut position "77" is "1", and the number of structural data at sequence cut position "194" is "1". If at least one sequence cut position with the most structural data is selected, then at least one of sequence cut position "184" and sequence cut position "183" is selected as the site detection information corresponding to the first organism.

[0207] Among them, site detection information is used to detect the abnormal biological state of the first organism.

[0208] In some embodiments, a disease cutting location dataset is obtained, which stores multiple cutting location data. The cutting location data is used to characterize the cutting locations in the biomolecular sequence when the disease is present. The sequence cutting locations corresponding to at least two cell structure data are matched with the disease cutting location dataset to obtain the biological abnormal state of the first organism.

[0209] Optionally, a disease cutting location dataset is pre-acquired, which stores multiple disease-related cutting location data, representing locations in the biomolecular sequence that are easily cut when the disease is present.

[0210] In cases where the disease cutting location dataset includes sequence cutting locations, the first biological abnormality state is output. This could be an indication that the first organism is diseased, or an indication that the biological cells within the first organism may be abnormal (e.g., referring to the first organism possibly being diseased), where the biological cells are the cells within the first organism.

[0211] Alternatively, if the disease segmentation location dataset does not include sequence segmentation locations, output a second biological abnormality status. For example, the second biological abnormality status could indicate that the first organism is likely not diseased or has a low probability of developing the disease in the future; or it could indicate that the biological cells within the first organism are likely not abnormal (e.g., suggesting that the first organism is likely healthy but diseased).

[0212] Optionally, the site detection information is compared with multiple cutting location data respectively. If the site detection information is included in the multiple cutting location data, the probability of the first organism having a certain disease is predicted.

[0213] Optionally, a pre-trained disease prediction model can be obtained, and the site detection information can be analyzed through the disease prediction model to output the disease prediction result.

[0214] In some embodiments, for a first organism, at least two cell structure data can be obtained from one cfDNA, and the cell structure data corresponds to at least two cell structure data of the cfDNA. Since blood usually contains multiple cfDNAs, each cfDNA corresponds to at least two cell structure data. When predicting diseases in the first organism, the sequence cutting position corresponding to at least two cell structure data of each cfDNA can be predicted to provide more comprehensive information for disease prediction.

[0215] In an optional embodiment, the biological abnormality state of the first organism is predicted based on the sequence cut positions corresponding to at least two cell structure data.

[0216] In a schematic manner, after simulating and constructing at least two cell structure data for a first organism, statistical analysis of the sequence cutting positions corresponding to the predicted at least two cell structure data can be used to obtain the positions in the biomolecular sequences of the first organism that are more easily cut by molecularly acting substances, thereby predicting the abnormal biological state of the first organism, or it can be regarded as predicting the cellular mutation state of biological cells in the first organism.

[0217] A biological cell, or simply cell, is the basic unit of life that makes up an organism; it is the smallest structural and functional unit of life activities. Whether it's a single-celled organism (such as bacteria and yeast) or a multicellular organism (such as plants, animals, and humans), the cell is a fundamental component of the organism. Each cell performs essential functions necessary for life, including growth, division, metabolism, response to external stimuli, and apoptosis. Cells contain a rich array of molecules, including DNA, ribonucleic acid (RNA), and proteins. DNA contains information that encodes proteins and regulates cellular activities, while RNA plays a role in transmitting information and performing tasks during gene expression. Therefore, biological cells contain biological sequencing data, thus being responsible for the storage, transmission, and execution of genetic information during cellular processes.

[0218] In some embodiments, when performing statistical analysis on the sequence cleavage positions corresponding to at least two predicted cell structure data, the frequency of each sequence cleavage position is counted. A higher frequency of a sequence cleavage position indicates that the position in the biomolecule sequence is more easily cleaved by molecularly acting substances, and vice versa. A lower frequency of a sequence cleavage position indicates that the position in the biomolecule sequence is less easily cleaved by molecularly acting substances.

[0219] For example, cell structure data A, B, C, and D are constructed based on the biomolecular sequences corresponding to the first organism. The predicted sequence cut position for cell structure data A is 1, for cell structure data B it is 2, for cell structure data C it is 1, and for cell structure data D it is 3. The frequency of sequence cut position 1 is 2, sequence cut position 2 is 1, and sequence cut position 3 is 1. Therefore, sequence cut position 1 has the highest frequency, indicating that sequence cut position 1 in the biomolecular sequence of the first organism is more easily cleaved by molecularly interacting substances.

[0220] Optionally, a disease cut location dataset is obtained, which stores multiple disease-related cut location data, representing locations in the biomolecular sequence that are easily cut when the disease is present.

[0221] For example, the cutting position dataset includes cutting position 1, cutting position 9, cutting position 35, etc.; if the sequence cutting position 1 is predicted to be easily cut by molecularly acting substances based on the cell structure data corresponding to the first organism, and the sequence cutting position 1 exists in the cutting position dataset (sequence cutting position 1 and cutting position 1 correspond), then the first organism is predicted to have a certain probability of disease.

[0222] Optionally, after counting the frequency of each sequence cutting position, at least one sequence cutting position and the cutting frequency corresponding to at least one sequence cutting position are input into a pre-trained disease prediction model. The relationship between the sequence cutting position and the cutting frequency is analyzed through the disease prediction model, thereby outputting the disease prediction result.

[0223] The disease prediction model is a pre-trained neural network model, such as at least one of Convolutional Neural Network (CNN), Deep Neural Network (DNN), and Artificial Neural Network (ANN), without any limitation here.

[0224] In some embodiments, when performing statistical analysis on the sequence cutting positions corresponding to at least two predicted cell structure data respectively, the sequence cutting position corresponding to each cell structure data is input into a pre-trained disease prediction model, so as to analyze whether there is a correlation between the predicted sequence cutting position and the disease, and thus output the disease prediction result.

[0225] The disease prediction results described above can be presented in a binary classification manner. For example, a disease prediction result output of 1 indicates a predicted risk of disease, while a disease prediction result output of 0 indicates a predicted no risk of disease, and so on. Since the biomolecular sequence corresponding to the first organism is usually the substance that guides the activities of the organism's cells, if the disease prediction result input indicates a risk of disease, it means that the organism's cells may have a tendency to cellular mutation, i.e., the first organism has a biological abnormality. If the disease prediction result input indicates no risk of disease, it means that the organism's cells are highly likely to have a tendency to cellular mutation, thus predicting and displaying the biological abnormality state corresponding to the first organism, i.e., the first organism has a biological abnormality.

[0226] It is worth noting that the above are merely illustrative examples, and the embodiments of this application are not limited thereto.

[0227] In this embodiment, using the biomolecular sequence related to the first organism and additionally provided molecularly active substances, the most likely sequence cut positions of the molecularly active substances on the biomolecular sequence are analyzed. Since biological information such as diseases and genetic mutations is often reflected through sequence cut positions, by using multiple cell structure data obtained based on the combination of biomolecular sequences and molecularly active substances, the sequence cut positions of the biomolecular sequences within each cell structure data can be predicted as comprehensively and accurately as possible. Therefore, combining multiple sequence cut positions helps to more accurately analyze the disease risk of the first organism, improve the accuracy of judging abnormal biological states, and aid in the early diagnosis of diseases. This can better assist in disease avoidance and also contribute to the development of more comprehensive diagnostic methods and treatment strategies.

[0228] In an optional embodiment, considering that nucleosomes are structural units formed by DNA winding around histones, the dynamic arrangement and modification state of nucleosomes directly reflect gene regulation. Abnormalities are often early markers of diseases (especially cancer, autoimmune diseases, etc.). Nucleosome DNA, as the DNA within the nucleosome, can be analyzed to predict disease status by forecasting nucleosome DNA cleavage. Cleavage is related to cleaving enzymes, and structural prediction models can be used to predict the possible conformations formed between cleaving enzymes and nucleosomes.

[0229] Therefore, the above-mentioned method for predicting sequence cleavage sites can also be called "a precise prediction method for nucleosome DNA cleavage sites based on a structure prediction model," aiming to protect a cleavage site prediction protocol based on a structure prediction model. This protocol uses a large amount of conformational data generated by a statistical structure prediction model, combined with machine learning or statistical analysis methods, to calculate the most likely cleavage site of the cleaving enzyme on nucleosome DNA. Figure 11 As shown, the prediction process is explained as follows.

[0230] (i) Using structural prediction models to generate multiple conformational data of the binding of cleavage enzymes to nucleosome DNA.

[0231] To illustrate, 937 nucleosome data points (1110) were collected from the Structural Bioinformatics Research Collaboration website and analyzed. Nucleosome data points (1110) describe nucleosomes, the basic units of chromatin composed of DNA wrapped around histone octamers. Structural analysis of nucleosomes is crucial for understanding gene regulation.

[0232] Schematic, based on nucleosome data 1110, nine superior histone sequences 1121 were selected, including eight core histone sequences and one H1 sequence. The eight core histone sequences include two A2A sequences, two A2B sequences, two A3 sequences, and two A4 sequences, which can be collectively referred to as histone octamers or core histones. The H1 sequence is the amino acid sequence of the H1 histone (i.e., the coding information of the H1 histone). The H1 histone, also known as linker histone, is distinct from the histone octamers that wrap around DNA. The H1 histone is responsible for stabilizing the linker DNA between nucleosomes, helping chromatin to compress into higher-order structures.

[0233] Meanwhile, double-stranded DNA (dsDNA)1122 was used as the prediction target. Double-stranded DNA contains two sequences.

[0234] Since cfDNA formation is associated with natural apoptosis or disease, and natural apoptosis is a natural physiological process of cells, while disease-induced abnormal apoptosis is due to cell damage, stress, or pathological factors, cfDNA can be found in bodily fluids (such as human blood) due to natural apoptosis (a natural physiological process), and disease-induced abnormal apoptosis can also lead to the presence of cfDNA in bodily fluids. Therefore, cfDNA collected from the first organism is used as a predictive object for subsequent analysis. For example, after collecting cfDNA, the corresponding DNA sequence of the first organism can be derived for analysis.

[0235] In addition to being related to the cfDNA being predicted, the determination of the cleavage site is also related to the cleavage enzyme (such as an endonuclease). Illustratively, additional cleavage enzymes are obtained to study the cleavage of the DNA sequence in order to predict the cleavage site during the cleavage process.

[0236] Enzymes are a class of enzymes that can cleave specific chemical bonds in large molecules (such as DNA, RNA, and proteins). Examples of enzymes include restriction endonucleases (which recognize specific DNA sequences and cleave double-stranded DNA at those locations) and nucleic acid cleavage enzymes (which specifically cleave DNA or RNA). Endonucleases cleave the strand at specific locations within the molecule, forming internal cleavage sites; restriction endonucleases belong to this class of enzymes. Exonucleases cleave sequentially from the ends of the molecule and typically act on single-stranded DNA or RNA.

[0237] Optionally, taking deoxyribonuclease I (DNASE1), deoxyribonuclease I like 3 (DNASE1L3), and DNA fragmentation factor subunit Beta (DFFB) as examples, the cleavage enzymes may be illustrated as containing a single sequence.

[0238] DNASE1 is an endonuclease that non-specifically cleaves DNA strands to generate short fragments with 5'-phosphate groups. DNASE1 plays a crucial role in apoptosis, DNA degradation, and immune regulation. DNASE1L3 is a homolog of DNASE1 and primarily participates in DNA degradation during apoptosis; compared to DNASE1, DNASE1L3 exhibits higher cleavage specificity for specific DNA sequences or structures. DFFB is one of the key enzymes in DNA fragmentation during apoptosis. It forms a complex with the DNA fragmentation factor α subunit (DFFA). Upon activation by cysteine-aspartic protease (caspase), DFFB is released and cleaves DNA to generate typical DNA ladder-like fragments.

[0239] Taking DNASE1L3 as the cleavage inhibitor (molecular interacting substance) used in the analysis as an example, then Figure 11 The molecular active substance shown is DNASE1L3 cleavage enzyme 1123.

[0240] Optionally, based on the above 12 sequences, a structural prediction model can be used to make large-scale predictions and generate multiple structural data.

[0241] In illustrative terms, structure prediction models are tools and methods that use computational methods to predict the three-dimensional structure of biological macromolecules such as molecules and proteins. The structural data predicted by these models represents the three-dimensional structure of the biological macromolecules. Structure prediction models such as AlphaFold, Proteinix, Chai-1, and Rosetta predict structural data by simulating the protein folding process. For example: Figure 11 AlphaFold was used as the structure prediction model to analyze 12 sequences in order to generate predicted structure data.

[0242] As an illustration, the order of the eight core histone sequences out of the 12 sequences is independent of the prediction process; the predicted structural data focuses on describing the predicted spatial cleavage between the cleaving enzyme and DNA. For example, using a structural prediction model to perform large-scale predictions on the 12 sequences generates 2500 different predicted structural data points. Each predicted structural data point represents the predicted spatial cleavage between the cleaving enzyme and DNA, such as: Figure 11 The predicted complex structures 1 to 2500 are shown.

[0243] Optionally, by analyzing multiple structural data, the cleavage site distribution of the cleavage enzyme can be obtained, as shown in a table; or, as shown in... Figure 11 The bar chart shown is in the form of 1130, where the horizontal axis represents different sequence cutting positions and the vertical axis represents the number of cuts (the amount of structural data) corresponding to each different sequence cutting position.

[0244] Furthermore, the study found that most of the DNA in the generated predicted structure data was the Widom601 sequence. Widom601 is a classic and widely used DNA sequence, typically used in studies of nucleosome assembly and chromatin structure. The unique feature of the Widom601 sequence is that it is a high-affinity DNA sequence, capable of binding very stably to histones, thus forming stable predicted structure data. Additionally, the generated predicted structure data is not identical to the cfDNA of cells cleaved by the DNASE1L3 enzyme.

[0245] In addition, the study found that in the generated predicted structure data, the length of DNA is usually around 215 bps. bps is the unit of DNA base pairs, and 215 bps represents 215 base pairs. Since DNA has a double-stranded structure, the double helix length is expressed in base pairs.

[0246] Indicatively, each structural data point represents a predicted cleavage pattern when a cleaving enzyme acts on DNA. Therefore, a predicted cleavage site is obtained from each structural data point. By summing up the cleavage sites corresponding to multiple structural data points, the distribution of cleavage sites of the cleaving enzyme is obtained.

[0247] In an optional embodiment, after generating multiple predicted structural data, a structural filtering algorithm is introduced to filter the predicted structural data. The structural filtering algorithm can be viewed as a set of conditional rules used to filter the predicted structural data. The purpose of structural filtering is to filter out problematic predicted structural data from the multiple predicted structural data according to the structural filtering algorithm, thereby retaining structural data that can fold well into the correct nucleosome complex, which serves as the cellular structural data for segmentation site analysis.

[0248] Indicative, such as Figure 9 As shown, 34 / 2500 represents that out of 2500 structural data (or conformations), only 34 structural data are correctly identified based on the structural filtering algorithm. These 34 structural data are used as the cell structural data for segmentation site analysis. Finally, a table is output as the cleavage profile, illustrating the cut fragments and their locations. The "|" symbol represents the cut location, distinguishing between left and right sides.

[0249] In some embodiments, the structure filtering algorithm is described as follows, wherein at least one of the following three conditional rules must be followed.

[0250] (1) The cleavage enzyme should not be bound to the core histone.

[0251] The core histone represents the aforementioned histone octamer; since the analysis and study are about how the cutting enzyme cuts the DNA sequence, the cutting enzyme should not be bound to the core histone, but rather should have a cutting relationship with the DNA sequence.

[0252] Taking DNASE1L3 as an example, if DNASE1L3 is bound to a core histone, it does not cut the DNA sequence and therefore does not play a cutting role. This is because when studying the cutting site, it is necessary to discard the structural data on which the cutting enzyme is bound to the core histone from multiple structural data.

[0253] In other words, structural data where the cleavage enzyme is associated with the DNA sequence are retained, while structural data where the cleavage enzyme is bound to the core histone are discarded.

[0254] Furthermore, the cleavage enzyme can be replaced with any target that requires focused analysis, such as, but not limited to, DNASE1, DFFB, etc., making it suitable for the study of various DNA cleavage enzymes.

[0255] (2) The H1 histone should be in the correct position.

[0256] To illustrate, the H1 histone is also known as a linker histone or linker protein. The correct location of the H1 histone should be at the linker DNA, which is the DNA region between two nucleosomes. It is usually relatively long and does not have a structure directly surrounding the core histone.

[0257] Therefore, if the H1 histone is located at the DNA junction, it means that the H1 histone is in the correct position, and the structural data of the H1 histone at the DNA junction is retained as cellular structural data; if the H1 histone is not located at the DNA junction, it means that the H1 histone is not in the correct position, and the structural data of the H1 histone not located at the DNA junction is discarded.

[0258] (3) cfDNA can properly surround the core histone and H1 histone.

[0259] Indicatively, before the DNA sequence is cut, the DNA is wrapped around core histones. cfDNA typically originates from cell death (such as natural or aberrant apoptosis) and is fragments of intracellular chromatin; DNA is usually wrapped around histones to form nucleosomes. During cell death, chromatin is broken down, and DNA is released from histones, forming short DNA fragments. Although cfDNA is free-floating, it may still carry some histone binding information because DNA and histones are not completely separated during cell death. Sometimes, free cfDNA still retains traces of interaction with core histones and H1 proteins.

[0260] Therefore, free cfDNA in bodily fluids is not a completely naked DNA fragment, but may have a small amount of histone attached. Although cfDNA may not completely surround the core histones and H1 histones, it is highly likely to surround some of them. That is, the release of cfDNA is not just a simple shedding of DNA, but may also be accompanied by certain structural "traces," such as histone binding. In addition, compared with cfDNA formed by natural apoptosis, cfDNA formed by abnormal apoptosis may surround more core histones and H1 histones.

[0261] Therefore, studying cfDNA that can correctly surround the core histones and H1 histones can help analyze and even predict richer cellular biological information (such as modes of cell death). Thus: retain structural data where cfDNA can correctly surround the core histones and H1 histones, and discard structural data where cfDNA cannot correctly surround the core histones and H1 histones.

[0262] In some embodiments, the above structure filtering algorithm can also be implemented as a case prohibition method, such as requiring that at least one of the following three prohibited cases does not exist (the three cases correspond one-to-one with the three condition rules mentioned above).

[0263] (1) Ectopic cleavage enzymes: bind to core histones instead of DNA sequences.

[0264] Indicatively, the prohibited condition (1) corresponds to the above condition rule (1). Since the analysis studies the cleavage of DNA sequences by the cleavage enzyme, the cleavage enzyme should be bound to the DNA sequence (i.e., the cleavage relationship is predicted) and should not be bound to the core histone. Therefore, the structural data of the cleavage enzyme and the core histone binding need to be discarded.

[0265] (2) Abnormal H1 histone location: located outside the normal position of the DNA strand.

[0266] Indicatively, the prohibited condition (2) corresponds to the above condition rule (2). Since the correct position of the H1 histone should be at the DNA linker, if the H1 histone is located outside the normal position of the DNA strand, i.e. not at the DNA linker, the structural data of the H1 histone with an incorrect position is discarded.

[0267] (3) Incorrect cfDNA wrapping: cfDNA cannot wrap the core and cannot interact normally with H1 histones.

[0268] Indicatively, the prohibited condition (3) corresponds to the above condition rule (3). Since cfDNA is not a completely naked DNA fragment during apoptosis, but may be attached with a small amount of histones, if cfDNA cannot wrap the core and cannot interact normally with H1 histones, it means that the structural data containing the cfDNA is wrong, that is, the structural data in which cfDNA cannot wrap the core and cannot interact normally with H1 histones should be discarded.

[0269] In some embodiments, taking the structure screening based on conditional rules as an example, from multiple structure data generated by the structure prediction model, structure data that simultaneously meets the above-mentioned conditional rules (1), (2) and (3) are selected as cell structure data that need to be predicted for cleavage sites; or, from multiple structure data generated by the structure prediction model, structure data that meets at least one of the above-mentioned conditional rules (1), (2) and (3) are selected as cell structure data that need to be predicted for cleavage sites.

[0270] Taking the structure screening based on prohibition conditions as an example, from the multiple structure data generated by the structure prediction model, the structure data that does not meet the above prohibition conditions (1), (2) and (3) are selected as the cell structure data that needs to be predicted for the cutting site; or, from the multiple structure data generated by the structure prediction model, the structure data that does not meet at least one of the above prohibition conditions (1), (2) and (3) are selected as the cell structure data that needs to be predicted for the cutting site.

[0271] In some embodiments, for the above-mentioned condition rule (1) or prohibition condition (1), the purpose is to analyze the relationship between the cutting enzyme and the DNA sequence under structural data. The cutting enzyme should be bound to the DNA sequence, and the binding determination is based on the distance between the cutting enzyme and the DNA sequence.

[0272] As an illustration, for the generated structural data, there may be some structural data that indicates that the reaction pocket of the cleaving enzyme is not bound to the DNA sequence, that is, there may be some structural data that indicates that the cleaving enzyme is not bound to the DNA sequence. Therefore, it cannot be used as cell structural data for predicting cleavage sites.

[0273] The reaction pocket of a cutting enzyme is its active site or catalytic site, typically a specific region on the enzyme that is dedicated to binding to the substrate molecule (i.e., DNA sequence) and carrying out a chemical reaction. Therefore, for a cutting enzyme, the reaction pocket is the region on the enzyme that is used to recognize and cut a specific molecule.

[0274] Optionally, for any given structural data, the first distance between the first critical residue in the reaction pocket of the cleavage enzyme and any atom in the DNA sequence is calculated; if the first distance is less than a first preset threshold, it indicates that the cleavage enzyme has correctly captured the DNA sequence, and this is considered as a binding between the cleavage enzyme and the DNA sequence.

[0275] Schematic, the first key residue in the reaction pocket refers to the key amino acid residue in the reaction pocket of the cleavage enzyme that participates in the catalytic reaction and substrate molecule recognition. The first key residue plays an important role in the interaction between the cleavage enzyme and the substrate molecule, and the first key residue is usually based on pre-determined information of the cleavage enzyme.

[0276] DNA sequences, as substrate molecules corresponding to cleavage enzymes, consist of multiple atoms. The composition of these atoms in a DNA molecule includes: carbon (C) atoms present in deoxyribose and bases, which form the molecular backbone; hydrogen (H) atoms that are bound to carbon atoms and constitute the organic part of the molecule; oxygen (O) atoms present in sugar rings and phosphate groups that help maintain the structure of the DNA molecule; nitrogen (N) atoms present in bases (adenine A, guanine G, cytosine C, thymine T) that participate in hydrogen bonding for base pairing; and phosphorus (P) atoms present in phosphate groups of DNA to form the backbone.

[0277] For example: the first critical residue in the reaction pocket of the DNASE1L3 cleavage enzyme is 78, with a pre-set first preset threshold. (E) is a unit of measurement. Taking 0.1 nm as an example, an arbitrary atom is an atom arbitrarily selected from the DNA molecule (such as a randomly selected atom or the nearest atom); calculate the first distance between the first key residue 78 and the selected arbitrary atom, which can also be called the interstrand distance; if the first distance corresponding to structural data 1 is less than This indicates that structural data 1 represents that the cleavage enzyme has correctly captured the DNA sequence. At this point, it is considered that there is a binding between the cleavage enzyme and the DNA sequence, and structural data 1 is used as cell structural data.

[0278] In some embodiments, for the above condition rule (2) or prohibition condition (2), the purpose is to analyze the relationship between H1 histone and linker DNA under structural data, whereby H1 histone should be located at the linker DNA, i.e., the location of H1 histone needs to be verified.

[0279] As an illustration, with respect to the generated structural data, there may be some structural data that incorrectly characterizes the location of the H1 histone (e.g., not located between the linkers of two DNA segments).

[0280] Optionally, the structural data is generated based on nucleosome data. The H1 histones included in the nucleosome data are the same as those included in the structural data, but the spatial locations of the H1 histones in the nucleosome data and the H1 histones in the structural data may differ.

[0281] Schematic, a first central residue is determined from the H1 histones included in the nucleosome data, and a second central residue identical to the first central residue is determined from the H1 histones included in the structural data, but the spatial positions of the first and second central residues differ; the residue distance between the first and second central residues (e.g., denoted as h1_distance) is calculated; if the residue distance is less than a second preset threshold, it indicates that the position of the H1 histone is correct. For example, the second preset threshold is... If the residue distance corresponding to structural data 2 is less than If the position of the H1 histone in structural data 2 is correct, then structural data 2 will be used as the cell structural data.

[0282] In some embodiments, with respect to the above-mentioned condition rule (3) or prohibition condition (3), the purpose is to analyze the relative relationship between cfDNA and the core under structural data, and to analyze the relative relationship between cfDNA and H1 histone under structural data.

[0283] Indicatively, with respect to the generated structural data, there may be some structural data that indicates that the cfDNA does not surround the core and H1 histone, that is, the structural data indicates incorrect cfDNA entanglement.

[0284] Optionally, the structural data is generated based on nucleosome data. The H1 histones included in the nucleosome data are the same as those included in the structural data, but the spatial locations of the H1 histones in the nucleosome data and the structural data may differ. Similarly, the core histones included in the nucleosome data are the same as those included in the structural data, but the spatial locations of the core histones in the nucleosome data and the structural data may also differ, etc.

[0285] To illustrate, a structural alignment method is used to align structural data with nucleosome data, and the root mean square deviation (RMSD) between the two is calculated. If the RMSD is less than a third preset threshold, it indicates that the structural data is similar to the given nucleosome data, and also represents that the structural data is similar to the core and DNA winding pattern of the correct nucleosome.

[0286] For example: Structural data is represented as x, and the given nucleosome data is a PDB structure y (e.g., 8tb9, representing nucleosome data with sequence number 8tb9 in the PDB). The sequences in the structural data and the corresponding sequences in the nucleosome data are aligned. The alignment tool in PyMOL can be used for this calculation, and the root mean square deviation (RMSD) between the two is calculated and represented as pymol_rmsd. If the third preset threshold is 3, then if the pymol_rmsd corresponding to structural data 3 is less than 3, it indicates that the structural data is similar to the given nucleosome data, and therefore structural data 3 is retained as the cell structure data for subsequent analysis.

[0287] (ii) Analyze cell structure data using statistical methods to identify the interaction characteristics between cleavage enzymes and DNA.

[0288] As an illustration, after filtering a large amount of structural data through a structural filtering algorithm, some structural data is retained as cell structural data for statistical analysis; then, the interaction characteristics between cleaving enzymes and DNA can be analyzed based on the cell structural data, that is, the cleaving sites on DNA acted by cleaving enzymes can be analyzed.

[0289] Indicatively, the nearest DNA atom is located and the base is confirmed by the second key residue of the cleavage enzyme; since each base consists of multiple DNA atoms, if the nearest DNA atom is found, the base formed by the aggregation of that DNA atom is taken as the confirmed base.

[0290] For example, the second key residue of the cleavage enzyme is selected as 138. Based on the key residue 138, the nearest DNA atom is found, and the base that is polymerized from this DNA atom is taken as the confirmed base.

[0291] Based on this base, the DNA fragments on the left and right are extracted to obtain the cleavage pattern; in addition, the cleavage site on the sequence is recorded.

[0292] (III) Based on statistical results, predict the most likely cleavage site of the cleavage enzyme on nucleosome DNA.

[0293] To illustrate, after obtaining multiple cell structure data, the cleavage sites corresponding to each of the multiple cell structure data are analyzed; the distribution of cleavage sites is obtained by summarizing the multiple cleavage sites, and the distribution of cleavage sites is presented in tabular form.

[0294] To illustrate, count the number of sites with the same cutting site; if there are multiple cutting sites, determine the number of sites corresponding to each cutting site.

[0295] Optionally, the ratio between the number of each site and the number of cell structure data is determined to obtain the number ratio corresponding to various cleavage sites respectively; if the number ratio corresponding to a cleavage site is larger, it means that the frequency of cleavage of that site is higher, and therefore it means that the position is more likely to be the cleavage site corresponding to the cleavage enzyme.

[0296] To illustrate, the ratio of the number of structures at the same location to the total number of all reasonable conformations is statistically analyzed (e.g., 9 / 34). The higher the frequency of a conformation at a certain incision site, the more likely that site is to be a cleavage site for the enzyme.

[0297] In other words, the above process first inputs the sequence information of the cleavage enzyme and nucleosome DNA; then, a structural prediction model generates multiple conformations of the cleavage enzyme binding to nucleosome DNA (i.e., a large amount of predicted structural data or screened cell structural data); subsequently, statistical methods are used to analyze the conformational data to identify the interaction characteristics between the cleavage enzyme and DNA, resulting in a tabular distribution of cleavage sites; then, based on the statistical results, the most likely cleavage site of the cleavage enzyme on nucleosome DNA is predicted (e.g., determining the most frequent sequence cleavage position); finally, the predicted cleavage sites and their confidence scores are output for biological analysis processes such as disease prediction based on the cleavage sites.

[0298] In illustrative terms, the above analytical process can be applied to early non-invasive cancer detection scenarios. For example, by using the above process to analyze the genome, significant differences in DNA sequence cutting positions between cancer patients and healthy individuals can be compared, and these differences can be explained based on structural mechanisms to obtain statistically significant differential sites.

[0299] Any organism can go to a hospital for a blood test. Based on the cfDNA fragments in the blood, the aforementioned sequence cutting location determination process is performed and mapped to the corresponding location on the genome to find significantly different cutting sites. Based on these cutting sites, the probability of an organism developing a certain type of cancer can be assessed, thus achieving early non-invasive cancer detection before cancer cells develop to stage 1.

[0300] Furthermore, the above process is not only applicable to the prediction of nucleosome DNA cleavage sites, but can also be extended to other research areas of DNA-protein interactions, such as the prediction of transcription factor binding sites and the analysis of DNA repair enzyme mechanisms, without limitation here.

[0301] In summary, by utilizing the biomolecular sequences associated with the first organism and additional molecularly active substances, we analyzed the most likely sequence cut sites on the biomolecular sequences. Since biological information such as diseases and genetic mutations is often reflected through sequence cut sites, predicting these sites as comprehensively and accurately as possible helps to more accurately analyze the disease risk of the first organism, serving as an early detection basis for disease identification. This can better assist in disease avoidance and also contribute to the development of more sophisticated diagnostic methods and treatment strategies.

[0302] This application proposes an innovative method based on a structure prediction model for accurately predicting the cleavage sites of cleaving enzymes on nucleosome DNA. By simulating the interaction between the cleaving enzyme and nucleosome DNA, the cleavage mechanism is elucidated, and the precise location of the cleavage site is predicted. In the life sciences, research on the DNA cleavage mechanism of cleaving enzymes is of great significance, not only for early cancer diagnosis but also for research on other DNA cleavage-related diseases, providing new technical support for life sciences and medicine. Therefore, combining the structure prediction model with statistical methods enables precise localization of the cleavage site, significantly improving prediction accuracy. Furthermore, it supports the replacement of various structure prediction models and cleaving enzymes, making it suitable for a wide range of research scenarios and possessing strong versatility. Moreover, combining the structure prediction model with statistical analysis fills the technical gap in nucleosome DNA cleavage site prediction. Finally, it has significant application value in early cancer diagnosis, DNA damage repair research, and gene editing, and its high versatility and scalability allow for widespread application in cancer diagnosis, DNA damage repair, and gene editing, providing new technical tools for life science research and clinical medical applications.

[0303] Figure 12 A structural block diagram of a sequence cutting position prediction device provided in an exemplary embodiment of this application is shown below. Figure 12 As shown, the device includes the following parts:

[0304] The acquisition module 1210 is used to acquire the biomolecular sequence corresponding to the first organism, wherein the biomolecular sequence includes at least two molecular sites;

[0305] The acquisition module 1210 is also used to acquire molecularly active substances, which are used to cleave the biomolecule sequence;

[0306] The prediction module 1220 is used to predict the sequence cut position corresponding to the biomolecule sequence based on the relative positional relationship between the molecular active substance and the biomolecule sequence. The sequence cut position is used to characterize the cut position of the molecular active substance in the at least two molecular sites.

[0307] In an optional embodiment, the acquisition module 1210 is further configured to determine a first key sub-substance from the molecularly acting substance, wherein the first key sub-substance is a sub-substance in the molecularly acting substance that participates in the catalytic segmentation process; based on the relative positional relationship between the molecularly acting substance and the biomolecular sequence, determine a first molecular site in the biomolecular sequence that is closest to the first key sub-substance; and based on the first molecular site, predict the sequence cleavage position corresponding to the biomolecular sequence.

[0308] In an optional embodiment, the acquisition module 1210 is further configured to, for the first cell structure data in the at least two cell structure data, determine a first key sub-substance from the molecularly active substance of the first cell structure data, wherein the first key sub-substance is a sub-substance in the molecularly active substance that participates in the catalytic cleavage process; determine a first molecular site closest to the first key sub-substance from the biomolecular sequence of the first cell structure data based on the relative positional relationship between the molecularly active substance and the biomolecular sequence; predict the sequence cleavage position corresponding to the first cell structure data based on the first molecular site, and predict the sequence cleavage positions corresponding to the at least two cell structure data respectively.

[0309] In an optional embodiment, the molecular site is used to store a biomolecule, the biomolecule comprising at least one biological atom;

[0310] The acquisition module 1210 is further configured to determine the first biological atom closest to the first key molecular substance based on the relative positional relationship between the molecular active substance and the biomolecule sequence; determine the first biomolecule including the first biological atom from the biomolecule sequence; and determine the first molecular site where the first biomolecule is located.

[0311] In an optional embodiment, the acquisition module 1210 is further configured to determine at least one adjacent molecular site from the biomolecular sequence that is adjacent to the first molecular site; determine a second molecular site from the at least one adjacent molecular site based on the distance relationship between the first biological atom and the at least one adjacent molecular site; and determine the sequence cutting position corresponding to the biomolecular sequence between the first molecular site and the second molecular site.

[0312] In an optional embodiment, the acquisition module 1210 is further configured to collect the free molecular sequence corresponding to the first organism, wherein the free molecular sequence is a fragment of biomolecule sequence free in the first organism; and determine the biomolecule sequence corresponding to the first organism based on the free molecular sequence.

[0313] In an optional embodiment, the acquisition module 1210 is further configured to determine the biological sequencing data corresponding to the first organism based on the free molecular sequence; acquire public cell data, the public cell data including core histones and linker histones; and wrap the core histones, the linker histones and the biological sequencing data according to sequence generation rules to obtain the biomolecular sequence corresponding to the first organism.

[0314] In an optional embodiment, the acquisition module 1210 is further configured to perform structural prediction on the biomolecule sequence and the molecular active substance to generate at least two cell structure data corresponding to the free molecular sequence; based on the relative positional relationship between the molecular active substance and the biomolecule sequence in the cell structure data, predict the sequence cutting position corresponding to the biomolecule sequence in the cell structure data, and obtain the sequence cutting positions corresponding to the at least two cell structure data respectively.

[0315] In an optional embodiment, the acquisition module 1210 is further configured to perform the structure prediction on the biomolecule sequence and the molecular active substance, generating at least two predicted structure data corresponding to the free molecule sequence, wherein the predicted structure data is structure data obtained by combining the biomolecule sequence and the molecular active substance; and from the at least two predicted structure data, the predicted structure data that meets the structure screening criteria are selected as the cell structure data, wherein the structure screening criteria are based on the spatial positional relationship between the biomolecule sequence and the molecular active substance.

[0316] In an optional embodiment, the prediction module 1220 is further configured to: select from the at least two predicted structural data where the molecular active substance is not bound to the core histone as the cell structural data; or select from the at least two predicted structural data where the connecting histone is within the biological sequencing data as the cell structural data; or select from the at least two predicted structural data where the free molecular sequence surrounds the core histone and the connecting histone as the cell structural data.

[0317] In an optional embodiment, the prediction module 1220 is further configured to, for the first predicted structural data in the at least two predicted structural data, determine the first key residue corresponding to the molecular active substance in the first predicted structural data, and determine the first atom in the biological sequencing data; obtain the first distance between the first key residue and the first atom; and, if the first distance is less than a first preset threshold, use the first predicted structural data as the cell structural data.

[0318] In an optional embodiment, the prediction module 1220 is further configured to, for the second predicted structural data in the at least two predicted structural data, determine a first central residue in the connectometic protein in the second predicted structural data, and determine a second central residue in the connectometic protein corresponding to the first central residue from publicly available cell data; calculate the residue distance between the first central residue and the second central residue; and, if the residue distance is less than a second preset threshold, use the second predicted structural data as the cell structural data.

[0319] In an optional embodiment, the prediction module 1220 is further configured to, for the third predicted structural data among the at least two predicted structural data, determine at least two first sequences in the third predicted structural data, the at least two first sequences including the biomolecule sequence and the molecular active substance in the third predicted structural data; and determine at least two second sequences in the published cell data, the at least two second sequences including the biomolecule sequence and the molecular active substance in the published cell data; align the first sequences and second sequences representing the same information; and obtain the root mean square deviation value between the third predicted structural data and the published cell data based on the sequence alignment; and if the root mean square deviation value is less than a third preset threshold, use the third predicted structural data as the cell structural data.

[0320] In an optional embodiment, the prediction module 1220 is further configured to determine at least one sequence cut position based on the sequence cut positions corresponding to the at least two cell structure data respectively; for a first sequence cut position among the at least one sequence cut position, determine the number of structural data of the cell structure data having the first sequence cut position, and determine the number of structural data corresponding to the at least one sequence cut position respectively; and predict the biological abnormal state of the first organism based on the number of structural data corresponding to the at least one sequence cut position respectively.

[0321] In an optional embodiment, the prediction module 1220 is further configured to use at least one sequence cutting position with the largest number of structural data as the site detection information corresponding to the first organism; the site detection information is information used to detect the abnormal biological state of the first organism.

[0322] In an optional embodiment, the acquisition module 1210 is further configured to acquire a disease cutting location dataset, which stores multiple cutting location data, the cutting location data being used to characterize the location of the cutting in the biomolecular sequence when the disease is present; matching the sequence cutting locations corresponding to the at least two cell structure data with the disease cutting location dataset to obtain the biological abnormal state of the first organism; wherein, if the disease cutting location dataset includes the sequence cutting location, a first biological abnormal state is output; or, if the disease cutting location dataset does not include the sequence cutting location, a second biological abnormal state is output.

[0323] In summary, by utilizing the biomolecular sequences associated with the first organism and additional molecularly active substances, we analyzed the most likely sequence cut sites on the biomolecular sequences. Since biological information such as diseases and genetic mutations is often reflected through sequence cut sites, predicting these sites as comprehensively and accurately as possible helps to more accurately analyze the disease risk of the first organism, serving as an early detection basis for disease identification. This can better assist in disease avoidance and also contribute to the development of more sophisticated diagnostic methods and treatment strategies.

[0324] It should be noted that the sequence cutting position prediction device provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the sequence cutting position prediction device and the sequence cutting position prediction method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0325] Figure 13 This illustration shows a schematic diagram of the structure of a server provided in an exemplary embodiment of this application. Specifically, it includes the following structure.

[0326] Server 1300 includes a Central Processing Unit (CPU) 1301, a system memory 1304 including Random Access Memory (RAM) 1302 and Read Only Memory (ROM) 1303, and a system bus 1305 connecting the system memory 1304 and the CPU 1301. Server 1300 also includes a mass storage device 1306 for storing the operating system 1313, application programs 1314, and other program modules 1315.

[0327] Mass storage device 1306 is connected to central processing unit 1301 via a mass storage controller (not shown) connected to system bus 1305. Mass storage device 1306 and its associated computer-readable media provide non-volatile storage for server 1300. That is, mass storage device 1306 may include computer-readable media (not shown) such as hard disk or compact disc read-only memory (CD-ROM) drives.

[0328] Without loss of generality, computer-readable media can include computer storage media and communication media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media include RAM, ROM, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other solid-state storage technologies, CD-ROM, digital versatile disc (DVD) or other optical storage, magnetic tape cassettes, magnetic tape, disk storage, or other magnetic storage devices. Of course, those skilled in the art will recognize that computer storage media are not limited to the above-mentioned types. The system memory 1304 and mass storage device 1306 described above can be collectively referred to as memory.

[0329] According to various embodiments of this application, server 1300 can also be connected to a remote computer on a network, such as the Internet. That is, server 1300 can be connected to network 1312 via network interface unit 1311 connected to system bus 1305, or it can also use network interface unit 1311 to connect to other types of networks or remote computer systems (not shown).

[0330] The aforementioned memory also includes one or more programs, which are stored in the memory and configured to be executed by the CPU.

[0331] Embodiments of this application also provide a computer device including a processor and a memory. The memory stores at least one instruction, at least one program, code set, or instruction set. The processor loads and executes the at least one instruction, at least one program, code set, or instruction set to implement the sequence cutting position prediction method provided in the above-described method embodiments. Optionally, the computer device may be a terminal or a server.

[0332] Embodiments of this application also provide a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the sequence cutting position prediction method provided in the above-described method embodiments.

[0333] Embodiments of this application also provide a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the sequence cutting position prediction method described in any of the above embodiments.

[0334] Optionally, the computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), solid-state drives (SSDs), or optical discs, etc. The random access memory may include resistive random access memory (ReRAM) and dynamic random access memory (DRAM). The sequence numbers of the embodiments in this application are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0335] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0336] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for predicting sequence cutting positions, characterized in that, The method includes: Obtain the biomolecular sequence corresponding to the first organism, wherein the biomolecular sequence includes at least two molecular sites; Obtain a molecularly active substance, which is used to cleave the biomolecule sequence; Based on the relative positional relationship between the molecular active substance and the biomolecule sequence, the sequence cleavage position corresponding to the biomolecule sequence is predicted, and the sequence cleavage position is used to characterize the cleavage position of the molecular active substance in the at least two molecular sites.

2. The method according to claim 1, characterized in that, The prediction of the sequence cut position corresponding to the biomolecule sequence based on the relative positional relationship between the molecularly active substance and the biomolecule sequence includes: A first key sub-substance is determined from the molecularly interacting substance, wherein the first key sub-substance is a sub-substance in the molecularly interacting substance that participates in the catalytic splitting process; Based on the relative positional relationship between the molecular active substance and the biomolecule sequence, the first molecular site closest to the first key molecular substance is determined from the biomolecule sequence; Based on the first molecular site, the sequence cut position corresponding to the biomolecule sequence is predicted.

3. The method according to claim 2, characterized in that, The molecular site is used to store biomolecules, the biomolecules comprising at least one biological atom; The determination of the first molecular site closest to the first key molecular substance from the biomolecular sequence based on the relative positional relationship between the molecularly active substance and the biomolecular sequence includes: Based on the relative positional relationship between the molecularly interacting substance and the biomolecule sequence, the first bioatom closest to the first key molecular substance is determined; Identify a first biomolecule comprising the first biological atom from the biomolecule sequence, and determine the first molecular site where the first biomolecule is located.

4. The method according to claim 3, characterized in that, The step of predicting the sequence cut position corresponding to the biomolecule sequence based on the first molecular site includes: Identify at least one adjacent molecular site from the biomolecule sequence that is adjacent to the first molecular site; Based on the distance relationship between the first biological atom and the at least one adjacent molecular site, a second molecular site is determined from the at least one adjacent molecular site; Determine the sequence cut position corresponding to the biomolecule sequence between the first molecular site and the second molecular site.

5. The method according to any one of claims 1 to 4, characterized in that, The step of obtaining the biomolecular sequence corresponding to the first organism includes: Collect the free molecular sequence corresponding to the first organism, wherein the free molecular sequence is a fragment of biomolecule sequence free in the first organism; The biomolecule sequence corresponding to the first organism is determined based on the free molecule sequence.

6. The method according to claim 5, characterized in that, The step of determining the biomolecule sequence corresponding to the first organism based on the free molecule sequence includes: Based on the free molecule sequence, determine the biological sequencing data corresponding to the first organism; Acquire publicly available cellular data, including core histones and connective histones; By winding the core histone, the linker histone, and the biological sequencing data according to the sequence generation rules, the biomolecular sequence corresponding to the first organism is obtained.

7. The method according to any one of claims 1 to 4, characterized in that, The prediction of the sequence cut position corresponding to the biomolecule sequence based on the relative positional relationship between the molecularly active substance and the biomolecule sequence includes: Perform structural prediction on the biomolecule sequence and the molecular active substance to generate at least two cell structure data corresponding to the free molecule sequence; Based on the relative positional relationship between the molecular active substances and the biomolecule sequences within the cell structure data, the sequence cutting positions corresponding to the biomolecule sequences within the cell structure data are predicted, thereby obtaining the sequence cutting positions corresponding to the at least two cell structure data respectively.

8. The method according to claim 7, characterized in that, The step of performing structural prediction on the biomolecule sequence and the molecular active substance to generate at least two cell structure data corresponding to the free molecular sequence includes: The structure prediction is performed on the biomolecule sequence and the molecular active substance to generate at least two predicted structure data corresponding to the free molecule sequence. The predicted structure data is the structure data obtained by combining the biomolecule sequence and the molecular active substance. From the at least two predicted structural data, the predicted structural data that meet the structural screening criteria are selected as the cell structural data. The structural screening criteria are based on the spatial positional relationship between the biomolecule sequence and the molecular interacting substance.

9. The method according to claim 8, characterized in that, The step of selecting the predicted structural data that meet the structural screening criteria from the at least two predicted structural data as the cell structural data includes: From the at least two predicted structural data sets, the predicted structural data where the molecular active ingredient is not bound to a core histone are selected as the cell structural data; or... From the at least two predicted structural data, the predicted structural data in which the connector histones are located within the biological sequencing data are selected as the cell structural data; or... From the at least two predicted structural data, the predicted structural data in which the free molecular sequence surrounds the core histone and the linker histone are selected as the cell structural data.

10. The method according to claim 9, characterized in that, The step of selecting predicted structural data from the at least two predicted structural data from which the molecular active substance is not bound to a core histone as the cell structural data includes: For the first predicted structural data in the at least two predicted structural data, determine the first key residue corresponding to the molecular active substance in the first predicted structural data, and determine the first atom in the biological sequencing data; Obtain the first distance between the first key residue and the first atom; If the first distance is less than a first preset threshold, the first predicted structural data is used as the cell structural data.

11. The method according to claim 9, characterized in that, The step of selecting the predicted structural data from the at least two predicted structural data that link histones within the biological sequencing data as the cell structural data includes: For the second predicted structural data in the at least two predicted structural data, a first central residue in the connectometic protein in the second predicted structural data is determined, and a second central residue in the connectometic protein corresponding to the first central residue is determined from publicly available cell data; Calculate the residue distance between the first central residue and the second central residue; If the distance between the residues is less than a second preset threshold, the second predicted structural data is used as the cell structural data.

12. The method according to claim 9, characterized in that, The step of screening the predicted structural data of the free molecular sequences surrounding the core histone and the linker histone as the cell structural data includes: For the third predicted structural data in the at least two predicted structural data, at least two first sequences in the third predicted structural data are determined, the at least two first sequences including the biomolecule sequence and the molecular active substance in the third predicted structural data; and at least two second sequences in the disclosed cell data are determined, the at least two second sequences including the biomolecule sequence and the molecular active substance in the disclosed cell data; Align the first sequence and the second sequence, which represent the same information, and obtain the root mean square deviation value between the third predicted structural data and the publicly available cell data based on the sequence alignment. If the root mean square deviation value is less than a third preset threshold, the third predicted structural data is used as the cell structural data.

13. The method according to claim 7, characterized in that, After obtaining the sequence cut positions corresponding to the at least two cell structure data, the method further includes: Based on the sequence cutting positions corresponding to the at least two cell structure data, at least one sequence cutting position is determined; For a first sequence cut position among the at least one sequence cut positions, determine the number of structural data of the cell structural data having the first sequence cut position, and determine the number of structural data corresponding to each of the at least one sequence cut positions; Based on the number of structural data corresponding to each of the at least one sequence cutting positions, the biological abnormality state of the first organism is predicted.

14. The method according to claim 13, characterized in that, The prediction of the biological abnormality state of the first organism based on the number of structural data corresponding to each of the at least one sequence cut positions includes: The at least one sequence cutting position with the largest number of structural data is used as the site detection information corresponding to the first organism; the site detection information is used to detect the abnormal biological state of the first organism.

15. The method according to claim 13, characterized in that, The method further includes: Obtain a disease cut location dataset, which stores multiple cut location data, and the cut location data is used to characterize the cut locations in the biomolecular sequence when the disease is present; By matching the sequence cutting positions corresponding to the at least two cell structure data with the disease cutting position dataset, the biological abnormal state of the first organism is obtained; Specifically, if the disease cutting location dataset includes the sequence cutting location, a first biological abnormality state is output; or, if the disease cutting location dataset does not include the sequence cutting location, a second biological abnormality state is output.

16. A device for predicting the position of sequence cutting, characterized in that, The device includes: An acquisition module is used to acquire the biomolecular sequence corresponding to the first organism, wherein the biomolecular sequence includes at least two molecular sites; The acquisition module is also used to acquire molecularly active substances, which are used to cleave the biomolecule sequence; The prediction module is used to predict the sequence cut position corresponding to the biomolecule sequence based on the relative positional relationship between the molecular active substance and the biomolecule sequence. The sequence cut position is used to characterize the cut position of the molecular active substance in the at least two molecular sites.

17. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one program, which is loaded and executed by the processor to implement the method for predicting sequence cutting positions as described in any one of claims 1 to 15.

18. A computer-readable storage medium, characterized in that, The storage medium stores at least one program, which is loaded and executed by a processor to implement the method for predicting sequence cutting positions as described in any one of claims 1 to 15.

19. A computer program product, characterized in that, It includes computer instructions that, when executed by a processor, implement the method for predicting sequence cutting positions as described in any one of claims 1 to 15.

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