A screening method, device, equipment and medium of functional oligopeptide collagen

Abstract

The application discloses a functional oligopeptide collagen screening method, device, equipment and medium, relates to the oligopeptide collagen screening technical field, and comprises the following steps: determining a target collagen type of a functional oligopeptide collagen to be screened, performing tripeptide frequency calculation on each amino acid sequence of the target collagen type to obtain corresponding frequency calculation results; screening a target tripeptide that meets tripeptide appearance frequency conditions according to the frequency calculation results; obtaining a target functional fragment corresponding to the target collagen type from a preset protein fragment library; according to a polypeptide generation strategy, the functional oligopeptide collagen to be screened is exhausted to obtain a candidate polypeptide; calculating the frequency sum of the target tripeptide and the target functional fragment of each candidate polypeptide to obtain a target frequency value of each candidate polypeptide, and screening a functional oligopeptide collagen that meets polypeptide screening frequency conditions according to each target frequency value.

Description

Technical Field

[0001] This invention relates to the field of oligopeptide collagen screening technology, and particularly to a method, apparatus, equipment and medium for screening functional oligopeptide collagen. Background Technology

[0002] Collagen is an important protein in the human body, mainly distributed in tissues such as skin, joints, and bones. It accounts for more than 30% of the body's total protein and plays a vital role in maintaining skin elasticity, protecting joints, strengthening blood vessel walls, and promoting bone health. With age and environmental factors, collagen synthesis gradually decreases, leading to problems such as loose skin, joint pain, and osteoporosis. Therefore, supplementing with collagen can improve skin elasticity, alleviate joint discomfort, and enhance bone health, thereby improving overall health. Collagen research primarily employs lengthy experimental methods (spectroscopy, mass spectrometry, biophysics, cell culture, animal experiments, genetic engineering, and tissue engineering, etc.). While this approach yields the most valuable results, it is prohibitively time-consuming and costly. Currently, research on innovative oligopeptide collagen is mainly conducted experimentally, requiring extensive data screening and validation through experiments, which demands significant time and financial investment. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide a method, apparatus, device, and medium for screening functional oligopeptide collagen, which can achieve the screening of functional oligopeptide collagen without the need for manual screening of large amounts of data and experimental verification, thereby reducing time and economic costs. The specific solution is as follows:

[0004] In a first aspect, this application discloses a method for screening functional oligopeptide collagen, comprising:

[0005] The target collagen type of the functional oligopeptide collagen to be screened is determined, and the tripeptide frequency of each amino acid sequence of the target collagen type is calculated to obtain the corresponding frequency calculation results.

[0006] Based on the frequency calculation results, target tripeptides that meet the frequency occurrence criteria are selected.

[0007] Obtain the target functional fragment corresponding to the target collagen type from a preset protein fragment library;

[0008] Exhaustive enumeration of the functional oligopeptides to be screened using the peptide generation strategy to obtain candidate peptides;

[0009] The sum of the frequencies of the target tripeptide and the target functional fragment of each candidate polypeptide is calculated to obtain the target frequency value of each candidate polypeptide, and functional oligopeptide collagen that meets the polypeptide screening frequency conditions is screened according to the target frequency value.

[0010] Optionally, before obtaining the target functional fragment corresponding to the target collagen type from the preset protein fragment library, the method further includes:

[0011] The amino acid sequence information and functional fragments of each collagen under different collagen types are obtained, and the collagen types are bound to the corresponding amino acid sequence information and functional fragments according to the correspondence and stored in a preset protein fragment library.

[0012] Optionally, the step of binding collagen types with corresponding amino acid sequence information and functional fragments according to the correspondence and storing them in a preset protein fragment library includes:

[0013] According to the correspondence, collagen types are bound to corresponding amino acid sequence information and functional fragments to obtain corresponding mapping relationships. Based on the mapping relationships, the collagen types and corresponding amino acid sequence information and functional fragments are subjected to data structuring and image processing, and the image-processed information is stored in a preset protein fragment library.

[0014] Optionally, determining the target collagen type for the functional oligopeptide collagen to be screened, and calculating the tripeptide frequency for each amino acid sequence of the target collagen type to obtain the corresponding frequency calculation results, includes:

[0015] Obtain the current screening request for functional oligopeptide collagen, determine the target collagen type from the screening request, and use a preset programming language script to calculate the tripeptide frequency of each amino acid sequence under the target collagen type to obtain the corresponding frequency calculation results.

[0016] Optionally, the step of filtering target tripeptides that meet the tripeptide occurrence frequency condition based on the frequency calculation result includes:

[0017] Arrange the frequency calculation results in descending order to generate a target result table;

[0018] A preset number of frequency calculation results are sequentially obtained from the target result table as the target frequency calculation results;

[0019] The tripeptide corresponding to the calculated target frequency is taken as the target tripeptide.

[0020] Optionally, after calculating the tripeptide frequency for each amino acid sequence of the target collagen type to obtain the corresponding frequency calculation results, the method further includes:

[0021] Remove the frequency calculation results of the tripeptides glycine-proline-proline, proline-glycine-proline, and proline-proline-glycine in each amino acid sequence to obtain the target frequency calculation results;

[0022] Accordingly, the step of screening target tripeptides that meet the frequency occurrence criteria based on the frequency calculation results includes:

[0023] Based on the target frequency calculation results, target tripeptides that meet the tripeptide occurrence frequency conditions are selected.

[0024] Optionally, the step of exhaustively searching the functional oligopeptides to be screened according to the peptide generation strategy to obtain candidate peptides includes:

[0025] Set up a peptide generation strategy to generate tripeptides into short-chain oligopeptides.

[0026] According to the peptide generation strategy, the functional oligopeptide collagen to be screened is exhaustively enumerated using a preset programming language script to obtain different candidate peptides with different numbers of amino acids.

[0027] The short-chain oligopeptide is a polypeptide composed of no more than 45 amino acids.

[0028] Secondly, this application discloses a screening device for functional oligopeptide collagen, comprising:

[0029] The frequency calculation module is used to determine the target collagen type of the functional oligopeptide collagen to be screened, and to perform tripeptide frequency calculation on each amino acid sequence of the target collagen type to obtain the corresponding frequency calculation results.

[0030] The first screening module is used to screen target tripeptides that meet the tripeptide occurrence frequency conditions based on the frequency calculation results.

[0031] The functional fragment acquisition module is used to acquire the target functional fragment corresponding to the target collagen type from a preset protein fragment library;

[0032] The second screening module is used to exhaustively list the functional oligopeptide collagen to be screened according to the peptide generation strategy in order to obtain candidate peptides.

[0033] The third screening module is used to calculate the sum of the frequencies of the target tripeptide and the target functional fragment of each candidate polypeptide to obtain the target frequency value of each candidate polypeptide, and to screen out functional oligopeptide collagen that meets the polypeptide screening frequency conditions based on the target frequency value.

[0034] Thirdly, this application discloses an electronic device, including:

[0035] Memory is used to store computer programs;

[0036] A processor is configured to execute the computer program to implement the steps of the aforementioned disclosed method for screening functional oligopeptide collagen.

[0037] Fourthly, this application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the aforementioned disclosed method for screening functional oligopeptide collagen.

[0038] As can be seen, this application discloses a method for screening functional oligopeptide collagen, comprising: determining the target collagen protein type of the functional oligopeptide collagen to be screened, and performing tripeptide frequency calculation on each amino acid sequence of the target collagen protein type to obtain the corresponding frequency calculation result; screening target tripeptides that meet the tripeptide occurrence frequency condition according to the frequency calculation result; obtaining target functional fragments corresponding to the target collagen protein type from a preset protein fragment library; exhaustively enumerating the functional oligopeptide collagen to be screened according to a peptide generation strategy to obtain candidate peptides; calculating the sum of the frequencies of the target tripeptide and the target functional fragment of each candidate peptide to obtain the target frequency value of each candidate peptide, and screening functional oligopeptide collagen that meets the peptide screening frequency condition according to each target frequency value. Therefore, by calculating tripeptide frequencies and screening for target tripeptides that meet frequency criteria, it is possible to more accurately identify short sequences that play an important role in this type of collagen. Obtaining corresponding target functional fragments from a pre-defined protein fragment library, considering the known functional regions of collagen, helps in discovering oligopeptides with practical application value. Exhaustively searching for functional oligopeptide collagens to be screened according to peptide generation strategies can cover more potential active peptides, increasing the chances of discovering valuable oligopeptides. Screening for functional oligopeptide collagens that meet peptide screening frequency criteria based on target frequency values ​​can identify the most promising oligopeptides from numerous candidate peptides, reducing the workload of subsequent experiments and research. In this way, a method for screening functional oligopeptide collagens from multiple perspectives (functional fragments, structure) and across the entire range (exhaustive search of all target fragments, frequency statistics) has been developed, forming a systematic, rapid, and efficient method for screening innovative functional oligopeptide collagens. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0040] Figure 1 This is a flowchart of a screening method for functional oligopeptide collagen disclosed in this application;

[0041] Figure 2 This application discloses a functional oligopeptide collagen screening method and system flowchart;

[0042] Figure 3 This is a flowchart of a specific screening method for functional oligopeptide collagen disclosed in this application;

[0043] Figure 4 This is a schematic diagram of the structure of a screening device for functional oligopeptide collagen disclosed in this application;

[0044] Figure 5 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0046] Collagen is an important protein in the human body, mainly distributed in tissues such as skin, joints, and bones. It accounts for more than 30% of the body's total protein and plays a vital role in maintaining skin elasticity, protecting joints, strengthening blood vessel walls, and promoting bone health. With age and environmental factors, collagen synthesis gradually decreases, leading to problems such as loose skin, joint pain, and osteoporosis. Therefore, supplementing with collagen can improve skin elasticity, alleviate joint discomfort, and enhance bone health, thereby improving overall health. Research on collagen primarily employs lengthy experimental methods (spectroscopy, mass spectrometry, biophysics, cell culture, animal experiments, genetic engineering, and tissue engineering, etc.). While this approach yields the most valuable results, it is prohibitively time-consuming and costly.

[0047] Currently, research on innovative oligopeptide collagen is mainly conducted experimentally, requiring manual screening of large amounts of data and protocols, followed by experimental verification. This process is time-consuming and costly. Therefore, computer-based screening methods can accelerate the research of innovative oligopeptide collagen. Existing new technologies, through searching and researching relevant literature, collect functional oligopeptide sequences from animal collagen to construct a collagen oligopeptide database. This database is then used to evaluate and predict existing data in order to screen for novel oligopeptide collagen. However, this approach has limitations. It only evaluates based on the single aspect of effective data, and the screening efficiency is low due to manual analysis. Furthermore, it may result in the omission of a large number of potential oligopeptide protocols.

[0048] Therefore, this invention provides a screening scheme for functional oligopeptide collagen, which can achieve the screening of functional oligopeptide collagen without the need for manual screening of a large amount of data and experimental verification.

[0049] Reference Figure 1 As shown in the figure, an embodiment of the present invention discloses a method for screening functional oligopeptide collagen, comprising:

[0050] Step S11: Determine the target collagen type for the functional oligopeptide collagen to be screened, and perform tripeptide frequency calculation on each amino acid sequence of the target collagen type to obtain the corresponding frequency calculation results.

[0051] In this embodiment, a current screening request for functional oligopeptide collagen is obtained. The target collagen type is determined from the screening request, and a preset programming language script is used to calculate the tripeptide frequency of each amino acid sequence under the target collagen type to obtain the corresponding frequency calculation results. It is understood that since different types of collagen proteins play unique roles in different tissues or physiology, obtaining a current screening request for functional oligopeptide collagen is crucial. For example, if the screening request is to screen collagen present in tissues such as skin, tendons, and bones, providing strength and structural support to these tissues, then the current screening request is determined to be for collagen of type I, type III, and type VI. Therefore, the target collagen type is one or a combination of collagen of type I, type III, and type VI, without specific limitations. After determining the target collagen type, a Python script is used to calculate the tripeptide frequency of each amino acid sequence under the target collagen type to obtain the corresponding frequency calculation results. Here, since the core fragment of collagen is collagen tripeptide, namely Gly-XY, "Gly" refers to glycine, and "X" and "Y" represent other types of amino acids, such as GPH (Glycine-Proline-Hyp, glycine-proline-hydroxyproline). Furthermore, the primary structure of the protoplasmic collagen peptide chain has (Gly-XY)n repeating units. This means that in the primary structure of the protoplasmic collagen peptide chain (i.e., the linear sequence of amino acids), tripeptide combinations such as "glycine-other amino acid-another other amino acid" will repeatedly appear. "n" represents the number of repetitions. Three procollagen peptide chains can intertwine with each other through interchain hydrogen bonds to form a stable triple helix structure, which can improve the strength and stability of collagen. Therefore, a Python script (version 3.7.16) was used to calculate the frequency of each tripeptide for each amino acid sequence of each target collagen type to obtain the corresponding frequency calculation results. The frequency calculation of each tripeptide can be performed using Python dictionaries and loops to execute a direct counting method. The direct counting method involves iterating through each amino acid sequence, combining every three consecutive amino acids as a tripeptide, and using a data structure (such as a hash table or dictionary) to record the occurrence frequency of each tripeptide. The ratio of the occurrences of Gly-XY to the total occurrences is then calculated as the corresponding frequency result. Furthermore, if the data volume is huge and high computational speed is required, parallel computing combined with a sliding window method can be considered, along with the use of relevant high-performance libraries to obtain the frequency calculation results. It should be noted that other algorithms can also be used for the frequency calculation process; no specific limitations are imposed.

[0052] Step S12: Based on the frequency calculation results, select target tripeptides that meet the tripeptide occurrence frequency conditions.

[0053] In this embodiment, the frequency calculation results are arranged in descending order to generate a target result table; a preset number of frequency calculation results are sequentially obtained from the target result table as target frequency calculation results; and the tripeptide corresponding to the target frequency calculation result is taken as the target tripeptide. It can be understood that the tripeptide frequency calculation results for each amino acid sequence in the target collagen type are arranged in descending order of frequency to form a target result table. Then, 10 frequency calculation results are selected from top to bottom from this target result table as target frequency calculation results, and the tripeptides corresponding to these 10 target frequency calculation results are taken as target tripeptides.

[0054] In collagen, different tripeptides may have varying degrees of importance and frequency. Selecting the top 10 most frequent target tripeptides better represents common and important structural patterns in collagen sequences. These high-frequency target tripeptides likely play a crucial role in maintaining collagen's structure and function. Specific tripeptide sequences in collagen are closely related to their formation of a stable triple helix structure and their biological functions. High-frequency target tripeptides are more likely to be important components of a stable triple helix structure, significantly impacting collagen's strength and stability. Considering all possible tripeptides in the collagen sequence would lead to excessive computational burden. Selecting the top 10 target tripeptides can significantly reduce the computational burden of subsequent analysis and processing without excessive information loss, thus improving computational efficiency. These high-frequency tripeptides may be related to specific collagen functions, interactions with other molecules, or specific biological processes. Focusing on them helps to gain a deeper understanding of collagen's functional characteristics and mechanisms of action. In large datasets, high-frequency patterns are often more regular and identifiable. Selecting the top 10 most frequent target tripeptides helps to discover and analyze potential patterns and regularities in collagen sequences, providing valuable clues for further research.

[0055] Step S13: Obtain the target functional fragment corresponding to the target collagen type from the preset protein fragment library.

[0056] In this embodiment, the target collagen type (Collagen N type) and its corresponding relationship are used to find the corresponding target functional fragment (i.e., sequence fragment) from the RIMP (Readline Innovative raw material platform, an AI-assisted platform for the innovative research and development of new cosmetic raw materials) protein fragment library. It is important to note that the RIMP protein fragment library is pre-constructed. Specifically, before obtaining the target functional fragment corresponding to the target collagen type from the pre-constructed protein fragment library, the process further includes: obtaining the amino acid sequence information and functional fragments of each collagen type, and binding the collagen type with the corresponding amino acid sequence information and functional fragments according to the correspondence and storing them in the pre-constructed protein fragment library. (Refer to...) Figure 2 As shown, at the source data layer, extensive searches and statistical analyses were conducted using relevant literature and public databases such as Unprot and PDB bank (Protein Data Bank) to summarize all sequence information for 29 types of collagen, from Collagen I to Collagen XXIX, totaling 80 different amino acid sequences, as shown in Table 1.

[0057] Table 1

[0058]

[0059] Table 2 provides a brief overview of the protein functions of some Collegen proteins listed in Table 1.

[0060] Table 2

[0061]

[0062] It is evident that different types of collagen play different functional roles in different tissues. Therefore, it is necessary to pre-collect and store the amino acid sequence information and functional fragments of various collagen types, including 25 different functional fragments. Then, according to the correspondence, collagen types are linked to their corresponding amino acid sequence information and functional fragments and stored in the RIMP-protein fragment library, thus completing the construction of the RIMP-protein fragment library. For example... Figure 2 As shown, the RIMP-protein fragment library is located in the database layer and is used to provide amino acid sequence information, efficacy information (functional fragments), and target information.

[0063] In this embodiment, collagen types are bound to their corresponding amino acid sequence information and functional fragments according to a correspondence to obtain a mapping relationship. Based on this mapping relationship, the collagen types, their corresponding amino acid sequence information, and functional fragments are subjected to data structuring and image processing, and the image-processed information is stored in a preset protein fragment library. It can be understood that the specific process of constructing the RIMP-protein fragment library is as follows: after obtaining each collagen type and its corresponding amino acid sequence information and functional fragments, Python is used to exhaustively enumerate every amino acid sequence in each collagen triple helix structure. The exhaustive enumeration scheme includes tripeptide-peptide (no more than 45 amino acids). This exhaustive enumeration ensures that all possible tripeptide-peptide fragments in the collagen triple helix structure are covered. This avoids missing some potentially important fragments and provides a more comprehensive understanding of the structural characteristics of collagen. The large amount of fragment data generated by the exhaustive enumeration helps to discover hidden patterns and regularities. Some specific tripeptide or peptide patterns may be closely related to the function, structure, or interaction with other molecules of collagen. By analyzing these exhaustively enumerated peptide fragments, patterns can be better identified and understood, leading to the inference of potential functional regions. After generating a large number of candidate fragments, further comparison and screening can be performed. For example, comparisons can be made with known sequence fragments with specific functions to discover new fragments with similar functions. This facilitates systematic analysis and comparison of sequences from different collagen proteins. This comprehensive perspective helps to discover the commonalities and differences among various collagen proteins, providing a deeper understanding of the overall characteristics of the collagen family. Collagen's structure and function are complex; single sequence information or known functional fragments may not fully reveal all its properties. Exhaustive enumeration can provide richer data to address this complexity and uncover more potential information. While directly obtaining sequence and functional fragment information is fundamental to library construction, the exhaustive enumeration step further deepens our understanding of collagen, providing more comprehensive and in-depth data support for subsequent research and applications. Therefore, the exhaustively enumerated peptide fragment data is bound to functional fragments according to correspondences to obtain corresponding mapping relationships. Based on these mapping relationships, data structuring and visualization processing are performed on collagen types and their corresponding peptide and functional fragment data.

[0064] In this embodiment, the specific processing steps for data structuring are as follows: First, select a suitable data structure to represent the sequence information, functional fragment information, and exhaustively obtained tripeptide-to-peptide fragment data of collagen. For example, a database (such as a relational database or a NoSQL database) can be used to store and manage this structured data. Relational databases organize data through tables, facilitating association and querying; NoSQL databases (such as document databases, key-value databases, etc.) are more suitable for processing unstructured or semi-structured data. Second, define data fields and relationships, clarifying the information contained in each data element (such as sequence name, amino acid sequence, functional description, etc.) and the relationships between different data (such as a certain tripeptide belonging to a specific collagen protein). Third, ensure data consistency and integrity by setting constraints, validation rules, etc., to guarantee data quality.

[0065] In this embodiment, the specific processing steps for image processing are as follows: Structured data is converted into images using professional visualization tools or libraries. For example, data visualization libraries in Python (such as Matplotlib, Seaborn, etc.) can be used to create charts to display information such as the structural features and tripeptide frequency distribution of collagen. Depending on specific needs, different types of charts such as bar charts, line charts, pie charts, and heatmaps can be created to more intuitively present the patterns and trends of the data. For the visualization needs of collagen's three-dimensional structure, professional molecular visualization software (such as PyMol, Chimera, etc.) can be used to convert its amino acid sequence into a three-dimensional structural image, which can then be rendered and displayed.

[0066] Finally, establish a data management system or framework to integrate the structured storage and visual display of data. This can be achieved by writing code to implement functions such as data reading, processing, visualization, and interactive operations. Design a user interface (such as a web interface or desktop application interface) to allow users to easily access and browse structured data and corresponding visual results. Users can query, filter, and analyze data through the interface and view corresponding visualizations or images. Ensure data synchronization and consistency; when data changes (such as adding, modifying, or deleting collagen sequences), the corresponding visual results should be updated promptly. Version control can be used to manage changes to data and code to track and trace back data and visualization results at different stages, unifying the management of these structured and visual data for download and use via Python scripts (version: 3.7.16) or a web server (in this invention, the web interface of the RIMP system).

[0067] Step S14: Exhaustively list the functional oligopeptides to be screened according to the peptide generation strategy to obtain candidate peptides.

[0068] In this embodiment, based on the identified target functional fragment and the sequence length requirements of the functional oligopeptide collagen to be screened, a full sequence exhaustive search is performed on the functional oligopeptide collagen to be screened to obtain candidate peptides. Specifically, a peptide generation strategy is set to generate tripeptides into short-chain oligopeptides. Following the peptide generation strategy and using a preset programming language script, the functional oligopeptide collagen to be screened is exhaustively searched to obtain different candidate peptides with different numbers of amino acids. The short-chain oligopeptides are peptides composed of no more than 45 amino acids. For example, for Collagen M-type collagen with a sequence length of L, if the target sequence length to be studied is 3≤N≤45, then a full sequence exhaustive search is performed according to the peptide generation strategy to obtain L-N+1 candidate sequences, i.e., candidate peptides.

[0069] like Figure 2 As shown, let's assume we're studying Collagen X type collagen. First, we analyze each sequence of Collagen X using a Python script (version 3.7.16) to calculate the frequency of various tripeptides. Assume the top 10 most frequent target tripeptides are "GLY-PRO-HYP", "ALA-ARG-GLY", "PRO-LEU-GLY", etc. Then, we identify the corresponding functional fragments for Collagen X from the RIMP protein fragment library. Assume there are 5 different functional fragments: "FS1", "FS2", "FS3", "FS4", and "FS5". Next, assuming a Collagen X sequence is 100 amino acids long, and we want to study sequences with a length of at least 15 amino acids, we perform a full sequence exhaustive search, resulting in 100 - 15 + 1 = 86 candidate sequences.

[0070] In this embodiment, the specific process of exhaustive screening based on the peptide generation strategy is as follows: First, the complete amino acid sequence information of the functional oligopeptide collagen to be screened is listed as follows:

[0071] METGLYSERPROVALGLNHISARGLEUPHEASNTHR.

[0072] Then, according to the peptide generation strategy requirements, the peptide to be generated is a tripeptide of no more than 45 amino acids. A partial exhaustive list of tripeptides is shown below:

[0073] A partial exhaustive list of tripeptides:

[0074] "MET", "ETG", "TGL", "GLY", "LYS", "YSE", ..., "THR".

[0075] As can be seen, in tripeptide exhaustive search, the sequence exhaustive search includes all cases. Only one amino acid is moved to perform exhaustive search for all cases, and so on, up to peptides approaching 45 amino acids. The exhaustive search results are statistically analyzed to obtain candidate peptides.

[0076] In practical applications, because collagen sequences are typically quite long, the number of short peptides that can be exhaustively listed is extremely large. This necessitates the use of efficient algorithms and computer programs to process and analyze this data.

[0077] Step S15: Calculate the sum of the frequencies of the target tripeptide and the target functional fragment of each candidate polypeptide to obtain the target frequency value of each candidate polypeptide, and screen out functional oligopeptide collagen that meets the polypeptide screening frequency conditions based on the target frequency value.

[0078] In this embodiment, to study Collagen V, the top 10 most frequent target tripeptides in each sequence are first calculated, and then the corresponding functional fragments for Collagen V are identified from the RIMP protein fragment library. Next, for example, a Collagen V sequence of 50 amino acids, if the goal is to study target sequences of 10 amino acids or longer, a full sequence exhaustive search is performed, resulting in, for example, 41 candidate peptides. Finally, the sum of the frequencies of each candidate peptide among the previously selected "Top 10 target tripeptides" and "functional fragments" is calculated, and the top 10 sequences with the highest total frequency are selected for subsequent research.

[0079] For these 86 candidate peptides, the sum of the frequencies of the previously selected "Top 10 target tripeptides" and "functional fragments" in each candidate peptide is calculated. For example, in the first candidate peptide, "GLY-PRO-HYP" appears 3 times, "ALA-ARG-GLY" appears 2 times, "PRO-LEU-GLY" appears once, functional fragment 1 appears once, and functional fragment 2 appears twice. Therefore, the sum of the frequencies of the "Top 10 tripeptides" and "functional fragments" in this candidate peptide is 3+2+1+1+2=9. The sum of the frequencies of all 86 candidate peptides is calculated in the same way, and then these sums are sorted. The top 10 sequences with the highest sum frequencies are selected as the final set of screening sequences. Assuming the top 10 functional oligopeptide collagens selected are amino acids 12-26, 20-34, 35-50, 5-20, 40-55, 60-75, 7-21, 30-45, 18-32, and 50-65, these selected functional oligopeptide collagens will be used for further in-depth research and analysis.

[0080] The selected functional oligopeptide collagens were evaluated and validated through relevant synthesis, sequencing, and efficacy assessment. Based on the Collagen III sequence information (Uniprot ID: P02461, human fragment) provided in RIMP, a large-scale exhaustive search targeting tripeptides was conducted from the sequence information (AA=1586) using Python. The frequency of each different tripeptide was calculated, and the top 10 sequences were selected for sequence analysis. The tripeptide sequence with the highest frequency was GPH (glycine-proline-hydroxyproline). Here, hydroxyproline is the result of proline hydroxylation and is not a natural amino acid, represented as P or H. By synthesizing the sequence of this result, a tripeptide product (Gly-Pro-Hyp) was obtained. Because small peptides (mainly dipeptides and tripeptides) are present in the small intestine as carriers, the product can be fully absorbed by the intestine and its absorption can be accelerated. Therefore, two rat small intestinal absorption experiments were conducted on the tripeptide product (Gly-Pro-Hyp) and ordinary collagen (Pro-Hyp). After a 6-hour follow-up experiment, the results were as follows: Experiment 1: The absorption rate of Gly-Pro-Hyp was 1824.8 ng / ml (1500-2000 ng / ml), while the absorption rate of ordinary collagen (Pro-Hyp) was 88.3 ng / ml (75-100 ng / ml); Experiment 2: The absorption rate of Gly-Pro-Hyp was 2065.5 ng / ml (2000-2200 ng / ml), while the absorption rate of ordinary collagen (Pro-Hyp) was 433.9 ng / ml (400-440 ng / ml). The absorption rate of the tripeptide product (Gly-Pro-Hyp) is 5-20 times that of ordinary collagen (Pro-Hyp), with an absorption rate of ng / ml. This indicates that this screening method has successfully identified oligopeptide collagen with good activity. Furthermore, it is evident that in the top 10 frequency statistics of each type of collagen screened using the method of this invention, the most frequently occurring tripeptide fragment is primarily composed of G, P, and P, consistent with the core components of collagen, providing factual evidence.

[0081] As can be seen, this application discloses a method for screening functional oligopeptide collagen, comprising: determining the target collagen protein type of the functional oligopeptide collagen to be screened, and performing tripeptide frequency calculation on each amino acid sequence of the target collagen protein type to obtain the corresponding frequency calculation result; screening target tripeptides that meet the tripeptide occurrence frequency condition according to the frequency calculation result; obtaining target functional fragments corresponding to the target collagen protein type from a preset protein fragment library; exhaustively enumerating the functional oligopeptide collagen to be screened according to a peptide generation strategy to obtain candidate peptides; calculating the sum of the frequencies of the target tripeptide and the target functional fragment of each candidate peptide to obtain the target frequency value of each candidate peptide, and screening functional oligopeptide collagen that meets the peptide screening frequency condition according to each target frequency value. Therefore, by calculating tripeptide frequencies and screening for target tripeptides that meet frequency criteria, it is possible to more accurately identify short sequences that play an important role in this type of collagen. Obtaining corresponding target functional fragments from a pre-defined protein fragment library, considering the known functional regions of collagen, helps in discovering oligopeptides with practical application value. Exhaustively searching for functional oligopeptide collagens to be screened according to peptide generation strategies can cover more potential active peptides, increasing the chances of discovering valuable oligopeptides. Screening for functional oligopeptide collagens that meet peptide screening frequency criteria based on target frequency values ​​can identify the most promising oligopeptides from numerous candidate peptides, reducing the workload of subsequent experiments and research. In this way, a method for screening functional oligopeptide collagens from multiple perspectives (functional fragments, structure) and across the entire range (exhaustive search of all target fragments, frequency statistics) has been developed, forming a systematic, rapid, and efficient method for screening innovative functional oligopeptide collagens.

[0082] Reference Figure 3 As shown, this embodiment of the invention discloses a specific method for screening functional oligopeptide collagen. Compared with the previous embodiment, this embodiment further explains and optimizes the technical solution. Specifically:

[0083] Step S21: Obtain the current screening request for functional oligopeptide collagen, determine the target collagen type from the screening request, and use a preset programming language script to calculate the tripeptide frequency of each amino acid sequence under the target collagen type to obtain the corresponding frequency calculation results.

[0084] Step S22: Remove the frequency calculation results of the tripeptides glycine-proline-proline, proline-glycine-proline, and proline-proline-glycine in each amino acid sequence to obtain the target frequency calculation results.

[0085] In this embodiment, after calculating the tripeptide frequency of each amino acid sequence, because this invention requires obtaining functional oligopeptide collagen, specific peptides need to be screened and excluded. Specifically, tripeptides such as GPP, PGP, and PPG do not play a key role in the structure or function of collagen, or their presence may interfere with subsequent analysis and research. Specifically, the function and properties of collagen are mainly determined by its specific amino acid sequence and structure. Different tripeptide sequences may have different biological activities or interactions with other molecules. Tripeptides such as GPP, PGP, and PPG do not possess the specific function of interest, or their high frequency may mask information about other more meaningful tripeptides. Furthermore, researchers, based on previous research experience or knowledge in related fields, may find that these tripeptides are not important in specific experiments or analyses, or they may be removed to focus on other more relevant tripeptides. For example, some studies may find that other specific tripeptides are closely related to a certain function of collagen or the binding of other biomolecules, while GPP, PGP, and PPG do not participate in these important interactions. By removing these, the frequency and distribution of tripeptides more directly related to the research objective can be observed and analyzed more clearly, thus enabling better screening of sequence fragments with specific functions or significant meanings. Therefore, tripeptides containing G, P, and P simultaneously are removed from the obtained frequency calculation results; that is, the frequency calculation results of GPP, PGP, and PPG tripeptides appearing in the amino acid sequence are all removed to obtain the target frequency calculation results.

[0086] Step S23: Based on the target frequency calculation results, select target tripeptides that meet the tripeptide occurrence frequency conditions.

[0087] In this embodiment, compared to directly screening the target tripeptide from the frequency calculation results in the previous embodiment, this embodiment further screens the target tripeptide from the target frequency calculation results after removing the frequency calculation results of GPP, PGP, and PPG tripeptides. This avoids GPP, PGP, and PPG tripeptides becoming target tripeptides and affecting the subsequent screening of functional oligopeptide collagen. The screening process is detailed in the aforementioned disclosed embodiments and will not be repeated here.

[0088] Step S24: Obtain the target functional fragment corresponding to the target collagen type from the preset protein fragment library.

[0089] Step S25: Exhaustively list the functional oligopeptides to be screened collagen according to the peptide generation strategy to obtain candidate peptides.

[0090] Step S26: Calculate the sum of the frequencies of the target tripeptide and the target functional fragment of each candidate polypeptide to obtain the target frequency value of each candidate polypeptide, and screen out functional oligopeptide collagen that meets the polypeptide screening frequency conditions based on the target frequency value.

[0091] For more detailed processing procedures in steps S21, S23, S24, S25, and S26, please refer to the aforementioned disclosed embodiments; they will not be repeated here.

[0092] It is evident that removing specific tripeptides (GPP, PGP, PPG) can reduce interference in the data, allowing subsequent tripeptide frequency analysis to focus more on other potentially more important or functionally relevant tripeptides. Selecting the top 10 most frequent tripeptides and combining them with functional fragment analysis can highlight those relatively more important or functionally specific parts of the sequence. This helps to more accurately identify key sequences related to the Collagen N type under study. Exhaustive full-sequence enumeration of target sequences of a specific length, calculating the sum of the frequencies of the "Top 10 tripeptides" and "functional fragments," and then selecting the top 10 candidate sequences helps to clarify the scope of key sequences in the study, reducing unnecessary complexity and workload. Through this targeted screening and analysis method, a deeper understanding of the sequence characteristics of Collagen N type, as well as the roles and distribution of specific tripeptides and functional fragments within it, can be achieved. Identifying frequently occurring tripeptides and functional fragments may reveal potential associations between them and the structure, function, or other biological properties of collagen, providing valuable clues for further research on the properties, mechanisms of action, or related applications of collagen. By progressively screening and focusing on key sequences, indiscriminate analysis of all possible sequences is avoided, thus saving computational resources and time. Based on the screening of key sequences, subsequent experiments or analyses can be conducted more specifically, improving the efficiency and effectiveness of the research and making it easier to obtain meaningful results. The results obtained by this method can be compared and verified with existing research in the field or known knowledge about collagen, further deepening the understanding of collagen and potentially uncovering new insights or supplementing the existing knowledge system.

[0093] Reference Figure 4 As shown, the present invention also discloses a screening device for functional oligopeptide collagen, comprising:

[0094] The frequency calculation module 11 is used to determine the target collagen type of the functional oligopeptide collagen to be screened, and to perform tripeptide frequency calculation on each amino acid sequence of the target collagen type to obtain the corresponding frequency calculation results.

[0095] The first screening module 12 is used to screen target tripeptides that meet the tripeptide occurrence frequency conditions based on the frequency calculation results.

[0096] The functional fragment acquisition module 13 is used to acquire the target functional fragment corresponding to the target collagen type from a preset protein fragment library;

[0097] The second screening module 14 is used to exhaustively list the functional oligopeptide collagen to be screened according to the peptide generation strategy in order to obtain candidate peptides.

[0098] The third screening module 15 is used to calculate the sum of the frequencies of the target tripeptide and the target functional fragment of each candidate polypeptide to obtain the target frequency value of each candidate polypeptide, and to screen out functional oligopeptide collagen that meets the polypeptide screening frequency conditions based on the target frequency value.

[0099] As can be seen, this application discloses a method for determining the target collagen type for screening functional oligopeptide collagen, calculating the tripeptide frequency for each amino acid sequence of the target collagen type to obtain the corresponding frequency calculation results, screening target tripeptides that meet the tripeptide occurrence frequency conditions based on the frequency calculation results, obtaining target functional fragments corresponding to the target collagen type from a preset protein fragment library, exhaustively searching the functional oligopeptide collagen to be screened according to a peptide generation strategy to obtain candidate peptides, calculating the sum of the frequencies of the target tripeptide and the target functional fragment for each candidate peptide to obtain the target frequency value of each candidate peptide, and screening functional oligopeptide collagen that meets the peptide screening frequency conditions based on each target frequency value. Therefore, by calculating the tripeptide frequency and screening for target tripeptides that meet the frequency conditions, short sequences that play an important role in the collagen type can be found more accurately. Obtaining the corresponding target functional fragments from a preset protein fragment library considers the known functional regions of collagen, which helps to discover oligopeptides with practical application value. Exhaustively searching the functional oligopeptide collagen to be screened according to a peptide generation strategy can cover more possible potential active peptides and increase the chance of discovering valuable oligopeptides. Screening for functional oligopeptides that meet the target frequency criteria for peptide screening allows for the identification of the most promising oligopeptides from a large pool of candidate peptides, reducing the workload of subsequent experiments and research. This provides a method for screening functional oligopeptides in collagen from multiple perspectives (functional fragments, structure) and across a comprehensive range (exhaustive enumeration of all target fragments, frequency statistics), forming a systematic, rapid, and efficient approach to screening for innovative functional oligopeptides.

[0100] Furthermore, embodiments of this application also disclose an electronic device, Figure 5 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0101] Figure 5 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the screening method for functional oligopeptide collagen disclosed in any of the foregoing embodiments. Alternatively, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0102] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0103] The processor 21 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 21 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 21 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 21 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 21 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0104] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0105] The operating system 221 manages and controls the various hardware devices and computer programs 222 on the electronic device 20 to enable the processor 21 to perform calculations and processing on the massive amounts of data 223 in the memory 22. It can be Windows Server, Netware, Unix, Linux, etc. The computer program 222, in addition to including a computer program capable of performing the functional oligopeptide collagen screening method disclosed in any of the foregoing embodiments executed by the electronic device 20, may further include computer programs capable of performing other specific tasks. The data 223 may include data received by the electronic device from external devices, as well as data collected by its own input / output interface 25.

[0106] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned method for screening functional oligopeptide collagen. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0107] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0108] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application. The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly in hardware, software modules executed by a processor, or a combination of both. The software module may be located in random access memory (RAM), memory, read-only memory (ROM), electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, removable disks, CD-ROMs (Compact Disc-Read Only Memory), or any other form of storage medium known in the art.

[0109] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0110] The solution provided by the present invention has been described in detail above. Specific examples have been used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for screening functional oligopeptide collagen, characterized in that, include: The target collagen type of the functional oligopeptide collagen to be screened is determined, and the tripeptide frequency of each amino acid sequence of the target collagen type is calculated to obtain the corresponding frequency calculation results. Based on the frequency calculation results, target tripeptides that meet the frequency occurrence criteria are selected. Obtain the target functional fragment corresponding to the target collagen type from a preset protein fragment library; Exhaustive enumeration of the functional oligopeptides to be screened using the peptide generation strategy to obtain candidate peptides; The sum of the frequencies of the target tripeptide and the target functional fragment of each candidate polypeptide is calculated to obtain the target frequency value of each candidate polypeptide, and functional oligopeptide collagen that meets the polypeptide screening frequency conditions is screened according to the target frequency value.

2. The screening method of functional oligopeptides collagen according to claim 1, characterized in that, Before obtaining the target functional fragment corresponding to the target collagen type from the preset protein fragment library, the method further includes: The amino acid sequence information and functional fragments of each collagen under different collagen types are obtained, and the collagen types are bound to the corresponding amino acid sequence information and functional fragments according to the correspondence and stored in a preset protein fragment library.

3. The screening method of functional oligopeptides collagen according to claim 2, characterized in that, The step of binding collagen types with corresponding amino acid sequence information and functional fragments according to the correspondence and storing them in a preset protein fragment library includes: According to the correspondence, collagen types are bound to corresponding amino acid sequence information and functional fragments to obtain corresponding mapping relationships. Based on the mapping relationships, the collagen types and corresponding amino acid sequence information and functional fragments are subjected to data structuring and image processing, and the image-processed information is stored in a preset protein fragment library.

4. The method of screening for functional oligopeptides collagen according to claim 1, wherein The process of determining the target collagen type for the functional oligopeptide collagen to be screened, and calculating the tripeptide frequency for each amino acid sequence of the target collagen type to obtain the corresponding frequency calculation results, includes: Obtain the current screening request for functional oligopeptide collagen, determine the target collagen type from the screening request, and use a preset programming language script to calculate the tripeptide frequency of each amino acid sequence under the target collagen type to obtain the corresponding frequency calculation results.

5. The method of screening for functional oligopeptides collagen according to claim 4, wherein The step of screening target tripeptides that meet the frequency occurrence criteria based on the frequency calculation results includes: Arrange the frequency calculation results in descending order to generate a target result table; A preset number of frequency calculation results are sequentially obtained from the target result table as the target frequency calculation results; The tripeptide corresponding to the calculated target frequency is taken as the target tripeptide.

6. The method of screening for functional oligopeptides collagen according to claim 4, wherein After calculating the tripeptide frequency for each amino acid sequence of the target collagen type to obtain the corresponding frequency calculation results, the method further includes: Remove the frequency calculation results of the tripeptides glycine-proline-proline, proline-glycine-proline, and proline-proline-glycine in each amino acid sequence to obtain the target frequency calculation results; Accordingly, the step of screening target tripeptides that meet the frequency occurrence criteria based on the frequency calculation results includes: Based on the target frequency calculation results, target tripeptides that meet the tripeptide occurrence frequency conditions are selected.

7. The method of screening for functional oligopeptides collagen according to claim 1, wherein The process of exhaustively searching the functional oligopeptides to be screened according to the peptide generation strategy to obtain candidate peptides includes: Set up a peptide generation strategy to generate tripeptides into short-chain oligopeptides. According to the peptide generation strategy, the functional oligopeptide collagen to be screened is exhaustively enumerated using a preset programming language script to obtain different candidate peptides with different numbers of amino acids. The short-chain oligopeptide is a polypeptide composed of no more than 45 amino acids.

8. A screening device for functional oligopeptides collagen, characterized by, include: The frequency calculation module is used to determine the target collagen type of the functional oligopeptide collagen to be screened, and to perform tripeptide frequency calculation on each amino acid sequence of the target collagen type to obtain the corresponding frequency calculation results. The first screening module is used to screen target tripeptides that meet the tripeptide occurrence frequency conditions based on the frequency calculation results. The functional fragment acquisition module is used to acquire the target functional fragment corresponding to the target collagen type from a preset protein fragment library; The second screening module is used to exhaustively list the functional oligopeptide collagen to be screened according to the peptide generation strategy in order to obtain candidate peptides. The third screening module is used to calculate the sum of the frequencies of the target tripeptide and the target functional fragment of each candidate polypeptide to obtain the target frequency value of each candidate polypeptide, and to screen out functional oligopeptide collagen that meets the polypeptide screening frequency conditions based on the target frequency value.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the screening method for functional oligopeptide collagen as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store a computer program; wherein, when executed by a processor, the computer program implements the steps of the screening method for functional oligopeptide collagen as described in any one of claims 1 to 7.

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