37 results about "Biocomputer" patented technology

The invention relates to the technical field of biological computing, in particular to a single-cell Hi-C atlas interpolation method and device, and the method comprises the steps: obtaining single-cell sequencing library data and a reference genome, determining the position of the sequencing data in the genome, and carrying out the statistics of the interaction frequency to obtain a Hi-C contact matrix; performing interpolation on the Hi-C contact matrix of any chromatin of the single cell according to similarity of adjacent nodes to obtain an adjacent node interpolation map of the single chromatin of the single cell; calculating an interpolation map of each chromatin of each cell, performing representation learning by taking the interpolation map as an adjacent matrix, splicing vectors to obtain a cell pre-embedded vector, and performing dimensionality reduction to obtain a whole cell embedded vector matrix; a cellular level similarity matrix is calculated by using a single cell embedded vector matrix and converted into a Markov probability transfer matrix, and the Markov probability transfer matrix is used for correcting an adjacent node interpolation map to obtain a Hi-C interpolation map, so that the interpolation accuracy is improved.

A biological computing platform may include a multielectrode array (MEA) connected to a computing device. The MEA may include a 2D grid of excitation sites, biological neurons disposed on the MEA, a processing device to apply a plurality of impulses at excitation sites having coordinates on the 2D grid, and one or more sensors to measure electrical signals output by one or more of the biological neurons at coordinates of the 2D grid, wherein the processing device is to receive the electrical signals from the one or more sensors and generate a representation of the electrical signals. The computing device may be programmed to receive a digital input signal, convert the digital input signal into instructions for the plurality of impulses, send the instructions to the MEA, receive the representation of the electrical signals from the MEA, and process the representation of the electrical signals.

The invention provides an mRNA sequence optimization method and device, an mRNA molecule, a pharmaceutical composition and application, and relates to the technical field of artificial intelligence, in particular to the technical field of biological calculation and the like. The mRNA sequence optimization method comprises: obtaining a first mRNA sequence for synthesizing a target protein, the first mRNA sequence comprising a 5'untranslated region sequence and a coding region sequence; and adjusting the 5'untranslated region sequence and the coding region sequence with the goal of maximizing a first score of the first mRNA sequence to obtain an optimized second mRNA sequence for synthesizing the target protein, the first score reflecting at least one of the following indicators of the first mRNA sequence: translation initiation efficiency, codon adaptability index and minimum free energy.

The invention relates to the technical field of biological computing, in particular to a hierarchical TADs structural domain identification method and system based on interpretable deep learning, and the method comprises the steps: obtaining a Hi-C contact matrix, extracting basic TADs structural domain boundary points in the Hi-C contact matrix, and generating each hierarchy of candidate hierarchical TADs structural domains according to the boundary points and the maximum TADs structural domain limiting condition. And then training a deep learning model by taking the position relationship between any two chromatin fragments in the Hi-C contact matrix and all candidate hierarchical TADs structural domains and the genome distance between the two fragments as input and taking the interaction frequency between the two fragments as output. And then explaining the deep learning model, and screening out the most significant hierarchical TADs structural domain according to the contribution degree of each candidate hierarchical TADs structural domain to the whole contact frequency. The process is helpful to quantify the importance of each TADs structural domain on the whole Hi-C contact matrix, and provides an important basis for genome three-dimensional structure research and the like.

The invention belongs to the technical field of biological computing, and relates to a TADs structural domain prediction method and system based on attribute graph node clustering. The method comprises the following steps: comparing Hi-C sequencing data with a reference genome, constructing and preprocessing a Hi-C contact matrix, constructing a chromatin attribute graph model, taking chromatin equal-length segments as graph nodes, taking interaction among the nodes as edges, and splicing node attributes by A / B compartment structures and DNA sequence features; the node attribute values serve as new nodes to be inserted into the graph, node amplification is carried out, and an extended attribute graph is formed; performing node clustering on the extended attribute graph, and dividing closely connected structural nodes into the same cluster to serve as a preliminary TADs structural domain; and according to biological prior knowledge, carrying out continuity and size constraint processing on the preliminary TADs, and finally obtaining a predicted TADs structural domain. According to the method, various characteristic information of chromatin is fused through a graph model, and biological priori knowledge is combined, so that the TADs structural domain is effectively predicted.

The present disclosure provides a polypeptide generation method, device, electronic equipment and storage medium, relates to the technical field of artificial intelligence, in particular to biological computing technology. The method comprises the following steps: obtaining a protein target and a reference polypeptide corresponding to the protein target, wherein the protein target is a protein causing pathological changes; generating a candidate polypeptide corresponding to the reference polypeptide; and inputting the protein target and the candidate polypeptide into an affinity evaluation module for screening to obtain a target polypeptide, wherein the affinity evaluation module comprises at least two levels of evaluation sub-modules arranged in a preset evaluation order, and the later the evaluation sub-module in the evaluation order, the higher the accuracy of the evaluation sub-module. The present disclosure can balance the accuracy and efficiency, and improve the "performance-price ratio" of the polypeptide drug design process.

The invention provides a bicyclic peptide generation and model training method and device, equipment and a medium, and relates to the technical field of artificial intelligence, in particular to the technical fields of deep learning, biological calculation and the like. According to the implementation scheme, a target protein sequence and an initial polypeptide sequence are obtained; inputting the target protein sequence and the initial polypeptide sequence into a molecular structure prediction model to obtain at least one confidence score of an initial polypeptide-protein compound structure output by the molecular structure prediction model; and based on the at least one confidence score of the initial polypeptide-protein complex structure, performing at least one update on the initial polypeptide sequence to obtain the target bicyclopeptide, thereby improving the accuracy and stability of the target bicyclopeptide by gradually improving the polypeptide structure, and improving the generation efficiency of the bicyclopeptide by automatically performing the whole iteration process.

The application discloses a DNA nanocage probe for leukemia cell multi-specific membrane protein logic circuit analysis, a kit and a method thereof, and realizes in-situ imaging analysis on three target membrane proteins by using a probe of a cross-section octahedral DNA nanocage main frame, stable structure, and designing a three-specific recognition biological computing DNA molecule logic gate based on a nucleic acid aptamer (Sgc4f, TC01, Sgc8c), which provides a new tool and idea for early diagnosis of tumors and biomedical application of the DNA molecule logic gate in a complex cell system.

The invention provides an RNA representation obtaining method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence and biological information, in particular to the technical field of biological computing. According to the specific implementation scheme, the method comprises the following steps: obtaining a structural discretization expression of RNA according to structural information of the RNA; obtaining sequence discretization representation of the RNA according to the sequence information of the RNA; and fusing the structure discretization expression and the sequence discretization expression of the RNA to obtain the target expression of the RNA.

The application discloses a dopamine transporter dynamics mechanism-based mental stimulant addiction risk prediction method, and belongs to the field of computer-aided drug design and biological computing simulation. The method comprises the following steps: firstly, preparing a conformation and constructing a drug-target-membrane environment; then, performing classical molecular dynamics (cMD) simulation and binding affinity evaluation; then, extracting a dissociation energy barrier by using stretching molecular dynamics (SMD); then, extracting a binding path based on a ligand binding-parallel cascade molecular dynamics (LB-PaCS-MD) and a Markov state model; and finally, comprehensively evaluating addiction and screening drugs. By using the method, a dynamic dissociation / binding path can be accurately mapped on a full-atom level, and a residence time parameter can be calculated, so as to guide accurate development of low-addiction mental drugs.

The present disclosure provides a molecule generation method and device, electronic equipment and storage medium, and relates to the fields of artificial intelligence such as biological computing and deep learning. The method can include: obtaining a first molecule to be processed, and obtaining a synthesis route of the first molecule as an original synthesis route; modifying the original synthesis route to obtain a target synthesis route; and generating a second molecule required according to the target synthesis route. The scheme disclosed in the present disclosure can improve the generation efficiency of molecules and the quality of generated molecules.

The application belongs to the technical field of biological computing, and discloses a ternary addition system and a computing method based on DNA strand displacement, which comprises an input unit, a computing unit and an output unit. The input unit adds the input information to the corresponding computing module. The computing unit comprises nine modules, each of which is provided with a competitive blocking circuit and a carry computing gate, and dynamically selects a result bit 1 or 2 and generates a carry signal. The output unit outputs the digital information of 0-2 according to the fluorescent signal of the result bit, extracts the next bit carry information generated by the carry computation, amplifies the signal and then adds it to the next bit computation. The application breaks through the limitation of the number of DNA computing bits through the cooperation of the ternary gate control architecture and the fuel chain amplification, can accurately calculate the ternary addition, can expand the number of bits calculated, can realize 10-bit addition operation, and can be integrated with the expansion of the multiplication logic, thereby providing a universal framework for the molecular arithmetic unit.

The application discloses a complex structure prediction method and device and a model training method and an electronic equipment, relates to the technical field of artificial intelligence, in particular to the technical field of deep learning and biological computing. The specific implementation scheme is as follows: obtaining the amino acid sequence of a receptor and the sequence information of a ligand to be combined, and the protein structure of the receptor, determining the orientation information of the ligand relative to the protein structure based on the protein structure of the receptor, and determining the target complex structure of the combination of the receptor and the ligand according to the orientation information of the ligand relative to the protein structure, the amino acid sequence of the receptor and the sequence information of the ligand.

The invention belongs to the technical field of biological computer analysis, and discloses an analysis method based on network pharmacology and a molecular docking technology. The psoriasis treatment of fucoidin is analyzed on the basis of network pharmacology and a molecular docking technology, five core targets, 30 GO annotations and 66 KEGG pathways are screened through the network pharmacology and the molecular docking technology, and the affinity of active ingredients and the core targets is good.

The present disclosure provides a method and device for generating a molecular graph, which relates to the field of computer technology, and specifically to the field of biological computing technology and the field of artificial intelligence technology. The specific implementation scheme is: first, a molecular graph generation model is obtained, and the molecular graph generation model includes an atom prediction sub-model and a chemical bond prediction sub-model, and then the start symbol is input into the atom prediction sub-model for atom prediction, and a candidate atom sequence is determined, and finally the candidate atom sequence is input into the chemical bond prediction sub-model for chemical bond prediction, and a molecular graph corresponding to the candidate atom sequence is generated. The generation of the molecular graph is completed by using the two steps of candidate atom sequence generation and chemical bond generation between candidate atoms, which can effectively improve the effect of molecular graph generation, reduce the difficulty of molecular graph generation, and improve operation efficiency.

A model-based peptide design method in the field of artificial intelligence technology such as biological computing is provided. The specific implementation includes: obtaining a pocket of an objective target protein and an objective peptide, a reserved position for designing a unnatural amino acid is identified in the objective peptide, and the pocket binds to the objective peptide via the reserved position; obtaining a feature of the pocket of the objective target protein and multimodal features of each known amino acid in the objective peptide; the multimodal features of each known amino acid comprise a backbone orientation feature, a backbone rotation feature, a side chain type feature, and a rigid atom group distribution feature; designing the unnatural amino acid at the reserved position in the objective peptide using a pre-trained peptide design model based on the feature of the pocket of the objective target protein and the multimodal features of each known amino acid in the objective peptide.

The present disclosure provides a model-based polypeptide design method, model training method, device and equipment, and particularly relates to the field of artificial intelligence technologies such as biological computing. The specific implementation scheme is: obtaining a pocket of a target target protein and a target polypeptide, the target polypeptide being marked with a reserved position of a non-natural amino acid to be designed; the pocket is combined with the target polypeptide through the reserved position; obtaining the characteristics of the pocket of the target target protein and the multi-modal characteristics of each known amino acid in the target polypeptide; the multi-modal characteristics of each known amino acid include backbone direction characteristics, backbone rotation characteristics, side chain type characteristics, and rigid atom group distribution characteristics; based on the characteristics of the pocket of the target target protein and the multi-modal characteristics of each known amino acid in the target polypeptide, a pre-trained polypeptide design model is used to design a non-natural amino acid at the reserved position in the target polypeptide. The technology of the present disclosure can effectively improve the accuracy of the non-natural amino acid at the reserved position in the designed target polypeptide.

The invention discloses a prediction method and device of a compound structure, a model training method and device and electronic equipment, and relates to the technical field of artificial intelligence, in particular to the technical fields of deep learning, biological calculation and the like. According to the specific implementation scheme, the method comprises the steps of obtaining an amino acid sequence of a receptor, sequence information of a to-be-bound ligand and a protein structure of the receptor, determining azimuth information of the ligand relative to the protein structure based on the protein structure of the receptor, and obtaining the to-be-bound ligand according to the azimuth information of the ligand relative to the protein structure, the amino acid sequence of the receptor and the sequence information of the ligand. And determining the structure of a target compound combined with the receptor and the ligand.

This invention relates to synthetic biology, gene and cell therapy, and particularly to a gene translation system regulated by non-natural amino acids and its applications. This gene translation system precisely regulates gene expression initiation using non-natural amino acids. The invention also provides eukaryotic expression vectors, mammalian cell lines, microcapsules, vacuum fiber tubes, and non-natural amino acid cookies containing the aforementioned gene translation system. Furthermore, this invention discloses that the multifunctional platform can precisely regulate insulin expression and release for diabetes treatment; this gene expression regulation platform can be used to construct complex biological computer-controlled systems. This invention provides a powerful new gene expression regulation tool for gene therapy and cell therapy.

The application belongs to the field of biological computer technology and DNA nanotechnology, and particularly relates to a logic element based on a DNA nanostructure and application of the logic element in manufacturing a true random number generator. The logic element based on the DNA nanostructure takes a two-dimensional cross DNA nanostructure as a main body of the element, utilizes basic characteristics of the DNA nanostructure, such as uniform size, accurate site programming and rich extensible edge chains, constructs logic elements carrying different information through site programming, takes the extensible edge chains as sticky ends to realize a stable and reliable coaxial physical connection relationship between the logic elements, and only when there is a complementary relationship between the sticky ends of the logic elements, can a dimer or a multimer be formed through self-assembly. Therefore, when the logic element is applied to manufacturing the random number generator, the multimer connected by the cross paper folds still has good rigidity, and the correctness of the random generation result is ensured.

The application discloses a biological computing multi-tool unified calling method and system based on a large language model, relates to the fields of biological information and computational chemistry, and at least comprises a task planner, an executor and a tool layer three parts, the method applied to the tool layer in the biological computing multi-tool unified calling system based on the large language model comprises the following steps: based on a unified interactive interface, responding to an interactive instruction with the executor, calling a corresponding type tool to return an execution result of a to-be-executed task step to the executor, wherein, when executing each to-be-executed task in a sequence of executable steps step by step, the executor triggers an interactive instruction with the tool layer based on the type of each to-be-executed task step, and the sequence of executable steps is obtained after the task planner sequentially performs semantic analysis, determines corresponding targets and constraints and performs corresponding task decomposition processing on a to-be-processed file carried in a user input instruction. The application improves task processing efficiency.

The application discloses a training method and device of a protein structure generation model, a protein structure generation method and device, and relates to the technical field of computers, in particular to the fields of artificial intelligence such as biological computing and deep learning. The specific implementation scheme is as follows: a noisy protein structure and a reference semantic representation of an original protein structure corresponding to the noisy protein structure are obtained; in the process of denoising the noisy protein structure by using an initial protein structure generation model, an intermediate hidden variable representation is extracted from the initial protein structure generation model; and the initial protein structure generation model is trained according to the intermediate hidden variable representation and the reference semantic representation, so as to obtain a trained protein structure generation model.

The present disclosure provides a protein design method, device, equipment and medium, relates to the field of artificial intelligence, in particular to the technical field of deep learning, biological computing and large language model. The generation method comprises the following steps: constructing a plurality of candidate proteins, each of which comprises a first chain of an original protein and a non-natural sequence constructed based on a second chain of the original protein; retrieving a first multiple sequence alignment of the first chain and a second multiple sequence alignment of the non-natural sequence; matching the first multiple sequence alignment and the second multiple sequence alignment to obtain a cross-chain homologous sequence by using a pre-trained initial protein language model; predicting the structure and a first score of the candidate protein by using a protein structure prediction model; determining a reward value based on the first score and performing reinforcement learning training on the initial protein language model; determining a second score of each of the plurality of candidate proteins by using the trained target protein language model and the protein structure prediction model, so as to obtain a protein design result.

The invention discloses a method for predicting the harmfulness of synonymous mutation based on multilevel biological characteristics. The method comprises the following steps: 1, collecting and preprocessing a synonymous mutation sample; 2, carrying out DNA, RNA and protein level feature annotation on the obtained synonymous mutation sample by using a biological calculation tool, and carrying out pretreatment; 3, performing dimension reduction on the high-dimensional biological characteristic data by using a causal characteristic selection algorithm to obtain a plurality of causal characteristic subsets; 4, splicing an optimal feature set and performing model construction and training by using a machine learning classifier; and 5, predicting an external test set sample by using the trained optimal model to obtain a synonymous mutation harmfulness prediction score. According to the method, the features having the causal relationship with the target variable can be screened out from the multi-level high-dimensional biological features, and then the causal features of the three biological levels are fused for prediction, so that the synonymous mutation harmfulness prediction precision is improved.

This disclosure provides a training method, design method, apparatus, and device for a protein reverse folding model, relating to the field of computer science, and particularly to artificial intelligence and biological computing technologies. The training method includes: acquiring a training sample set, which includes structural information of the target protein and the binding protein; inputting the training samples into the protein reverse folding model to obtain multiple candidate amino acid sequences of the binding protein; calculating the binding performance indices of the target protein and each candidate amino acid sequence of the binding protein, including complex conformation quantification indices and / or binding affinity quantification indices; constructing positive and negative sample pairs based on the binding performance indices; and training the protein reverse folding model based on a reinforcement learning algorithm and the positive and negative sample pairs to obtain the trained protein reverse folding model. This disclosure can improve the ability of protein reverse folding models to generate binding protein sequences with superior binding performance.

The invention belongs to the field of biological computer technology and DNA nanotechnology, and particularly relates to a logic element based on a DNA nanostructure and application of the logic element in manufacturing of a true random number generator. According to the logic element based on the DNA nano-structure, a two-dimensional cross DNA nano-structure is used as a main body of the element, and the logic element bearing different information is constructed through site programming by utilizing the basic characteristics that the DNA nano-structure is uniform in size, accurate in site programming and rich in extensible side chains; the extensible side chain serves as a sticky tail end to achieve a stable and reliable coaxial physical connection relation between the logic elements, and only when the sticky tail ends between the logic elements have a complementary relation, a dimer or a multimer can be formed through self-assembly, so that when the logic element is applied to manufacturing of a random number generator, the random number generator can be manufactured through self-assembly, and the reliability of the random number generator is greatly improved. It can be guaranteed that the polymer formed by connecting a plurality of cross-shaped origami still has good rigidity, and the correctness of a random generation result is guaranteed.

The present disclosure provides a method, apparatus, device, and medium for determining an mRNA sequence, which relates to the field of artificial intelligence technology, specifically to the field of biological computing and other technical fields. The implementation scheme is as follows: obtaining a DFA graph for determining the amino acid sequence of a target protein; wherein each path in the DFA graph is used to represent a candidate mRNA sequence translated into the target protein, and the candidate mRNA sequence is composed of a codon for each amino acid in the amino acid sequence; based on the free energy of each path in the DFA graph and the score of the adjacent codons in each path, searching the DFA graph for a target path; wherein the score is used to evaluate the contribution of adjacent codons to the translation efficiency and stability of the candidate mRNA sequence in the path in which they are located; and taking the candidate mRNA sequence represented by the target path as the expected mRNA sequence.

The present disclosure provides a method and apparatus for predicting the structure of a protein complex, which relates to technical fields such as artificial intelligence, natural language processing, and biological computing. The method includes: obtaining the initial coordinates of each amino acid residue in a target protein complex, and obtaining the target residue pair features, the first multiple sequence alignment features, and the second multiple sequence alignment features of each protein monomer in the target protein complex; inputting the initial coordinates of each amino acid residue, the target residue pair features, the first multiple sequence alignment features, and the second multiple sequence alignment features of each protein monomer into an N-level folding iterative network layer, and predicting the torsion angle, residue-level position transformation, and monomer chain-level position transformation of each amino acid residue by the N-level folding iterative network layer to obtain the target coordinates of each amino acid residue, thereby obtaining the predicted structure of the protein complex. The present disclosure can accurately predict the structure of a protein and improve the efficiency of predicting the structure of a protein complex.

The application discloses a kind of RNA small molecule binding site prediction method based on multiscale geometric deep learning, belong to bioinformatics and structural biological computing field.The method obtains RNA-small molecule complex structure data and constructs residue level binding site label, extracts RNA sequence information and atomic three-dimensional structure features, generates sequence representation using pre-training RNA language model, constructs deep learning model including hybrid atom encoder, attention type atom to residue polymerization module, residue level space graph neural network module and geometric bias global attention module, fuses atomic level, residue level and global multiscale information, carries out binding site prediction to RNA residue, outputs binding probability and prediction uncertainty.The method can improve prediction accuracy and generalization ability, and is suitable for RNA target drug design and structure-guided small molecule screening.