76 results about "Protein structure prediction" patented technology

The invention relates to a diacylglycerol acyltransferase mutant and application thereof in synthesis of triacylglycerol from saccharomyces cerevisiae, and belongs to the technical field of enzyme engineering. Diacylglycerol acyltransferase is a rate-limiting step of a synthetic route of saccharomyces cerevisiae triacylglycerol (TAG), and at present, a research on a DGA1 mutant for efficiently synthesizing TAG is lacked, and a research on the aspect of a DGA1 catalytic mechanism is also lacked. The DGA1 mutant capable of effectively improving TAG synthesis is obtained by screening in combination with a directed evolution technology, and a result shows that 282-site mutation of diacylglycerol acyltransferase can significantly increase the accumulation amount of TAG. Meanwhile, the catalytic mechanism of the DGA1 is preliminarily explored by combining protein structure prediction, molecular docking and molecular dynamics simulation, and a foundation is laid for deeply analyzing the catalytic mechanism of the DGA1. The recombinant saccharomyces cerevisiae is also constructed based on the mutant, so that the TAG synthesis is improved, and meanwhile, the relative proportion of C18: 1 in total fatty acids is remarkably increased.

Disclosed herein are methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for antibody structure prediction. In one example method, a target antibody sequence for a target antibody comprising an amino acid sequence is received. The target antibody sequence is processed by an antibody language model (ALM) to obtain residue coding and attention weight coding without multiple sequence alignment (MSA), where the ALM is a protein language model trained according to the antibody sequence, and the ALM comprises a plurality of self-attention layers. The residue coding and attention weight coding are converted into a single representation and paired representations and input into a structure prediction model. And determining a prediction structure of the target antibody by using the structure prediction model.

The invention discloses a quantum computing method for protein structure prediction. The method comprises the following steps: acquiring a protein amino acid sequence; performing coordinate generation and polar coordinate conversion on each residue, and calculating an interaction strength parameter to complete sequence pretreatment; constructing a continuous coordinate quantization encoder quantum circuit and realizing quantization encoding of protein continuous space coordinates; constructing an interaction strength quantization encoder quantum circuit and realizing quantization encoding of interaction strength between residues; iteratively updating the obtained data information to realize optimization of all residue coordinate information; and performing standardization processing according to the obtained data information to obtain a prediction result of the protein structure. The invention also discloses a system for realizing the quantum computing method for predicting the protein structure. According to the method, through quantization coding of continuous space coordinates of the protein, quantization coding of interaction strength between residues and the iterative updating process, prediction of the protein structure is achieved, reliability is higher, accuracy is better, and occupied computing resources are fewer.

The invention belongs to the field of bioinformatics, relates to an enzyme function prediction method based on structure perception and semantic features, and integrates technologies such as a protein large language model, a protein structure prediction model, a graph convolutional neural network and a cross-modal attention mechanism. The method comprises the following five steps of initial multi-view feature extraction, key structure perception, key semantic information extraction, multi-view information fusion and function classification. The method comprises the following steps: firstly, inputting an amino acid sequence of enzyme into ESM2 and ProtT5 protein large language models and an ESMFold protein structure prediction model for preprocessing so as to extract Esm semantic information, Prt semantic information and structure information; then, inputting the Esm semantic information and the structure information into a key structure sensing module provided by the method so as to obtain key structure features; a large number of experimental results show that the method can efficiently and accurately predict the enzyme function, and the performance of an existing method is remarkably improved.

The present invention proposes a method and system for predicting the binding of polypeptides to TCRs based on deep learning. The method comprises: digitizing the sequences of polypeptides and TCRs according to a digital dictionary of amino acids to extract the correlation features of the polypeptides and TCR sequences; obtaining the three-dimensional structure of the polypeptide and TCR sequences according to the HelixFoldSingle protein structure prediction model, and extracting the three-dimensional structural features of the polypeptide and TCR sequences; linearly weighting the correlation features of the polypeptide and TCR sequences and the three-dimensional structural features of the polypeptide and TCR sequences to obtain a prediction result of the binding of polypeptides to TCRs. The scheme proposed by the present invention takes into account the correlation features of the interaction between polypeptides and TCRs and also takes into account the spatial structural features of the sequences. By combining the correlation features between sequences and the sequence structural features through a multimodal deep learning model, the accuracy of predicting the binding of polypeptides to TCR sequences is further improved.

The invention provides a polypeptide and TCR combination prediction method and system based on deep learning. The method comprises the following steps: performing digital processing on sequences of polypeptide and TCR according to a digital dictionary of amino acid, and extracting correlation characteristics of the sequences of the polypeptide and the TCR; obtaining a three-dimensional structure of the polypeptide and the TCR sequence according to a HelixFoldSingle protein structure prediction model, and extracting three-dimensional structure characteristics of the polypeptide and the TCR sequence; and performing linear weighting on the correlation characteristics of the polypeptide and the TCR sequence and the three-dimensional structure characteristics of the polypeptide and the TCR sequence to obtain a prediction result of combination of the polypeptide and the TCR. According to the scheme provided by the invention, correlation characteristics of interaction between the polypeptide and the TCR are considered, and spatial structure characteristics of the sequence are also considered. The correlation characteristics and sequence structure characteristics between the sequences are combined through a multi-modal deep learning model, so that the accuracy of predicting the combination of the polypeptide and the TCR sequence is further improved.

The invention discloses application of novel reverse transcriptase and related fusion protein of the novel reverse transcriptase in plant-guided editing. Candidate RT protein is obtained through systematic mining from a public database, novel reverse transcriptase cl1 with activity is screened out in combination with a fluorescence report system, and a guided editing system suitable for plants is constructed. The system tests in wheat protoplast, and verifies the editing activity of cl1 to a plurality of endogenous targets. Further combining with protein structure prediction and rational design point mutation optimization, the cl1 mutant with higher editing efficiency is obtained, and the method can be applied to accurate and effective editing of plant genomes and plant breeding and improvement.

The invention discloses an IV-type secretory effect protein recognition method and system based on multi-modal information, and belongs to the technical field of bioinformatics and secretory protein recognition. The method comprises the following steps: firstly, extracting amino acid residue characteristics of protein by using a protein language model, and constructing a spatial adjacency graph of the protein by using a three-dimensional structure predicted by a protein structure prediction model; respectively extracting sequence features and structural features of the protein through a deep sequence module and a hierarchical graph module; meanwhile, a contrast learning module is introduced to realize cross-modal alignment of the same protein in a potential space; and performing bidirectional interaction on the sequence features and the structural features by using a cross attention module to obtain joint features, and finally outputting a classification result after nonlinear transformation by a gating linear unit for predicting the IV-type secretion effect protein. According to the invention, efficient coordination of sequence and structure bimodal information is realized, and the identification accuracy of the IV-type secretory effect protein is remarkably improved.

The invention relates to the technical field of bioinformatics, in particular to a transaminase screening method and device, electronic equipment and a storage medium. According to the transaminase screening method and device, the electronic equipment and the storage medium provided by the embodiment of the invention, firstly, interaction of enzyme and a substrate is learned based on a protein-substrate binding activity prediction model; the binding activity of transaminase and a substrate is rapidly predicted, and first screening is achieved; then, on the basis of a protein structure prediction model, constructing a three-dimensional structure of a compound formed by combining the transaminase subjected to the first screening and a substrate, and realizing second screening; and finally, carrying out molecular dynamics simulation on the compound subjected to the second screening. By means of the mode, deep learning and physical modeling are combined, the accuracy of transaminase-substrate interaction prediction is improved, and the transaminase screening efficiency can be improved.

The application discloses a kind of heterodimer interchain residue contact prediction method.The present application is aimed at the deficiency of existing method in prediction accuracy, long-range dependence modeling capability and generalization, and proposes a kind of deep neural network integrating multiple features and attention mechanism.Specifically, the application integrates multiple features such as protein language model as network model input, then the network adopts efficient channel attention (ECA) and spatial attention (SA) module and KAN convolution network module, effectively captures the local and global dependence features of heterodimer, so as to predict the interchain residue contact of heterodimer.Experiments show that the prediction accuracy of the application on the benchmark dataset is significantly better than that of existing methods, and the model has high robustness.The application can be widely used in the field of protein heterodimer interchain residue contact prediction and protein structure prediction, further promoting protein function research and protein drug development.

The invention provides a signal peptide category and cleavage site prediction method and system based on multi-modal characteristics, and belongs to the technical field of biological information analysis. The method comprises the following steps: acquiring an amino acid sequence of a signal peptide sample, and acquiring three-dimensional structure data of the signal peptide by utilizing a protein structure prediction model; obtaining sequence modal input data of the amino acid sequence of the signal peptide, and constructing a structural diagram to obtain structural modal input data; inputting the sequence modal input data into a sequence encoder and a protein language model, and extracting sequence features; inputting the structural modal input data into a structural encoder, and extracting structural features through graph convolution operation; carrying out fusion processing on the sequence features and the structural features to obtain multi-modal feature representation; and outputting a category prediction result and a cleavage site prediction result of the signal peptide through a prediction module. According to the invention, through fusion of the sequence and the structure information, the accuracy of signal peptide prediction and the recognition capability of minority class samples are improved.

The invention discloses a novel protein structure prediction method based on AI, and particularly relates to the technical field of protein structure research, a protein amino acid sequence is converted into a multi-dimensional feature matrix, homologous fragment information is fused, and feature representation with physical and chemical properties and potential folding rules is obtained; extracting local residue relation and global sequence dependency information by combining graph convolution and a self-attention mechanism, constructing a multi-scale subsurface space, and realizing comprehensive description of a folding trend; candidate conformation generation and uncertainty indexes are introduced into the submerged space, and it is ensured that the prediction process has reliable quantization; ranking and screening the priorities of the candidate conformations in combination with an energy constraint function and structural similarity measurement, and outputting an optimized structure set with reasonable energy and coordinated trend; a prediction structure is generated through multi-modal information fusion, and credibility evaluation is performed, so that the problem that prediction accuracy and interpretability are difficult to consider at the same time in an existing method is effectively solved.

A method is proposed for protein structure prediction. In the method, for a diffusion process, an initial structure of a target protein is obtained based on composition information concerning amino acids of the target protein. The diffusion process on the initial structure is performed based on a force field prediction for the target protein and the composition information. A target structure of the target protein is determined based on a result of the diffusion process.

The application relates to the technical field of breast cancer analysis and prediction, and particularly relates to a breast cancer gene drug screening method and system based on deep learning. The screening method obtains breast cancer gene mutation information, performs matching retrieval in a MySQL database, and judges whether the breast cancer gene mutation information is existing information. If not, sequence retrieval and consistency verification are performed to obtain mutant sequence information. Based on a deep learning model, the mutant sequence information is subjected to protein structure prediction to obtain three-dimensional spatial conformation data of mutant amino acids. Based on a drug small molecule, the three-dimensional spatial conformation data is subjected to multi-dimensional functional quantitative evaluation to obtain functional influence coefficient analysis data. The drug small molecules are classified, and based on the functional influence coefficient analysis data, the classified drugs and the breast cancer gene mutation are subjected to matching degree calculation to obtain a drug recommendation index. The screening method can improve the accuracy of breast cancer gene mutation drug screening.

The present invention belongs to the field of protein structure prediction technology and relates to a protein structure prediction method, system, and device based on an optical quantum computer. The method comprises: 1) obtaining a multiple sequence alignment (MSA) matrix of the target sequence based on the target sequence alignment of the protein to be predicted; 2) encoding the obtained MSA matrix into a {0,1} matrix; 3) converting the protein's amino acid interactions into an undirected graph model with {0,1} state nodes and training the model using an optical quantum computer using a Boltzmann machine training mechanism to obtain weight coefficients for the edges connecting the nodes in the undirected graph model; and 4) obtaining interaction coefficients between different amino acids in the protein based on the weight coefficients. This method improves the computational efficiency and solution results of protein structure prediction, and solves the problem that the existing technology is difficult to calculate and cannot obtain accurate solutions for complex scheduling problems.

The invention relates to the technical field of breast cancer analysis and prediction, in particular to a breast cancer gene drug screening method and system based on deep learning. According to the screening method, breast cancer gene mutation information is obtained, matching retrieval is conducted in a MySQL database, and whether the breast cancer gene mutation information is existing information or not is judged; if not, sequence retrieval and consistency verification are carried out, and mutant sequence information is obtained; performing protein structure prediction on the mutant sequence information based on a deep learning model to obtain three-dimensional space conformation data of mutant amino acids, and performing multi-dimensional function quantitative evaluation on the three-dimensional space conformation data based on drug small molecules to obtain function influence coefficient analysis data; and grading the drug small molecules, and carrying out matching degree calculation on the graded drug and the breast cancer gene mutation according to the function influence coefficient analysis data to obtain a drug recommendation index. The screening method can improve the accuracy of breast cancer gene mutation drug screening.

The present invention discloses a quantum computing method for predicting protein structure, comprising obtaining a protein amino acid sequence; performing coordinate generation and polar coordinate conversion on each residue and calculating interaction strength parameters to complete sequence pre-processing; constructing a continuous coordinate quantization encoder quantum circuit and realizing quantization encoding of protein continuous spatial coordinates; constructing an interaction strength quantization encoder quantum circuit and realizing quantization encoding of interaction strength between residues; iteratively updating the obtained data information to optimize the coordinate information of all residues; and performing standardization processing on the obtained data information to obtain a prediction result of the protein structure. The present invention also discloses a system for realizing the quantum computing method for predicting protein structure. The present invention not only realizes the prediction of protein structure through the quantization encoding of protein continuous spatial coordinates, the quantization encoding of interaction strength between residues and the iterative update process, but also has higher reliability, better accuracy and less computing resources occupied.

The application discloses RNase R mutants with improved reaction temperature and thermal stability, and a preparation method and application thereof, and relates to the technical field of biology.The application obtains three RNase R mutants with amino acid sequences shown in SEQ ID NO:3, SEQ ID NO:5 and SEQ ID NO:7 by using protein structure prediction related software to design, transform and screen RNase R.The reaction temperature and thermal stability of the RNase R mutants are improved compared with wild-type RNase R, and the RNase R mutants have obvious advantages in applications such as circular RNA enrichment, and the application range of RNase R in the field of RNA research is expanded.

This application discloses a method, system, device, and medium for minimizing protein conformational energy. The method includes: determining the loop region and main chain region of the protein; obtaining the potential energy function of the loop region based on hydrogen bond energy and main chain dihedral angle deviation, and obtaining the potential energy function of the main chain region based on the main chain dihedral angle; obtaining a dual potential energy function based on the loop region potential energy function and the main chain region potential energy function; and optimizing the protein conformation by dynamically adjusting the constraint force constant until the minimum value of the dual potential energy function is found. Using the embodiments of this application, the accuracy of protein structure prediction can be effectively improved, especially the accuracy of loop conformation. By employing a dual resonant potential energy constraint system, which considers the conformational characteristics of both the loop and non-loop regions, greater conformational flexibility can be given to the loop region while maintaining overall structural stability, providing an important structural basis for drug screening and structure-function studies.

The invention discloses an antibody-oriented double-branch structure feature prediction method, and relates to the technical field of protein expression learning and protein structure prediction, and the method comprises the following steps: 1, taking a given antibody amino acid sequence as an input, extracting three types of features including an MSA feature, a Pair feature and a template feature of an antibody, carrying out collaborative fusion on the three types of features; 2, constructing a double-branch structure feature prediction module, and optimizing an antibody structure; step 3, model training; and 4, testing the model to obtain an antibody structure. By adopting the antibody-oriented double-branch structure feature prediction method disclosed by the invention, efficient and accurate antibody structure feature prediction is realized, and powerful support is provided for related research and application of antibodies.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a predicted structure of a protein that is specified by an amino acid sequence. In one aspect, a method comprises: obtaining an initial embedding and initial values of structure parameters for each amino acid in the amino acid sequence, wherein the structure parameters for each amino acid comprise location parameters that specify a predicted three-dimensional spatial location of the amino acid in the structure of the protein; and processing a network input comprising the initial embedding and the initial values of the structure parameters for each amino acid in the amino acid sequence using a folding neural network to generate a network output comprising final values of the structure parameters for each amino acid in the amino acid sequence.

The invention relates to a method for introducing disulfide bonds in a protein design process, which comprises the following steps: firstly, designing main chain coordinates of all residues of a scaffold by using protein skeleton design software to enable the main chain coordinates to contain a given motif, and secondly, calculating candidate site pairs meeting disulfide bond forming conditions, then, redesigning all residues except the motif and the disulfide bond candidate sites by using protein sequence design software to obtain a protein sequence, carrying out structure prediction on the protein sequence by using protein structure prediction software to obtain a protein structure, and comparing the protein structure by using structure comparison software to obtain a protein structure; and screening out site pairs meeting conditions through calculation, and finishing the design if the site pairs meet the requirement on the number of disulfide bonds. Compared with the prior art, disulfide bonds are introduced in the design process of the protein motif scaffold, and the protein motif scaffold has diversity in the aspects of sequence length and residue distribution.

The invention discloses a salt water lotus reverse breeding and whole industry chain cooperative control method and system based on digital twinning and health demand driving. A cloud digital twinborn collaboration platform is taken as a core, and a clinical big data twinborn model, a molecular design digital twinborn body, a plant growth digital twinborn body and an extraction process twinborn body are constructed: firstly, a health target is simulated in a virtual space and digitally mapped into a raw material minimum effective concentration Cmin; then, virtual screening is carried out on a molecular level through protein structure prediction and a gRNA effectiveness model, and an editing scheme is determined; in the cultivation stage, a Ct measured value obtained through qPCR is compared with a twin target, PID is driven to carry out reverse adjustment on environmental factors such as salinity, and molecular-level closed-loop control is formed; triggering emergency flushing and closed-loop recovery by combining root CT and LSTM risk prediction of soil multi-source sensing data; the extraction end is linked with the supercritical CO2 density self-adaptive adjustment through near-infrared online monitoring, and a closed loop is finally detected through HPLC (High Performance Liquid Chromatography).

The application discloses an intelligent computing power scheduling system and method for mRNA coding protein structure prediction and electronic equipment, and relates to the technical field of protein structure prediction.The intelligent computing power scheduling system for mRNA coding protein structure prediction comprises an input and task management layer, which is used for acquiring and translating mRNA sequences to obtain atomic sub-tasks; an intelligent sensing and scheduling core layer, which is connected with the input and task management layer and is used for generating corresponding optimization scheduling decisions according to the atomic sub-tasks; a heterogeneous computing power resource layer, which is connected with the intelligent sensing and scheduling core layer and is used for performing classical calculation and / or quantum calculation according to the optimization scheduling decisions to obtain corresponding classical calculation results and / or quantum calculation results; and an output and optimization layer, which is connected with the heterogeneous computing power resource layer and is used for generating a protein 3D structure model according to the classical calculation results and / or quantum calculation results.The system can improve the accuracy and efficiency of protein structure prediction results and improve the utilization rate of computing power resources.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing protein structure prediction. In one aspect, a method comprises generating a distance map for a given protein, wherein the given protein is defined by a sequence of amino acid residues arranged in a structure, wherein the distance map characterizes estimated distances between the amino acid residues in the structure, comprising: generating a plurality of distance map crops, wherein each distance map crop characterizes estimated distances between (i) amino acid residues in each of one or more respective first positions in the sequence and (ii) amino acid residues in each of one or more respective second positions in the sequence in the structure of the protein, wherein the first positions are a proper subset of the sequence; and generating the distance map for the given protein using the plurality of distance map crops.

The application discloses a kind of inherent disordered region prediction methods based on protein atlas double-scale features, it is related to protein structure prediction technical field.The method steps are:S1, obtain the original amino acid sequence of protein to be predicted;S2, utilize protein encoder to carry out feature coding to the original amino acid sequence of protein, obtain residue level embedding matrix feature and three-dimensional space coordinate feature;S3, construct the protein hetero atlas containing scalar feature and vector feature;S4, the protein hetero atlas is input into joint feature learning module and carries out feature fusion and learning, obtains the fusion feature representation of each amino acid residue;S5, based on the fusion feature representation, the probability that each amino acid residue belongs to ordered region or disordered region is predicted by classifier.The application significantly improves the prediction accuracy and reliability of protein inherent disordered region, and provides a better quality of computational analysis tool for related biological mechanism research.

The invention discloses RNase R mutants capable of improving reaction temperature and thermal stability as well as a preparation method and application of the RNase R mutants, and relates to the technical field of biology. According to the invention, RNase R is designed, transformed and screened by using protein structure prediction related software, so that three RNase R mutants with amino acid sequences respectively shown as SEQ ID NO: 3, SEQ ID NO: 5 and SEQ ID NO: 7 are obtained. Compared with a wild type RNase R, the reaction temperature and the thermal stability of the RNase R are improved, the advantages in applications such as circular RNA enrichment and the like are obvious, and the application range of the RNase R in the field of RNA research is expanded.

The application relates to the field of enzyme engineering, in particular to a xylanase, a xylanase optimization design method, a DNA molecule, a recombinant plasmid and a modified cell. The xylanase with an amino acid sequence of SEQ ID NO: 2 has high enzyme activity and thermal stability. The sequence of the xylanase is obtained through optimization design. The mechanism of a multi-domain matrix enzyme is predicted through an online protein structure prediction platform, and the motion trajectory of the matrix enzyme in a solution is simulated under an AMBER99SB force field through molecular dynamics simulation software, the root mean square fluctuation values of different fragments in a connecting section between adjacent functional domains of the matrix enzyme are analyzed and calculated, the fragment with the maximum root mean square fluctuation value in the connecting section is deleted, and the amino acid sequence of the design enzyme is obtained. The optimization design of the enzyme through the method has high reliability and is helpful to improve the enzyme activity and thermal stability of the matrix enzyme.

The present invention discloses a method and apparatus for determining protein structure. First structural data is obtained, where the first structural data is modeled based on cryo-electron microscopy observation data. Second structural data is obtained, where the second structural data is predicted based on the amino acid sequence. The protein structure is determined based on the first and second structural data. This method effectively combines the advantages of two different protein structure prediction methods and overcomes the shortcomings of each method.

The application provides a protein structure prediction model training method and a protein structure prediction method, and relates to the technical field of computers. The protein structure prediction method and the protein structure prediction method provided by the application are characterized in that each amino acid sequence sample in a training data set has a first feature matrix and a second feature matrix, the first feature matrix and the second feature matrix of each amino acid sequence sample are used as a basis, and a knowledge distillation method is adopted to jointly train a feature enhancement network and a structure prediction network contained in a protein structure prediction model by means of an auxiliary training classification network. The obtained protein structure prediction model can be used to predict the structure of a to-be-processed protein based on a low-quality feature representation matrix of the to-be-processed protein, and a protein result prediction result with high precision can be obtained, thereby improving the prediction accuracy of protein structure prediction.