37 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.

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 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.

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 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 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 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.

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 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 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.

The invention belongs to the technical field of artificial intelligence, and provides a drug research and development architecture and method based on artificial intelligence, electronic equipment and a computer program.The drug research and development architecture constructs a multi-artificial-intelligence-model cluster interactive agent, and applies a large language model to deeply learn and integrate drug related data for new drug research and development. Fusing multi-dimensional information of the whole process of new drug research and development, performing effective analysis, and performing global reasoning to generate potential novel drug candidate molecules and biomolecular targets corresponding to the potential novel drug candidate molecules; and interaction, iteration and optimization are carried out through the thinking chain and various types of artificial intelligence models. The artificial intelligence models comprise a molecular docking model, a protein structure prediction model, a multi-omics conjoint analysis model, a pharmacokinetic model and a quantitative structure-activity model, so that the current situations of high failure rate and high risk of drug candidate molecules and targets in later chemical experiments, biological experiments and clinical experiments are effectively avoided; and the efficiency and success rate of drug research and development are greatly improved.

The application provides a protein classification method based on multi-modal feature fusion and an interpretable network. The method fuses multi-modal features of protein sequences and structures, and adopts an interpretable neural network model for classification. A pre-trained protein language model is used to extract sequence features, and a high-level protein structure prediction model is used to obtain three-dimensional structure information of the protein. Sequence and structure features are fused through an improved attention mechanism or a graph neural network to form a high-dimensional comprehensive representation. Finally, an interpretable classification network is used to output a prediction result and give an explanation of the contribution of key features. The method can efficiently and accurately identify DNA-binding proteins, significantly improve prediction accuracy, has good cross-species generalization ability, and enhances model decision transparency.

The invention belongs to the technical field of fusion proteins, and particularly relates to a mini-utrophin Utro-H3-R22 (mini-utrophin) Utro-H3-R22) as well as a coding gene and an application of the mini-utrophin Utro-H3-R22. The amino acid sequence of the mini-utrophin provided by the invention is as shown in SEQ ID NO. 1. The invention also provides a preparation method of the mini-utrophin. The method comprises the following steps: firstly, predicting the structure of a wild type amyotrophin resisting related protein (utrophin) by adopting protein structure prediction software Alphafold 3; on the basis of reading a large number of literatures and mastering the function of each structural domain, the mini-utrophin which can be loaded in the 4.7 kb capacity of the delivery carrier adeno-associated virus AAV is provided. According to the Utro-H3-R22, an N-terminal structural domain of wild type utrophin is reserved, a C-terminal structural domain is deleted, and the Utro-H3-R22 is mini-utrophin formed by connecting an R3 structural domain and an R22 structural domain through an H3 hinge region. Compared with mini-utrophin in the prior art, the compound disclosed by the invention has certain advantages in structure and function, and can be used for remarkably protecting muscle cells and improving the functions of muscles, so that the effect of treating Duchenne muscular dystrophy (DMD) is achieved.

This invention relates to the field of protein structure prediction technology, and particularly to a method and system for predicting diffusion proteins guided by secondary structure planning. The method includes: acquiring residue feature representations of the amino acid sequence of the protein to be predicted; determining the secondary structure type corresponding to each residue to obtain secondary structure prediction results; constructing a set of geometric constraints to characterize the spatial relationships between residues and determining the constraint weights corresponding to each geometric constraint; initializing the protein's three-dimensional structure generation process and introducing constraint guidance information to generate an initial protein three-dimensional structure; segmenting the amino acid sequence with overlapping regions to obtain multiple substructures and fusing them based on the overlapping regions; and post-processing and optimizing the initial protein three-dimensional structure or the fused structure to obtain the three-dimensional structure prediction results. This invention effectively solves the problem of insufficient prediction quality and controllability caused by the lack of effective guidance in structure generation in existing protein structure prediction methods.

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 belongs to the technical field of bioinformatics, and discloses a keratinase mining method based on structural analysis and keratinase. According to the method for mining keratinase based on structural analysis, provided by the invention, the steps of protein structure prediction, bioinformatics analysis, metagenome mining and the like jointly form an organic whole, so that funnel type screening of massive protein sequences in a metagenome database is realized; a protein sequence with keratinase activity can be obtained through accurate recognition and screening, and the method has huge application potential in efficient and high-throughput screening of keratinase.

The invention discloses an ESMFold model quantification method based on evolution bias search and attention reconstruction, and belongs to the technical field of protein structure prediction and model compression. The method comprises the following steps of: a first stage, keeping weight precision, quantizing an activation value, selecting an optimal logarithm base number for each layer by adopting a self-adaptive logarithm base, and preliminarily screening an offset value; in the second stage, a bias enhancement logarithmic quantizer is introduced, and an evolutionary bias search algorithm (EBS) is utilized to iteratively search the optimal bias parameter of each layer of activation value in the calibration stage so as to improve the expression capability of small activation values close to zero; and in the third stage, the weight is quantized, a block reconstruction optimization strategy aiming at the attention score is adopted, a quantized block structure is redefined, and quantization parameters of each block are reversely optimized by taking the output of the attention score of the teacher model as a guidance target. According to the method, the problems of large distribution difference of different layers, large small-value truncation error and attention mechanism error accumulation in ESMFold model quantization are solved, and the precision of protein structure prediction is effectively maintained while the video memory and calculation overhead of the model are greatly reduced.

The invention relates to a novel recombinant I-type humanized collagen, a coding nucleic acid, a gene expression cassette, a recombinant vector, a recombinant cell, a pharmaceutical composition, a preparation method, a purification method, an application and the like. The recombinant I-type humanized collagen obtained by screening and splicing through the design of combining hydrophilic and hydrophobic properties with Protenix protein structure prediction and the like has high hydrophilicity and stability, also has the biological activity of remarkably promoting cell migration, and has wide application prospects in the fields of medical treatment and cosmetology.

The invention provides a protein classification method based on multi-modal feature fusion and an interpretable network. According to the method, multi-modal features of protein sequences and structures are fused, and an interpretable neural network model is adopted for classification. And extracting sequence features by using a pre-trained protein language model, and obtaining three-dimensional structure information of the protein in combination with the advanced protein structure prediction model. And the sequence and structural features are fused through an improved attention mechanism or a graph neural network to form high-dimensional comprehensive representation. And finally, outputting a prediction result by adopting an interpretable classification network, and giving explanation of key feature contribution. According to the method, the DNA binding protein can be efficiently and accurately recognized, the prediction precision is remarkably improved, the good cross-species generalization ability is achieved, and the model decision transparency is enhanced.

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 invention provides a wheat gene-protein multi-modal information fusion and visualization method, relates to the technical field of protein structure prediction, designs a set of improved scheme combining artificial intelligence deep learning fusing geometric constraint and calculation optimization, and a constructed framework breaks through the limitation of traditional single-modal analysis. End-to-end generation from wheat genome regulation information to a protein three-dimensional structure is realized; according to the method, a subgenome specific noise scheduling mechanism is introduced into diffusion model training, folding preference of different subgenomes can be effectively distinguished, limitation of traditional sequence alignment is broken through, alpha-helix stability change of a DNA binding domain is successfully predicted, a dynamic mask mechanism is developed, a specific regulation element is allowed to be shielded in the generation process, and the method is suitable for large-scale popularization and application. Therefore, directional repair of the functional protein scaffold is realized, a dynamic visualization system is established, a time dimension is introduced, and conformation evolution of a wheat protein structure in different development stages or under different stress conditions can be dynamically presented.

Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media, for antibody structure prediction. In an example method, a target antibody sequence of a target antibody that includes a sequence of amino acids is received. The target antibody sequence is processed by an antibody language model (ALM) to obtain a residue encoding and an attention weight encoding without performing multiple sequence alignment (MSA), wherein the ALM is a protein language model trained from antibody sequences, and the ALM comprises a plurality of self-attention layers. The residue encoding and the attention weight encoding are transformed into a single representation and a pair representation that are input into a structure prediction model. A predicted structure of the target antibody is determined using the structure prediction model.

A protein structure prediction method is applied to the technical field of artificial intelligence. According to the prediction method of the protein structure, experimental data obtained by detecting the protein structure through various experimental modes are obtained firstly, various experimental data are converted into constraint data of the protein structure in a unified mode, and the constraint data can indicate distance constraint conditions required to be met by residues on the protein structure. Then, the constraint data and the amino acid sequence of the protein are jointly used as input of a neural network model, prediction of the protein structure is executed, and therefore the constraint data obtained by converting different types of experimental data is used for assisting the model to predict the protein structure, and the precision of the protein structure obtained through model prediction is improved. Moreover, according to the scheme, the prediction of the protein structure is realized through the model, manual participation is not needed, and the prediction efficiency of the protein structure can be effectively improved.

BIOLOGICAL PROTEIN STRUCTURE PREDICTION LABORATORY DEVICE USING GENERATIVE AI