64 results about "Protein pair" patented technology

The invention provides a drug-target correlation prediction method based on a hierarchical representation learning framework, and the method comprises the steps: screening high-information-density nodes based on a dynamic fluctuation threshold value, reducing low-noise nodes, and reconstructing topological connection according to a virtual edge weight formula; performing tensor splicing on the drug molecular features extracted by the dynamic neighborhood search framework and the protein semantic features generated by the denoising auto-encoder to form drug-protein pair joint feature representation; a multi-head attention mechanism is utilized to allocate dynamic weights for multi-view features based on drug-protein pairs, multi-view feature vectors are spliced, after key information is screened through the attention mechanism, the key information is input into a full-connection neural network, and a correlation prediction value is output based on the full-connection neural network. The association prediction method solves the problems that the prediction precision of the model on the complex biological interaction is poor, and the understanding ability of the model on the multilevel feature learning association in the complex biological network is seriously limited.

The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilizing compound-protein machine learning representations to generate target results. For example, the disclosed systems can utilize a compound-protein interaction machine learning model to generate a compound-protein machine learning representation for compound protein pairs. The disclosed systems can utilize the compound-protein machine learning representation to train and utilize other target machine learning models in generating predicted bioactivity results. For example, the disclosed systems train a target machine learning model from compound-protein machine learning representations to generate ADMET predictions and / or biological perturbation program predictions. Furthermore, the disclosed systems can utilize one or more explainability models in conjunction with target machine learning models trained based on compound-protein machine learning representations to identify proteins that contribute to predicted bioactivity results.

The invention provides a design method of a protein variant structure based on molecular dynamics, and belongs to the technical field of molecular dynamics simulation. A protein-standard nucleic acid compound ground molecule system and a protein-nonstandard sequence compound ground molecule system are respectively constructed, and based on a molecular dynamics simulation technology, protein conformation stability, low-energy conformation states, main change processes, collaborative movement and key residues of the protein-standard sequence system and the protein-nonstandard sequence system are compared, so that protein conformation stability and low-energy conformation states of the protein-standard nucleic acid compound ground molecule system and the protein-nonstandard sequence compound ground molecule system are obtained. The molecular mechanism of protein-nucleic acid interaction is analyzed, the recognition behavior of the protein on the standard nucleic acid sequence and the activation process of the standard nucleic acid sequence on the corresponding function of the protein are understood from the molecular level, and finally the protein variant with more related functions is designed.

The invention discloses a method for rapidly determining the content of breast milk oligosaccharide in dairy products, which comprises the following steps: sampling and pretreating different types of dairy products according to sample states, carrying out ultrasonic extraction, refrigerated centrifugal degreasing and centrifugal ultrafiltration protein purification on the obtained sample, and reducing the purified sample solution by using sodium borohydride to obtain a sample solution to be detected; reducing the breast milk oligosaccharide standard substance by sodium borohydride to obtain a standard solution to be detected; determining the standard substance solution and the to-be-detected sample solution by adopting ultra-high performance liquid chromatography-tandem mass spectrometry, establishing a standard curve, and carrying out quantitative analysis on the to-be-detected sample solution; the chromatographic conditions are as follows: an ACQUITY UPLC BEH Amide hydrophilic interaction chromatographic column is adopted; the mobile phase A is 20 mmol / L ammonium formate; the mobile phase B is acetonitrile; and gradient elution.

The invention aims to provide a method, equipment, medium and program product for determining acidity coefficient, the method comprises the following steps: constructing a simulation box corresponding to protein, the simulation box comprising the protein, a corresponding solvent and a buffer solution; performing molecular dynamics simulation on the simulation box by utilizing a trained machine learning force field to obtain corresponding track file information; and determining an acidity coefficient corresponding to each drippable site in the protein based on the track file information. A dynamic process of protein in a solution is accurately and efficiently simulated by utilizing a machine learning force field, and an accurate acidity coefficient is calculated based on a state of a protein solution system in simulation.

The invention provides a protein mutation site prediction method based on a graph neural network. The method comprises the following steps: extracting binding site information corresponding to each protein in a data set; converting each protein into graph structure data according to the PDB file and the binding site information of the protein to obtain a protein graph; constructing a graph neural network model, inputting the protein graph into the model, updating the features of nodes by using a message passing mechanism, and inputting the features of the nodes into a classifier for amino acid residue prediction; comparing the amino acid residue type predicted by the model with a wild type residue tag, and calculating a loss value; performing back propagation, further learning the model parameters until the loss value is stable, and storing the optimal parameters of the model after training is finished; and inputting a target protein into the trained model, comparing an amino acid residue predicted by the model with a wild-type amino acid residue at a corresponding site, and if the amino acid residue is different from the wild-type amino acid residue and has a property difference, considering that the site is likely to generate feasible mutation.

The present invention relates to methods of preparing a therapeutic exosome using a protein newly-identified to be enriched on the surface of exosomes. Specifically, the present invention provides methods of using the proteins for affinity purification of exosomes. It also provides methods of localizing a therapeutic peptide on exosomes, and targeting exosomes to a specific organ, tissue or cell by using the proteins. The methods involve generation of surface-engineered exosomes that include one or more of the exosome proteins at higher density, or a variant or a fragment of the exosome protein.

The invention relates to a drug target determination method and device, computer equipment and a storage medium. The method comprises the following steps: obtaining a protein heat map corresponding to candidate proteins; based on a feature extraction model, extracting features of the protein heat map to obtain heat map features corresponding to the candidate proteins; based on the heat map features corresponding to the candidate proteins, clustering the candidate proteins to obtain a plurality of candidate protein sets; based on the temperature data and the protein concentration data of each candidate protein in a candidate protein set, determining the protein concentration difference significance corresponding to the candidate protein set, and based on the protein concentration difference significance and the protein concentration data of the candidate proteins, determining the protein concentration difference significance corresponding to the candidate protein set; and determining standard reference data of the candidate proteins, and determining a drug target based on the standard reference data of each candidate protein in the plurality of candidate protein sets. According to the embodiment of the invention, the drug target can be accurately determined.

The invention belongs to the field of biological information, and relates to a protein interaction prediction method fusing large model semantics and contact graph information. According to the method, four key modules including protein sequence embedding based on an ESM2 language model, contact graph structure representation based on AlphaFold, a graph structure construction module and a depth graph convolutional coding module based on GCNII are combined. Through fusion of global semantic representation of a protein sequence and a structure contact relationship, the DGraph-PPI can effectively capture potential topological dependence and function association between proteins. Compared with an existing prediction model based on sequence or structure single information, the DGraph-PPI shows better performance on multiple evaluation indexes, and particularly has remarkable advantages in the aspects of recognizing complex interaction modes, processing low-sequence similarity protein pairs and improving generalization ability.

The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilizing compound-protein machine learning representations to generate target results. For example, the disclosed systems can utilize a compound-protein interaction machine learning model to generate a compound-protein machine learning representation for compound protein pairs. The disclosed systems can utilize the compound-protein machine learning representation to train and utilize other target machine learning models in generating predicted bioactivity results. For example, the disclosed systems train a target machine learning model from compound-protein machine learning representations to generate ADMET predictions and / or biological perturbation program predictions. Furthermore, the disclosed systems can utilize one or more explainability models in conjunction with target machine learning models trained based on compound-protein machine learning representations to identify proteins that contribute to predicted bioactivity results.

The application provides a double cell recognition method, comprising the following steps: step one) obtaining a data set to be subjected to double cell recognition, and then clustering and grouping the data set to be subjected to double cell recognition to obtain a plurality of cell populations; step two) determining mutually exclusive protein pairs according to the cell types of the data set to be subjected to double cell recognition, and then determining the expression negative cell population and the expression positive cell population corresponding to each protein in the mutually exclusive protein pairs by using the COSG method of cyclic iteration in the cell populations obtained in step one); step three) calculating the threshold value of the positive and negative expression of each protein in the mutually exclusive protein pairs according to the data in the expression negative cell population and the expression positive cell population; and step four) recognizing double cell data in the data set to be subjected to double cell recognition according to the threshold value. The double cell recognition method based on the COSG method of cyclic iteration is more stable and more effective in the recognition of positive and negative cell population modules.

A method for generating binding peptides presented by any given Major Histocompatibility Complex (MHC) protein is presented. The method includes, given a peptide and an MHC protein pair, enabling a Reinforcement Learning (RL) agent to interact with and exploit a peptide mutation environment by repeatedly mutating the peptide and observing an observation score of the peptide, learning to form a mutation policy, via a mutation policy network, to iteratively mutate amino acids of the peptide to obtain desired presentation scores, and generating, based on the desired presentation scores, qualified peptides and binding motifs of MHC Class I proteins.

The invention discloses a drug-target interaction prediction method combining link-perceived cross attention and link-level contrast learning, and introduces a link-perceived cross attention mechanism which allows global features of a sequence (such as protein) to serve as a query to pay attention to internal details of a paired sequence (such as a drug), so as to predict drug-target interaction. Context-aware local features are dynamically generated for each drug-protein pair. Besides, in order to cope with the challenge that information of a knowledge graph is difficult to apply to a cold start scene, a link-based contrast learning strategy (Link-based contrast learning) is designed, and local features generated by a sequence mode through cross attention are aligned with local features obtained through different relation links in a relation mode, so that the probability that the information of the knowledge graph is applied to the cold start scene is lowered, and the probability that the information of the knowledge graph is applied to the cold start scene is lowered. Richer and finer-grained supervision signals are provided for the model, and the sequence model is prompted to learn a'relation mode 'contained in the knowledge graph instead of memorizing a'relation instance' of a specific entity.

This application relates to the field of computer technology and provides a method, apparatus, electronic device, and computer program product for constructing a protein language. The method includes: segmenting a target protein based on secondary structure information to obtain two or more target protein fragments; determining the protein language terms corresponding to each target protein fragment; and determining the protein language statement corresponding to the target protein based on the protein language terms corresponding to each target protein fragment. This application facilitates the capture of semantic information at the molecular level by the protein language model, thereby obtaining better protein representation.

This disclosure provides a method, apparatus, medium, and electronic device for predicting RNA-protein interactions; relating to the field of artificial intelligence technology. The method includes: acquiring an RNA-protein pair to be predicted; extracting features from the RNA-protein pair to obtain sequence features; vectorizing the RNA-protein pair to obtain RNA sequence representation vectors and protein sequence representation vectors; based on the sequence features, RNA sequence representation vectors, and protein sequence representation vectors of the RNA-protein pair, using an interaction prediction model to obtain predicted interaction values ​​for the RNA-protein pair; and determining the interaction between the RNA and protein based on the predicted interaction values.

This application discloses a protein liquid-liquid phase separation prediction method based on cross-level information transfer. The method obtains the amino acid sequence of the target protein and generates residue-level fusion feature sequences using a branched coding network and a gated fusion network. These residue-level fusion feature sequences are then input into a residue prediction head to generate residue-level driving probabilities. The K highest probability values ​​from these driving probabilities are extracted, and corresponding high-response statistics are constructed. Protein-level aggregation of the residue-level fusion feature sequences is performed to generate a protein-level global representation. This global representation and the high-response statistics are then input into a protein prediction head to output a liquid-liquid phase separation tendency score for the target protein. This method achieves cross-level collaborative inference of residue-level driving region localization and protein-level phase separation tendency prediction, solving problems such as the disconnect between protein-level prediction and residue-level localization, and the dilution of the contribution of highly active driving sites by non-driving residues, thus improving the accuracy of the prediction results.

A nutritional composition for administering to a human includes an amount of bioavailable protein that is safe for human consumption and that is effective for supporting the synthesis of proteins, and the repair and maintenance of cells and tissues throughout the body. An amount of a medium-chain triglyceride that is safe for human consumption and that is effective for maintaining gut health, an amount of an emulsifier that is safe for human consumption and that is effective for maintaining cellular health, and an amount of soluble fiber that is safe for human consumption and that is effective for maintaining or enhancing a health of the gut or cardiovascular system are also included. An amount of one or more vitamins and minerals that are effective for blood cell production are also included. The nutritional composition is formed as a chewable dosage form comprising a gelatin matrix.

The present application relates to a unified protein modification method, apparatus and computer equipment, which fine-tune the initial protein representation model based on a text-protein pair data set, align the human language large model and the initial protein representation model in the semantic space, and obtain a target protein representation model; input the obtained optimization target into the human language large model to obtain a first representation; based on the obtained key mutation sites, generate a set of protein mutants; input the set of protein mutants into the target protein representation model to obtain a second representation corresponding to each of the protein mutants; based on the second representation matching the first representation, obtain a protein mutant that meets the optimization target, solve the problem of poor generalization of protein function prediction, improve the function prediction performance of unknown proteins and mutants, and achieve efficient protein modification effect.

Embodiments of the present application provide a protein stability prediction method and device, computer equipment and a storage medium. The method comprises: obtaining a first to-be-tested sequence corresponding to an original protein and a second to-be-tested sequence corresponding to a mutant protein; determining a plurality of first target residue features from the first to-be-tested sequence according to the attention mechanism of the protein language model and extracting the global features thereof to obtain first virtual structure microenvironment features; similarly, obtaining second virtual structure microenvironment features corresponding to a plurality of second target residue features; determining a first difference based on the first virtual structure microenvironment features and the second virtual structure microenvironment features, and determining a second difference based on a first global sequence feature corresponding to the first to-be-tested sequence and a second global sequence feature corresponding to the second to-be-tested sequence; and predicting based on the first difference and the second difference to obtain a protein stability prediction result. In this way, the accuracy of predicting the stability of the protein can be improved.

The invention relates to a method for recovering protein from chromium-containing sludge based on subcritical water, which comprises the following steps: pretreatment: taking chromium-containing sludge, adding normal-temperature water, adding magnesium oxide, adjusting the pH value to 9-10, uniformly stirring, and standing for 3-6 hours; adding into a subcritical water reaction device, adding water, and sealing; raising the temperature to 150 to 200 DEG C within 2 to 3 hours, and then reacting for 60 to 120 minutes; cooling to normal temperature, and filtering by using a 100-150-mesh screen to obtain filtrate, namely protein liquid C hydrolyzed by the chromium-containing sludge, and filter residue, namely hydrolysis residue D; and drying the protein liquid C obtained by hydrolyzing the chromium-containing sludge by using an atomization dryer to obtain the protein powder. The chromium-containing sludge is treated by adopting the subcritical water, so that not only can protein in the chromium-containing sludge be recycled for resource utilization, but also the content of leached chromium in the chromium-containing waste can be reduced, and the environmental harmfulness of the chromium-containing waste is reduced.

The embodiment of the invention discloses a protein generation method and device, electronic equipment and a storage medium, and the method comprises the steps: firstly obtaining protein demand information which comprises attribute information and functional domain information corresponding to protein; and then, performing feature extraction on the attribute information to obtain protein feature data corresponding to the attribute information, and performing feature extraction on the functional domain information to obtain functional domain feature data matched with a functional domain corresponding to the functional domain information, so as to obtain the protein feature data according to the protein feature data and the functional domain feature data. And generating a target protein containing the functional domain corresponding to the functional domain information. According to the technical scheme, the protein with the specific functional domain can be generated, the matching degree between the generated protein and the protein demand information is improved, and the success rate of protein generation is improved.

The application provides a protein interaction prediction method and device, electronic equipment and storage medium, comprising: obtaining a first amino acid sequence corresponding to a first protein and a second amino acid sequence corresponding to a second protein; performing protein prediction based on the first amino acid sequence and the second amino acid sequence through a pre-trained target prediction network to obtain a protein prediction result; wherein the target prediction network comprises an amino acid embedding subnetwork, a vector encoding subnetwork and a prediction subnetwork, and the protein prediction result is used to represent the probability of interaction between the first protein and the second protein. The application can efficiently and accurately predict whether the proteins will interact, and can also realize cross-species protein interaction prediction.

The invention discloses a protein targeted degradation system based on a biological orthogonal protein pair and application of the protein targeted degradation system, and belongs to the field of biological medicine. Wherein the protein targeting degradation system comprises a ligand A targeting a tumor specific receptor, a ligand B targeting a tumor target protein and a biological orthogonal protein pair, and the ligand A and the ligand B respectively form a fusion protein with one protein in the biological orthogonal protein pair. The targeted degradation system provided by the invention can realize precise, efficient and safe degradation of tumor-related proteins (such as EGFR, PD-L1, HER2, c-MET and the like) through dual precise recognition of the targeting ligand and the bio-orthogonal protein pair, thereby realizing anti-tumor treatment.

The invention relates to a novel near-infrared bioluminescent protein based on a BRET principle, the near-infrared bioluminescent protein is formed by serially connecting and fusing a fluorescence activated protein NirF and a luciferase NLuc, and the fusion mode comprises NirF-N, N-NirF and NirF-N-NirF. According to the present invention, the near-infrared light with the wavelength of 680 nm can be emitted through the BRET by using the NLuc light-emitting substrate, such that the characteristics of low background signal, high BRET efficiency, strong near-infrared signal and the like are provided, and the ultra-high sensitivity is provided in the protein interaction detection. The invention also provides a gene of the near-infrared bioluminescent protein, a protein pair for bioluminescent resonance energy transfer detection, an expression box and a using method thereof, and cells, transgenic microorganisms and a kit for producing the near-infrared bioluminescent protein.

Systems and methods are disclosed for building a regression-based machine learning algorithm to predict native and near-native binding confirmations between two or more proteins. They include receiving training data, the training data representing a plurality of protein to protein docking poses based on one or more hotspots and a set of descriptors characterizing interaction interface features of one or more of the plurality of protein to protein docking poses. The systems and methods further specify a dedicated training data set based on a variety of single domain interactions. The training data set comprises an information space required to create dedicated regression and classification models in order to identify near native binding configurations. A specific and diverse set of interaction interface descriptors is calculated to provide the foundation for both, the regression and classification models.

The present invention relates to a compound of formula (I) (G-X-NH) o-Y wherein: G is a glycan comprising n monosaccharide units linked by a glycosidic bond; n is an integer of monosaccharide units connected through a glucosidic bond, preferably n is 2 to 200, more preferably 2 to 100, and most preferably 2 to 20 monosaccharide units; o is an integer of 0 to 10, if Y is a peptide or protein, Y corresponds to an integer of 1 to 10, preferably an integer of 1 to 5, per 10 kDa of peptide or protein; x is based on an aldose, linked via a glycosidic bond to G and wherein the aldehyde group of the aldose has reacted with the primary amino group of the group Y to obtain a product of formula (I) wherein the aldehyde group of the aldose has been reduced in form in formula (I) to the corresponding secondary amino group preferably by reductive amination to obtain the secondary amino group of formula (I); and Y is selected from amino acids, peptides and proteins.

This invention relates to the field of bioinformatics, and particularly to a method and system for predicting protein interaction using a two-branch protein sequence feature fusion approach. The method includes: acquiring a first protein sequence and a second protein sequence and vectorizing them respectively to generate corresponding primary features; performing dynamic channel attention enhancement processing on the first protein's primary features to generate enhanced features for the first protein; performing semantic embedding encoding processing on the second protein's primary features to generate semantic features for the second protein; fusing the enhanced features and semantic features to obtain a two-branch interaction feature set; constructing nodes of a graph isomorphic network using the elements of the set; generating protein pair structure embedding representations based on feature weighting aggregation of neighboring nodes; inputting these representations into a fully connected classifier; and outputting protein interaction prediction results. This invention effectively solves the problem that existing technologies cannot fully capture the lack of collaborative representation of multi-scale biological semantics in protein interaction prediction.

The present disclosure provides a protein docking method, apparatus, electronic device, and storage medium, relating to the fields of artificial intelligence technology, particularly biocomputing, molecular docking, and deep learning. The method comprises: in each round of iteration, docking a first protein and a second protein according to at least two molecular docking methods to generate a complex conformation; identifying when an iteration termination condition is not met, continuing the next round of iteration until the iteration termination condition is met, and obtaining a final complex conformation. Thus, protein docking can be achieved by comprehensively considering at least two molecular docking methods, and multiple rounds of protein docking can be performed, i.e., multiple rounds of complex conformation iteration can be achieved, thereby improving the accuracy of protein docking.

The invention relates to the technical field of computer-aided drug design, in particular to feature matching sampling and screening equipment based on a protein pocket, and aims to solve the technical problems that in the prior art, when protein pair data with extremely unbalanced positive and negative samples is processed, the sampling time is short; the generalization ability of the model is poor, and the correlation characteristics between the protein pairs cannot be effectively captured. The core of the invention comprises: a feature extraction module for obtaining feature representation of a protein pocket; the feature fusion module is used for explicitly modeling a bidirectional dependency relationship between protein pairs by adopting a cross attention network; the data rebalance system integrates the generative adversarial network to perform positive sample data enhancement, and adopts a popularity-based dynamic negative sampling strategy to screen high-quality negative samples; and a classification output module. According to the method, the accuracy and robustness of predicting the similarity of the protein pockets on highly heterologous and unbalanced data are remarkably improved, the AUC value can reach 0.848, and the method is superior to a traditional method.

The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilizing compound-protein machine learning representations to generate target results. For example, the disclosed systems can utilize a compound-protein interaction machine learning model to generate a compound-protein machine learning representation for compound protein pairs. The disclosed systems can utilize the compound-protein machine learning representation to train and utilize other target machine learning models in generating predicted bioactivity results. For example, the disclosed systems train a target machine learning model from compound-protein machine learning representations to generate ADMET predictions and / or biological perturbation program predictions. Furthermore, the disclosed systems can utilize one or more explainability models in conjunction with target machine learning models trained based on compound-protein machine learning representations to identify proteins that contribute to predicted bioactivity results.