77 results about "Protein Interaction Networks" patented technology

The invention discloses a colorectal cancer drug relocation method based on multi-omics integration. The system comprises a multi-omics data acquisition and preprocessing module, a tumor microenvironment analysis module, a specific disease network construction module, a multi-dimensional drug relocation module and a result evaluation module. And the tumor microenvironment analysis module comprises cell heterogeneity identification, cell map construction, cell annotation and tumor cell subset annotation. The specific disease network construction module comprises tumor feature expression program extraction, expression program screening, meta-program construction, clinical related meta-program recognition and specific disease protein interaction network construction. And the multi-dimensional drug relocation module comprises a module for identifying diseases by using a random walk algorithm, carrying out drug screening based on disturbance data, carrying out drug screening based on network proximity and carrying out comprehensive drug relocation. From the perspective of single cell data, element programs related to colorectal cancer survival are excavated, corresponding modules are designed, and the efficiency and precision of colorectal cancer targeted drug screening are improved.

The invention provides a rapid quantitative analysis method for an outer vesicle proteome, and belongs to the technical field of quantitative analysis of the outer vesicle proteome. According to the rapid quantitative analysis method for the outer vesicle proteome, firstly, a high-quality outer vesicle sample is obtained through low-speed centrifugation, ultra-speed centrifugation and membrane filtration; and obtaining proteome data by utilizing two-dimensional electrophoresis and mass spectrometry. Then, a mathematical model equation set is constructed for preliminary analysis, a protein interaction network is optimized through an embedded game optimization index model, and feature extraction and quantitative analysis are conducted through a convolution parameter adaptive function and a residual network deep learning model; finally, a quantitative analysis result containing the protein expression level and the function importance is generated in combination with the multi-dimensional protein expression contribution index, and the technical problem that in the prior art, in the rapid quantitative analysis process of the outer vesicle proteome, the protein component complexity is high, and consequently the quantitative precision is low is solved.

The invention discloses a disease-related anomaly localization protein prediction method based on deep learning, and the method comprises the steps: carrying out the protein prediction based on the proteomics expression data of normal and disease samples and a known protein interaction network in a normal state; respectively constructing a protein interaction network under the activity characteristics of the sample pathway and the disease state; the proteomics expression data and the pathway activity characteristics are fused through a cross attention mechanism, and protein characterization characteristics with pathway perception ability are constructed; respectively predicting protein subcellular localization under the normal and disease states by using a graph attention network model based on the protein characterization characteristics and the protein interaction network corresponding to the normal and disease states; disease-related abnormal localization proteins are identified by comparing predicted protein subcellular localization in normal and disease states. The method can efficiently and accurately identify the abnormal localization protein related to the disease, and has important scientific research value and application prospect.

The application provides a method for analyzing the synergistic mechanism of hepatotoxicity and nephrotoxicity of non-steroidal anti-inflammatory drugs. The method comprises the following steps: preliminary toxicity prediction of NSAIDs and collection of toxicity target points, collection of liver and kidney disease target points, then cross and screening of the target points to obtain core target points and common core target points of NSAIDs induced liver and kidney diseases, and then constructing a protein interaction network of the common core target points; enrichment analysis of the common core target points to obtain the common action pathway of NSAIDs induced liver and kidney diseases; finally, further screening of the common core target points to obtain the key target points of NSAIDs induced liver and kidney diseases, and verification by using molecular docking technology. Compared with the traditional method, the advantages of the method are: first, the method does not depend on large-scale patient clinical data and a large number of animal or cell experiments, avoiding the ethical controversy in animal experiments and human experiments; second, the method can identify the potential cross-pathway and synergistic toxicity mechanism when a compound triggers multiple diseases, which is helpful for more comprehensive evaluation of the toxicity risk of NSAIDs.

The embodiment of the invention provides a drug optimization method and system based on the overall effect of a protein network. The method comprises the following steps: constructing a protein interaction network related to a target disease, and dividing each protein target into a risk protein set and a protection protein set; respectively calculating first binding affinity data of the candidate drugs and each protein target in the risk protein set, and generating a first network comprehensive score based on the first binding affinity data; respectively calculating second binding affinity data of the candidate drugs and each protein target in the protection protein set, and generating a second network comprehensive score based on the second binding affinity data; calculating network confrontation scores of the candidate drugs according to the first network comprehensive score and the second network comprehensive score; and determining whether the candidate drug is a preferred drug based on the network adversarial score. The method can overcome the defect that a single-target drug is insufficient in curative effect due to a network compensation effect, so that safer and more effective candidate drugs are screened out.

The disclosure provides a traditional Chinese medicine efficacy evaluation method and device based on a node-weighted network, equipment and a storage medium. The method determines target disease protein targets and corresponding target weights based on the comprehensive scoring results of candidate disease protein targets by a public database and a large language model. When determining the protein targets of medicinal materials and the corresponding protein target weights of medicinal materials, the prescription ratio, chemical component information, and the interaction probability between the chemical components and the protein targets of each medicinal material in the traditional Chinese medicine prescription to be evaluated are comprehensively considered. The target disease protein targets and the corresponding disease protein target weights, as well as the protein targets of medicinal materials and the corresponding protein target weights of medicinal materials, are added to a pre-constructed protein interaction network. The obtained node-weighted network focuses on the real pharmacological basis, effectively improves the network biological interpretation, and the multi-dimensional network index determined accordingly is used to evaluate the regulation effect of the traditional Chinese medicine prescription to be evaluated on the target disease, which is more accurate.

The invention discloses core components, targets and action mechanisms of Zhibai Dihuang pills / decoction for treating early diabetic nephropathy based on a directed PPI network and a plurality of network centrality algorithms. Core components and targets of the Zhibai rehmannia pill / decoction for treating diabetic nephropathy are determined through the steps of screening active components of the Zhibai rehmannia pill / decoction, collecting target information corresponding to the active components, collecting disease related targets, obtaining drug-disease intersection genes, constructing a directed protein interaction network and the like. The action mechanism of the Zhibai rehmannia pill / decoction for treating diabetic nephropathy is researched. The invention provides a scientific theoretical basis for the treatment of diabetic nephropathy by the Zhibai Dihuang pill / decoction, and provides a new method for the clinical treatment and scientific research of the Zhibai Dihuang pill / decoction on diabetic nephropathy.

The invention discloses a method for drawing a human full-coverage protein interaction network based on digital PCR next-generation sequencing. After the advanced pedestrian 293T cell line obtains mRNA, a modified random primer or an oligo-dT primer is adopted to obtain a cDNA library; carrying out homogenization treatment on the human cDNA library; the method comprises the following steps: transforming plasmids of a BACTH bacteria double-hybrid system to obtain transformed plasmids; carrying out homologous recombination on the transformed plasmids of the sample library and the double-impurity system, and introducing into escherichia coli for screening to obtain positive PPI clones of the library with interaction; and carrying out digital PCR-based next-generation sequencing on the screened clones to draw the human full-coverage protein interaction network. The method is suitable for performing high-throughput screening after thousands of positive PPI combinatorial clones are obtained by'library-to-library 'bacteria or yeast double hybrids, identifying the same cell bar code cDNA combinatorial pairs, and realizing PPI network identification in different species, among species and in hybridization technology system extensive scenes.

This invention discloses a molecular docking analysis method for obtaining core targets based on the RCSB database, comprising: screening drug-disease-immunity intersection targets through multiple databases; screening core targets through protein interaction network analysis and multi-topology algorithms; obtaining core target structure files from the RCSB database and downloading active ingredient structure files from the PubChem database; performing molecular docking through the CB-DOCK2 database; screening effective binding pairs using binding energy as an indicator and visualizing the results; and finally outputting the results through functional annotation and pathway enrichment analysis. This method improves the accuracy of core target screening and the reliability of molecular docking through multi-database integration, multi-algorithm collaboration, and multi-dimensional evaluation, forming a complete technical chain and providing efficient technical support for the analysis of the mechanisms of action of traditional Chinese medicine compound prescriptions.

The present invention discloses a method, device, terminal, and medium for predicting synthetic lethal gene pairs based on a graph convolutional neural network. The synthetic lethal gene pair prediction method includes: obtaining protein structural features based on protein structure data; obtaining protein sequence features based on protein sequence data; obtaining protein functional features based on a protein-protein interaction network; merging and standardizing the protein structural features, sequence features, and functional features to obtain the gene features of the primary protein-producing genes; obtaining interactions between genes, and training a synthetic lethal gene pair prediction model based on a graph convolutional neural network using the gene interactions and gene features; obtaining a final feature representation for each gene based on the trained synthetic lethal gene pair prediction model, and predicting whether two genes form a synthetic lethal gene pair based on the final feature representation. This method improves the efficiency of feature extraction and the ability to predict gene interactions.

The application relates to a cancer driver gene explainable identification method based on trust calibration and prototype learning, and relates to the technical field of biological information identification. A gene graph is constructed by fusing a protein interaction network and gene multi-omics characteristics, and part of nodes are labeled. Label-aware message passing is performed through a trust calibration encoder, the neighborhood is split into a labeled part and a non-labeled part for independent calibration, and node embedding is adaptively fused. An angle margin prototype classifier is used to construct a class prototype on a hypersphere, the decision boundary is expanded, and a prediction result is output. A pivot node self-supervised regularizer is introduced, center nodes are screened from labeled driver genes, positive constraints are applied to neighbor non-labeled nodes, negative penalties are applied to non-neighbors, and a supervised boundary is maintained when non-labeled data is used. Finally, a structured explanation module is used to reuse the internal evidence of the model, a verifiable explanation is provided for prediction, and the unification of high precision and credible explanation is realized.

The invention relates to a novel chemical cross-linking agent with mass spectrum fragmentable trehalose disaccharide as a skeleton structure and a preparation method thereof. The cross-linking agent disclosed by the invention has the following characteristics: 1) trehalose disaccharide is used as a skeleton structure, so that the cross-linking agent has excellent biocompatibility; 2) the trehalose skeleton has a pair of symmetrical mass spectrum fragmentable glucosidic bonds, so that a cross-linked peptide fragment can be simplified into a conventional peptide fragment modified by a cross-linking agent fragment; 3) the enrichment of the cross-linked peptide fragment can be realized by the trehalose skeleton under the condition of not adding an enrichment handle; and 4) active groups of the cross-linking agent comprise but not limited to a plurality of reactive groups such as succinamide ester, diaziridine, phenylsulfonyl fluoride, hydrazide group, amino group, hydroxylamine group and the like, and chemical cross-linking of a plurality of amino acids except lysine is realized. The trehalose cross-linking agent disclosed by the invention is applied to the field of proteomics, and provides technical support for realizing large-scale analysis of a protein complex in a complex sample, spatial structure analysis of protein and a protein-protein interaction network.

A method for identifying cell subpopulations associated with disease phenotypes, belonging to the biomedical field. To identify cell subpopulations associated with disease phenotypes, this invention collects single-cell RNA sequencing data of the disease to obtain a single-cell expression matrix, collects the bulk expression matrix of the disease and corresponding phenotypic tags, and downloads human protein-protein interaction data to construct a protein-protein interaction network; extracts gene signature features of cells and samples and maps them to the protein-protein interaction network to form corresponding cell modules and sample modules; calculates the distance between each cell module and each sample module, and determines a set of multiple sample modules as the sample module set of the disease phenotype; calculates the distance between cell modules and the sample module set of the disease phenotype; creates a background distance distribution to evaluate the statistical significance of the distance between cell modules and the sample module set of the disease phenotype, and identifies cells whose distance to the sample module set of the disease phenotype is significantly smaller than the background distance distribution.

Methods and systems for generating drug repurposing predictions for a disease caused by a pathogen, such as a novel pathogen, are provided. A multi-modal system includes a protein-protein interaction network (PPI), a graph neural network (GNN), a diffusion module, a proximity module, and an aggregation module. The GNN is configured to predict new edges between candidate drug nodes and disease nodes in an embedded representation of the PPI to produce a decoded embedding space. The diffusion module is configured to determine a proximity distance for pairs of nodes in the PPI, and the proximity module is configured to determine a proximity distance for pairs of nodes in the PPI, each pair comprising a pathogen-protein node and a drug-protein node. A ranked list of candidate drugs predicted to be effective in treatment of the disease based on candidate drug lists generated by the other modules is generated by the aggregation module.

The application discloses a method for predicting different metabolic states of cells in cancer and metabolic characteristic subtypes of the cells based on a metabolite-protein interaction network, and the method comprises the following steps: (1) identifying the classification and metabolic state of the cells: preprocessing data, filtering low-quality cells; performing twice principal component analysis according to key genes; performing twice dimension reduction clustering on the cells respectively; (2) predicting metabolic characteristic subtypes of a queue according to the proportion of different metabolic states of cells in the sample. The method can identify the metabolic state of the cells and metabolic subtypes, and provides a new perspective for cancer research.

A method for establishing and predicting a chemical potential biological target point model, the method for establishing comprising: obtaining a protein interaction network and transcriptome data containing differential genes under the influence of a chemical; diffusing the differential scores of the differential genes to adjacent gene nodes along the protein-protein interaction network to obtain diffused differential gene nodes; calculating the average shortest distance between each gene node and the diffused differential gene nodes; sorting the gene nodes in the protein interaction network based on the average shortest distance to obtain a target protein prediction list; adjusting the diffusion parameters until the target protein prediction list meets a first prediction accuracy condition; and selecting a cutoff value of the target protein prediction list that meets the first prediction accuracy condition so that the target protein prediction list before the cutoff value meets a second prediction accuracy condition. The prediction model can quickly and accurately predict potential biological target points.

The invention discloses a combined product based on NanoBiT luciferase three fragments, a method and application. The combination comprises a first fusion protein, a second fusion protein, and a third fusion protein. According to the combined product, NanoBiT luciferase is divided into three fragments, the three fragments are fused with different protein fragments, it is ensured that all the fragments of NanoBiT luciferase are integrated into a protein interaction network, therefore, non-specific signals are reduced, the biological authenticity and reliability of experimental results are improved, and compared with a traditional NanoBiT luciferase two-fragment system, the combined product has the advantages that the combined product is simple in structure and convenient to use, and the application range is wide. The light-emitting signal of protein interaction is obviously enhanced, and a more accurate tool is provided for in-vitro research of protein interaction.

A traditional Chinese medicine intelligent screening method based on network targeting comprehensive indexes belongs to the technical field of traditional Chinese medicine intelligent screening. The method comprises the following steps: constructing a target set A and a protein interaction network database corresponding to each traditional Chinese medicine; constructing a disease core gene set B based on a protein interaction network; calculating a network targeting comprehensive index based on the target point set A and the disease core gene set B; calculating the network targeting comprehensive index of each traditional Chinese medicine and then performing ascending sorting, wherein the smaller the network targeting comprehensive index is, the stronger the network targeting relevance between the traditional Chinese medicine and the disease is; and matching the traditional Chinese medicines according to the obtained ascending order, and generating a structured and explainable final recommendation report. According to the method, the disease-related gene identification accuracy is improved, the biological interpretation of the result is enhanced, the standardized processing of integrating multi-source data into intelligent recommendation is realized, and the urgent demand of efficient and accurate screening in modern research of traditional Chinese medicines is met.

The invention discloses an action mechanism analysis method of an external medicine for treating liver cancer ascites, and relates to the technical field of medicines. Comprising the following steps: screening active ingredients and related targets of a medicine through network pharmacology; predicting liver cancer ascites related disease targets; constructing an intersection target point network of active ingredient target points and disease target points, and performing protein interaction network analysis; carrying out GO function and KEGG pathway enrichment analysis on the intersection target spot; establishing a medicine, component, disease, target spot and pathway network; the binding activity of the core target and the active component is verified through molecular docking; establishing a liver cancer ascites model through animal experiments, and performing efficacy verification; network pharmacology and animal experiment data are integrated, and a drug action mechanism is analyzed. According to the invention, active ingredients, target spots and pathways of the medicine are rapidly screened through network pharmacology, so that the earlier-stage research period is greatly shortened; animal experiment design is standardized, samples can be processed in batches, and the method is suitable for high-throughput drug screening.

The invention provides a network pharmacology-based large model generated traditional Chinese medicine prescription curative effect evaluation method and system, and the method comprises the steps: employing a machine learning model to generate candidate prescriptions, and obtaining all known active components of each traditional Chinese medicine in the prescriptions; predicting human body protein targets corresponding to the patient based on the known active components, and gathering all the human body protein targets to obtain a prescription target set P; obtaining human body protein targets related to the corresponding diseases, and gathering all the human body protein targets corresponding to the diseases to obtain a disease target set D; constructing a protein-protein interaction network; inputting the prescription target point set P and the disease target point set D into a protein-protein interaction network, and constructing a connected sub-network; calculating curative effect indexes of the connected sub-networks; and optimizing the prescription based on the curative effect index to complete the curative effect evaluation of the traditional Chinese medicine prescription. According to the method, calculation and experiments are closely combined, the blindness and the cost of experimental verification are remarkably reduced through a calculation priority strategy, and the research and development efficiency is improved.

The present invention provides a method for reconstructing a weighted protein interaction network and a method for predicting protein complexes. The method for reconstructing a weighted protein interaction network comprises: step S1: checking the weight consistency index of the link weight matrix of a given weighted protein interaction network; step S2: keeping the edge conditions of the original weighted protein interaction network unchanged, selecting the entire original weighted protein interaction network as a training set, and reconstructing the weight information in the edges of the original weighted protein interaction network only through a link weight prediction algorithm based on a weight perturbation model and a latent factor model to obtain a new weighted protein interaction network. After adopting the above technical solution, data enhancement of the protein interaction network is achieved, the protein interaction network is optimized, and the prediction effect of the protein complex is improved.

The invention discloses an antibody specifically bound with arabidopsis thaliana blue light receptor cryptoflorin CRY1 and application thereof, and belongs to the technical field of biomedicine, the antibody has high specificity and high affinity when bound with arabidopsis thaliana CRY1 protein, and does not have cross reaction with arabidopsis thaliana CRY2, human CRY family protein and other species of photolyase / cryptoflorin, and the antibody can be used for detecting the photolyase / cryptoflorin of the arabidopsis thaliana blue light receptor cryptoflorin CRY1. The antibody can effectively solve the problems that an existing CRY1 detection method is poor in specificity, insufficient in sensitivity, unstable in result and the like, and a reliable tool is provided for expression dynamic tracking, subcellular localization and protein interaction network analysis of arabidopsis thaliana CRY1 protein.

The application discloses a high-throughput general sample protein interaction screening method. The method comprises the following steps: firstly, constructing a general sample open reading frame library, extracting mRNA of a target sample, and then obtaining a cDNA library by using a modified random primer or an oligo-dT primer; then, uniformly processing the general sample cDNA library; modifying plasmids of a double-hybrid system to obtain modified plasmids; homologously recombining the general sample library and the modified plasmids of the double-hybrid system, and introducing them into organisms to screen and obtain organisms with an interaction library; identifying single-cell PPI pairs of the screened organisms, and constructing a general sample protein interaction network. The application is suitable for screening and identifying the same Barcode cDNA combination pairs by using single-cell sequencing after obtaining tens of thousands of positive PPI combination clones by means of a "library-library" bacterial or yeast double-hybrid system, and realizing PPI network identification in different intra-species, inter-species and hybridization technology systems.

The invention provides a traditional Chinese medicine curative effect evaluation method and device based on a node weighted network, equipment and a storage medium. According to the method, a target disease protein target and a corresponding target weight are determined on the basis of a comprehensive scoring result of candidate disease protein targets by a public database and a large language model. When the medicinal material protein target and the corresponding medicinal material protein target weight are determined, the prescription proportion and the chemical component information of each medicinal material in the to-be-evaluated traditional Chinese medicine prescription and the interaction probability between the chemical component and the medicinal material target are comprehensively considered. A target disease protein target, a corresponding disease protein target weight, a medicinal material protein target and a corresponding medicinal material protein target weight are added to a pre-constructed protein-protein interaction network, and an obtained node weighted network focuses on a real pharmacological basis, so that the biological interpretation force of the network is effectively improved; the accuracy of evaluating the regulation effect of the to-be-evaluated traditional Chinese medicine prescription on the target disease by using the determined multi-dimensional network index is higher.

The invention relates to the technical field of biological medicine, and discloses a keratin related gene and application thereof in regulation and improvement of psoriasis, and the keratin related gene comprises KRT25, KRT28, KRT85 and KRTAP11-1. The invention also discloses application of the endocrine related gene in regulation and improvement of psoriasis, and the endocrine related gene is a CGA gene. By maintaining the expression level of the KRT25 gene, the KRT28 gene, the KRT85 gene, the KRTAP11-1 gene and / or the CGA gene, the psoriasis is relieved. The invention focuses on a dark green module, through GO / KEGG enrichment analysis, protein interaction network (PPI) construction and experimental verification, the molecular mechanism of OMT is further discussed, a network which takes keratin as a center and is related to endocrine (CGA gene) is clear as an effective node for psoriasis treatment, and a foundation is laid for discovery of a new target for psoriasis treatment.

The application discloses a method for drawing a human full-coverage protein interaction network based on digital PCR and second-generation sequencing. First, mRNA is obtained from a human 293T cell line, and then a cDNA library is obtained by using a modified random primer or an oligo-dT primer; then, the human cDNA library is uniformly processed; the plasmid of a BACTH bacterial double-hybrid system is modified to obtain a modified plasmid; the sample library and the modified plasmid of the double-hybrid system are homologously recombined and introduced into E. coli to screen positive PPI clones with interaction; and the screened clones are subjected to second-generation sequencing based on digital PCR to draw a human full-coverage protein interaction network. The application is suitable for high-throughput screening after obtaining tens of thousands of positive PPI combination clones by using a "library vs. library" bacterial or yeast double-hybrid system, and is suitable for identifying the same cell barcode cDNA combination to realize PPI network identification in different intra-species, inter-species and hybridization technology systems.

The invention discloses a method for analyzing an action mechanism of a plasticizer acetyl tri-n-butyl citrate (ATBC) for inducing breast cancer based on network toxicology. The method comprises the following steps: firstly, integrating multiple databases to obtain and standardize ATBC targets, and identifying disease-related genes in combination with differential expression of TCGA data and weighted gene co-expression network analysis (WGCNA); overlapping target spots are obtained through intersection of the three, a protein interaction network is constructed, and function enrichment analysis is carried out. TCGA is used as a training set, GEO is used as a verification set, random forest and Lasso regression are combined to screen out core targets MAOA and ADRA2A, and a prognosis model is constructed. And finally verifying that the ATBC can be stably combined with the two target spots through molecular docking (the combination energy is 1t;-5.0 kcal / mol). According to the invention, a full-process scheme from target prediction, function analysis, machine learning screening to molecular docking verification is established, and a standardized normal form is provided for the study of the carcinogenic mechanism of environmental chemicals.

The invention relates to a traditional Chinese medicine effective component screening method based on a chemical fingerprint spectrum and multi-omics combined analysis. The method comprises the following steps: preparing a semen cuscutae-fructus lycii medicine pair extract and establishing a UPLC-Q-TOF-MS / MS chemical fingerprint spectrum; detecting the levels of estradiol, luteinizing hormone, anti-mullerian hormone and follicle-stimulating hormone in the premature ovarian insufficiency animal model; screening characteristic components related to the drug effect through multivariate statistical analysis; performing transcriptome sequencing on the ovarian tissue, and combining GO / KEGG analysis to determine a key pathway; integrating database prediction targets and differential genes, screening hub genes and constructing a protein interaction network; and finally verifying the binding activity of the candidate component and the core target protein through molecular docking and molecular dynamics experiments. According to the invention, systematic screening and action mechanism analysis of drug effect related components are realized, and a new technical approach is provided for mass marker determination and estrogen simulating action research of the dodder-wolfberry drug pair.

The invention discloses a method for analyzing the anti-gastric cancer effect of baicalein by combining network pharmacology and Mendel randomization. The method comprises the following steps: S1, collecting potential target spots of baicalein; s2, obtaining exposure data and outcome data; s3, Mendel randomization analysis is carried out, and MR analysis is carried out by using a TwoSampleMR software package of R; s4, performing difference analysis; s5, establishing a protein-protein interaction network: importing the potential action target information obtained in the step S3 into a String database to obtain protein interaction data so as to obtain a PPI network diagram, analyzing a result by using Cytoscape Version 3.9. 1 software, constructing the protein interaction network, and screening a hub gene (TOP10) scored by Clustering Coefficient by using a Cytohubba plug-in; s6, carrying out enrichment analysis on GO and KEGG pathways; s7, molecular docking verification: taking the screened gene target as a receptor, finding a receptor 3D structure file in a PDB database, taking baicalein as a ligand, and finding a ligand 3D structure file by utilizing a PubChem database; molecular docking is carried out through AutoDock software, and a docking result is visualized through PyMOL software.