46 results about "Protein function" patented technology

The invention discloses a drug target prediction method based on fragment-level local and global feature fusion, and belongs to the technical field of computational biology and artificial intelligence drug design. Comprising the following steps: acquiring a medicine SMILES character string and a protein amino acid sequence; respectively segmenting the drug SMILES character string and the protein amino acid sequence to obtain a drug structure fragment sequence and a protein functional fragment sequence; and inputting the drug structure fragment sequence and the protein function fragment sequence into a pre-trained drug-target interaction prediction model to obtain a prediction probability of drug-target pair interaction. Compared with the prior art, the method has the advantages that convolution feature extraction, a multi-head attention mechanism and a gating fusion strategy are combined, an end-to-end DTI prediction framework is constructed, and the interaction between drugs and targets can be comprehensively mined.

The invention relates to the technical field of artificial intelligence, and provides a protein function prediction method and device based on multi-modal protein data, and the method comprises the steps: obtaining protein multi-source data, carrying out the feature extraction of a protein sequence in the protein multi-source data, and obtaining a protein sequence feature; constructing a heterogeneous graph based on the protein multi-source data; performing feature coding on the heterogeneous graph by adopting a graph attention mechanism to obtain protein graph features; performing multi-modal fusion on the protein sequence features and the protein map features by adopting a gating fusion mechanism to obtain fusion features; and performing multi-label classification prediction based on the fusion features to obtain a protein function annotation result. The accuracy and robustness of protein function prediction can be improved, and the problems that in the prior art, multi-source protein data cannot be effectively integrated, and the method is sensitive to data noise are solved.

The application discloses a recombinant oncolytic virus targeting CD317 gene and application thereof in anti-tumor, and belongs to the technical field of tumor treatment. The recombinant oncolytic virus comprises a CD317 inhibitor and an oncolytic virus, and is formed by integrating the CD317 inhibitor into the oncolytic virus genome. The CD317 inhibitor is a substance capable of inhibiting CD317 gene expression or targeting degradation of CD317 protein function, and is selected from shRNA or siRNA targeting CD317. The application develops the oncolytic virus targeting knockdown of CD317 expression, inhibits tumor cell proliferation by reducing CD317 expression of tumor cells, reduces PD-L1 expression so as to break the immune escape mechanism, simultaneously enhances the killing sensitivity of tumor cells to CD8+ T cells, forms a synergistic effect with the oncolysis of the oncolytic virus, and the recombinant oncolytic virus has stronger in-vivo anti-tumor activity, thereby providing a new potential scheme for CD317-driven tumor treatment.

The invention relates to a method for producing an animal model of heart failure. The method comprises the step of weakening or deleting DDB1 protein function in myocardial cells of the animal model. The present application demonstrates that nuclear DDB1 co-agglomerates with MEF2C to control NAD + biosynthesis as well as ion homeostasis genes in the heart, and the lack of which results in the development of HFpEF. Development of HFpEF in a'double strike 'mouse model can be reversed through AAV-mediated DDB1 overexpression in myocardial cell nucleuses. Therefore, it is detected that DDB1 coordinates NAD + biosynthesis and ion homeostasis to protect the heart from being affected by ejection fraction retention heart failure caused by obesity, and the scheme of the application has therapeutic significance on treatment of obesity / diabetes HFpEF.

A protein active site prediction method based on geometric graph neural network belongs to the field of bioinformatics and protein structure analysis. First, the original protein data is preprocessed by multi-modal feature extraction and geometric graph construction, and ProtT5 deep embedding and physicochemical properties are fused. Then, a deep ActiveSiteGNN model with explicit geometric perception ability is constructed, and the stacked geometric encoder and geometric edge update layer are used to dynamically capture the micro three-dimensional spatial features. Next, a multi-task collaborative optimization and dynamic threshold search strategy is designed, combined with weighted sampling to solve the serious sample imbalance, and the best decision threshold is selected based on the validation set in real time. The integrated reasoning and graph diffusion smoothing technology is introduced to globally calibrate the prediction probability distribution based on the biological space prior. Finally, the evaluation is carried out on the independent test set. The method has strong structure perception ability and provides a feasible solution for accurate prediction of protein functional sites.

This invention discloses a drug target affinity prediction method that integrates PPI quality and uncertainty. The method constructs a drug molecule map and a multimodal protein structure representation, and extracts multi-source features by combining the local PPI sub-map of the target protein. By calculating the protein's low-frequency level, prediction uncertainty, and PPI quality, a PPI quality-aware gating factor is generated to adaptively adjust the PPI information injection intensity, and a residual enhancement strategy is used to preserve the original protein features. Subsequently, the drug representation and the enhanced protein representation are fused using adaptive gating, and the result is input into a prediction network to output the drug-target affinity. This method effectively integrates protein function and interaction information, improves the prediction stability of low-frequency proteins and the model's generalization ability, and provides an accurate and reliable computational tool for drug screening and candidate molecule selection.

The invention discloses a method for creating a male sterile line and a maintainer line of maize based on a CRISPR-Cas12i. 3 system and application of the method. The invention belongs to the field of genetic breeding, and particularly relates to a method for creating a male sterile line and a maintainer line of maize based on a CRISPR-Cas12i. 3 system and application of the method. The method for preparing the transgenic corn comprises the following steps: mutating an Ms26 gene of a receptor corn genome to cause Ms26 protein function deletion to obtain the transgenic corn, and the transgenic corn has at least one of the following characteristics: (1) compared with the receptor corn, the tassel of the transgenic corn shows compact spikelet, tight glume protection and incapability of normal pollen scattering, and the transgenic corn has the characteristics that the Ms26 protein function deletion is caused by the mutation of the Ms26 gene of the receptor corn genome; no pollen is exposed; and (2) compared with the receptor corn, the transgenic corn is completely aborted. By establishing an efficient backcross transformation method, the genic male sterile line without transgenic ingredients is rapidly obtained, the breeding period is greatly shortened, and the application cost is reduced.

The invention relates to the technical field of medical detection, and particularly discloses a biomarker combination for identifying cervical cancer molecular subtypes, a kit and application. The biomarker combination comprises a protein CDH13, a protein TP53BP1, a protein NNMT and a protein HSPB1, and molecular subtype correlation analysis is carried out on a sample by detecting relative expression characteristics of the proteins in a cervical cancer in-vitro sample. Based on the relative expression characteristics of each protein, a cervical cancer sample can be divided into at least one of an epithelial-mesenchymal transition related subtype, a proliferation related subtype, an immune response related subtype and an epithelial differentiation related subtype. The invention also provides a detection kit and an analysis system for realizing cervical cancer molecular subtype identification. According to the technical scheme, the cervical cancer can be subjected to molecular typing from the protein function execution level, a reliable technical means is provided for molecular typing research, prognosis evaluation and accurate treatment related research of the cervical cancer, and the application prospect is good.

The invention discloses a PRRSV (Porcine Reproductive and Respiratory Syndrome Virus) N protein monoclonal antibody as well as a preparation method and application thereof, belongs to the technical field of biological materials, and solves the problem that in the prior art, the stability and specificity of a PRRSV monoclonal antibody screening result are not ideal enough. The method comprises the following steps: constructing a gene of a PRRSV N protein into a plasmid, and performing expression and purification to obtain a recombinant N protein; uniformly mixing the recombinant N protein, a Modifer reagent, a Quencher reagent and a DR-B reagent, centrifuging, taking supernate, re-suspending the supernate in an SB reagent, and separating out biotinylated N protein through a membrane; and screening the positive B cells combined with the biotinylated N protein by using a Hypercell high-throughput single cell screening platform to obtain the monoclonal antibody. The monoclonal antibody aiming at the PRRSV N protein is prepared by a single B cell screening technology, and a foundation is laid for PRRSV diagnosis and related research of N protein functions.

The present application discloses a protein marker for early pregnancy detection of mammals, and particularly relates to a hidden marker protein serum amyloid A of early pregnancy test paper. Protein sequencing used in the present application adopts liquid chromatography-mass spectrometry (LC-MS) technology, sample correlation is compared through Venn, sample correlation, PCA and PLS-DA analysis; protein function annotation is carried out through GO, KEGG, COG and Pfam, and subcellular localization prediction is carried out; difference protein, protein set and difference metabolite are calculated and counted; correlation analysis of proteome and transcriptome is carried out, difference protein of sheep serum in early pregnancy is obtained, and a key marker protein of early pregnancy of sheep is screened, which provides a basis for unknown early pregnancy detection of sheep.

This invention discloses a protein sequence spatial compression and functional optimization method based on a large-scale model, belonging to the fields of artificial intelligence and proteomics. This invention mines potential amino acid substitution sites in consensus sequences and then controls the sequential substitution process using a large protein language model, thereby maintaining the functional stability of proteins during sequence substitution and subsequently screening for substitution combinations that effectively enhance protein function. Introducing a large protein model transforms protein sequences into embedding vectors representing protein structure, function, and physicochemical properties. By analyzing the embedding vectors during the substitution process, it is possible to prevent new proteins from deviating from their original function and basic structure due to substitution. This invention combines consensus substitution identification with large-scale model analysis, effectively compressing the sequence space of amino acid substitutions, thereby significantly improving the efficiency of protein design and modification.

The invention provides a protein sequence structure joint design method based on a natural language, and relates to the technical field of artificial intelligence, and the method comprises the following steps: constructing natural language information representing protein functions, an amino acid secondary structure and an amino acid contact diagram according to protein design requirements; processing the natural language information through a text encoder to generate condition features; meanwhile, the triangular perception encoder is used for processing the amino acid secondary structure and the amino acid contact diagram information, and condition features are generated; a protein map structure is constructed through multiple pieces of modal information, and local and global context features of protein are captured; iteratively updating the amino acid sequence and three-dimensional structure coordinates of the protein by using an equivariant decoder; according to the method, invariant cross entropy loss, invariant frame alignment loss and contrast loss are utilized to perform joint optimization on generation of protein sequences and structures, and after multiple rounds of iteration, protein amino acid sequences and three-dimensional structures with'sequence-structure-function 'consistency are output.

The application provides a renaturation tag for purifying inclusion body proteins, and a gene-derived product and application thereof, and belongs to the technical field of protein purification. The application provides a renaturation tag P67, and the amino acid sequence is at least one of SEQ ID NO:1-SEQ ID NO:3, wherein the 33th amino acid is lysine, glutamic acid or proline. The renaturation tag P67 is fused with target proteins for expression in prokaryotes, so that the recombinant expressed inclusion body (insoluble inclusion body) is successfully renatured and has biological activity. The renaturation tag P67 establishes a foundation for subsequent protein function research and application transformation.

The invention relates to an anthrax protein nanopore system and application thereof in protein sequencing, and belongs to the technical field of nanopore detection. The invention aims to solve the problems of low flux, insufficient accuracy and limited nanopore resolution in the existing protein sequencing technology. According to the invention, an anthrax protein PA63 heptamer nanopore electrochemical detection device is constructed, an asymmetric pH buffer system is adopted, Trypsin, Calpain-1, Pancreative Elatase, CarboxyceptidaseA enzyme and the like are added step by step for polypeptide enzyme digestion, voltage is applied to drive amino acid to pass through nanopores, current signals are collected, amplitude, retardation time and frequency characteristics are analyzed, and polypeptide amino acid sequence detection is realized. The nanopore is small in opening current (fA level) and high in resolution ratio, detection accuracy and sensitivity are enhanced by combining multi-enzyme step-by-step cutting and carving, and a direct and efficient sequencing means is provided for protein function research.

This invention belongs to the field of biotechnology, and particularly relates to the use of the SPON2 gene or its protein as a target in the diagnosis or treatment of endometriosis. Supported by multi-omics and single-cell level data and tested with clinical samples, this invention, for the first time, elucidates the correlation between SPON2 and endometriosis (EM), revealing that high expression of SPON2 in eutopic and ectopic endometrial fibroblasts in endometriosis patients enhances their proliferation, invasion, and migration abilities. Inhibiting SPON2 gene expression or inhibiting SPON2 protein function reduces the proliferation and invasion abilities of endometrial tissue, as well as lesion migration, invasion, and fibrosis, thus improving the condition of endometriosis. This suggests that the SPON2 gene or its protein can serve as a diagnostic or therapeutic target, providing new ideas and directions for the clinical diagnosis and targeted therapy of endometriosis.

The present application relates to a kind of protein function prediction method and system based on deep mixed structure and topological framework, the method is realized to the comprehensive, complementary representation of protein function information by the multi-modal feature fusion of sequence, local structure and multi-scale topology.Especially introduced topological feature, it can capture the complex spatial configuration (such as hole, channel) that traditional distance contact diagram is difficult to describe, these configurations are directly related to ligand binding, ion transport and other key functions, so as to significantly improve the accuracy of prediction.Topological feature is used as the gate attention mechanism of dynamic query, can adaptively strengthen the residue feature related to functional site, while inhibiting the interference of irrelevant or noise feature, so that model is more focused on function-related structure area.Double-path pooling generates protein global representation, so that local focus path pays attention to the key residues after modulation, and global context path retains complete sequence evolution information.

The invention relates to the technical field of medicines, in particular to a compound for targeted degradation of mIDH1 protein and application of the compound. According to the invention, a series of compounds with a 'PL-Linker-EL' ternary structure are creatively designed and synthesized, so that a fundamental treatment strategy breakthrough from traditional'protein function inhibition 'to direct'pathogenic protein removal' is realized. The compound can be combined with mutant IDH1 (mIDH1) protein and E3 ubiquitin ligase in a targeted manner at the same time, and then mIDH1 is induced to be subjected to ubiquitination modification and is thoroughly degraded through a proteasome way, so that the problem of drug resistance caused by mutation is expected to be overcome, and a new treatment choice is provided for patients who are invalid or relapse in the existing therapy.

This invention provides a method for predicting protein methylation sites that integrates quantum and region-aware technologies, comprising the following steps: Step 1, obtaining protein sequences as data sources and constructing training and independent test sets respectively; Step 2, constructing multimodal features for each protein sequence using a three-way nested scattering network, fusing the multimodal features to obtain an optimized fused feature tensor; Step 3, inputting the optimized fused feature tensor into a RaQMeNet network model to perform methylation site prediction. This invention not only significantly outperforms existing technologies in terms of performance indicators but also possesses stronger adaptability, stability, and interpretability. It can be widely applied in multiple bioinformatics and biomedical fields such as protein functional annotation, disease mechanism research, and drug target discovery, demonstrating promising application prospects and commercial value.

This invention relates to the field of plant molecular breeding technology, and discloses a tomato NOR‑like1 The application of genes in breeding tomato varieties that are easy to deseed and process, the aforementioned NOR‑like1 The gene is an encoding a NAC transcription factor, the coding sequence of which is shown in SEQ ID NO: 1, or a nucleic acid sequence that has at least 90% sequence identity with the sequence shown in SEQ ID NO: 1 and has the function of regulating the development of the trichomes of tomato seed coat; by reducing or eliminating the above... NOR‑like1 Breeding goals are achieved by adjusting gene expression levels or protein function in tomato plants. The resulting tomato plants exhibit the absence of seed coat hairs on the seed surface, preventing adhesion between adjacent seeds, and producing large fruits. This invention fundamentally eliminates seed coat hairs on tomato seeds at the genetic level, and has broad application potential in breeding tomato varieties with easily separable seeds from the pulp and easily separable seeds.

The invention discloses a transcriptional activation type RUBY visual virus infection system as well as a method and application thereof. The system is based on a betacyanin biosynthesis pathway, a transcriptional activation system is utilized, and a yeast GAL4 DNA binding domain (BD) and a herpes virus VP16 transcriptional activation domain (AD) are fused and constructed in a virus vector; when the virus infects expressed transgenic tobacco, the expression of the RUBY whole gene can be activated, so that a red signal is generated. The construction method of the system is clear, complex instruments are not needed, an efficient and convenient tool platform is provided for protein function analysis, host factor screening and infection mechanism exploration in virology research, and good expandability is achieved.

The present invention relates to: a Cas protein function-regulating switch oligonucleotide, which is an oligonucleotide that hybridizes complementarily to a spacer of a guide RNA and consists of 5 to 20 nucleotides starting from the 5' end; and a target nucleic acid amplification composition comprising same. In addition, the present invention provides the Cas protein function-regulating switch oligonucleotide, which is an oligonucleotide that hybridizes complementarily to a spacer of a guide RNA and consists of 5 to 20 nucleotides starting from the 5' end, thereby regulating the rate at which a Cas protein binds to a target nucleic acid so as to cause an RPA-CRISPR reaction to occur within a single tube without physical separation or additional steps, and thus can be used in a point-of-care diagnostic device.

The invention discloses a protein allosteric site mutation effect prediction system based on an improved relation network, and the system extracts the sequence and structural features of a wild-type allosteric protein and a mutant-type allosteric protein through a protein language model, and generates a comprehensive feature matrix through the fusion of a cross-modal attention mechanism and a gating network. According to the improved relation network, a mixed attention module is introduced, prototype branches are added on the basis of original relation branches, double branches are formed, and accurate classification prediction of the mutation effect is achieved. According to the method, the focusing capability of mutation difference features can be enhanced, and the prediction precision and generalization capability of the model under a small sample condition can be improved; meanwhile, through compound loss function optimization, model convergence is further accelerated, the discrimination performance is improved, and efficient and reliable technical support is provided for protein function research and targeted drug design.

The invention relates to a construction method of a database for discriminating proteolytic enzyme restriction enzyme cutting sites. Comprising the following steps: S1, acquiring family classification information and restriction enzyme cutting site data of proteolytic enzyme from an MEROPS database, acquiring sequence and function annotation data of substrate protein from a UniProt database, and acquiring three-dimensional structure data of the substrate protein from an AlphaFold database; s2, migrating and integrating the data in the S1 to the local; and S3, dividing the processed data into four correlative core data tables, and storing the four core data tables in a local database. According to the method disclosed by the invention, by combining MEROPS, UniProt and AlphaFold databases, the restriction enzyme cutting site of the proteolytic enzyme is accurately predicted by adopting an artificial intelligence algorithm. Compared with a traditional method, the method has the advantages that the restriction enzyme cutting sites can be recognized more efficiently and more accurately, and the protein function analysis speed and accuracy are remarkably improved.

This invention belongs to the fields of bioinformatics and protein function identification, specifically relating to a machine learning-based method for identifying viral RNA silencing repressors. The method includes: obtaining the amino acid sequence of the viral protein to be tested, inputting it into a pre-trained ESM-2 model to extract deep feature vectors, then inputting the feature vectors into a trained XGBoost classifier, and outputting an identification result indicating whether the viral protein is a viral RNA silencing repressor. This invention integrates the automatic feature extraction capability of ESM-2 with the efficient classification performance of XGBoost, significantly improving prediction accuracy and generalization ability. It is mainly used in fields such as plant antiviral genetic engineering, disease-resistant breeding, and biopesticide development, enabling rapid screening of potential viral RNA silencing repressors and providing an efficient tool for functional gene mining.

The application provides a complex disease genetic risk assessment model construction method, comprising the following steps: S1, collecting research samples, wherein the research samples comprise patient samples and healthy person samples; S2, genome sequencing and data processing, comprising whole genome sequencing and mutation site analysis, mutation site annotation, and identification of rare genetic variations having a destructive impact on protein function; S3, statistical analysis, obtaining characteristic genes significantly related to whether a complex disease is suffered from; and S4, constructing a complex disease genetic risk assessment model. The application aims at the clinical diagnosis problem of complex diseases, and proposes a risk assessment strategy combining clinical experience and statistical inference for complex diseases with scarce data; the optimal risk assessment model is tested by using completely independent sporadic population data, and the generalization and application prospect of the complex disease risk assessment strategy are verified.

The invention discloses an integrated model method for predicting protein allosteric sites based on transfer entropy and energy contusion resistance, and belongs to the technical field of protein functional site prediction. Firstly, an allosteric protein data set is collected and arranged, and a training set and an independent test set are constructed. The method comprises the following four steps: 1, marking normal sites and allosteric sites verified by experiments according to information of a protein allosteric database; 2, extracting the sequence, the structure, the network topology, the dynamics and the energy blocking characteristic of the protein; 3, integrating feature information of neighbor residues by adopting a spatial neighborhood feature aggregation method, then performing oversampling processing to balance sample categories, and obtaining an optimal feature subset through feature selection; and 4, integrating a plurality of basic models, and realizing prediction of the protein allosteric sites in a soft voting mode. In the aspect of feature extraction, the transfer entropy is introduced for the first time to explore the information transfer relationship between a predicted site and a normal site, and meanwhile, the energy blocking feature is introduced to improve the ability of the model to identify high-blocking residues of which the local structure change can cause significant change of the overall energy of the protein; the high-inhibition residues are often closely related to a protein allosteric effect. Besides, the invention provides a spatial neighborhood feature aggregation method, and the spatial neighborhood feature aggregation method is applied to a protein allosteric site prediction task, so that the capturing capability of the model on spatial neighborhood residue feature information is effectively enhanced; the SVM-SMOTE oversampling method is adopted to solve the problem of unbalanced sample types.

The invention discloses a separated RUBY visual virus infection system as well as a construction method and application thereof. According to the system, a key gene of a betacyanin biosynthetic pathway is split into two parts: CYP76AD1 and a GT gene are connected in series through a 2A peptide sequence and are constructed in a plant stable expression vector, and a stably inherited transgenic plant is obtained in nicotiana benthamiana; and cloning another key gene DODA into a plant virus vector to construct a recombinant virus expression vector. According to the method, real-time, lossless and in-situ visual observation of the virus infection and movement process is achieved, the system construction method is clear, complex instruments are not needed, an efficient and convenient tool platform is provided for protein function analysis, host factor screening and infection mechanism exploration in virology research, and good expandability is achieved.

The application belongs to the technical field of fruit tree molecular marker assisted breeding, and particularly relates to a dCAPS molecular marker based on variation of a gene coding region, a primer pair, a kit and application thereof. MdTRX-M4 The molecular marker is located on the 11th chromosome of an apple GDDH13 v1.1 genome MdTRX-M4 The nucleotide sequence is shown as SEQ ID NO. 1, W represents polymorphism, which is A or T, the SNP causes amino acid variation. Compared with the existing promoter region marker, the marker is located in the coding region, directly regulates fruit fructose content by affecting protein function, has stronger effect, higher stability, 100% detection accuracy, and can improve the molecular marker assisted selection efficiency at the seedling stage.