114 results about "Protein function" patented technology

The invention provides a protein methylation site prediction method fusing quantum and region perception, which comprises the following steps: step 1, acquiring a protein sequence as a data source, and respectively constructing a training set and an independent test set; 2, constructing a multi-modal feature for each protein sequence by adopting a three-way nested scattering network, and fusing the multi-modal features to obtain an optimized fusion feature tensor; and step 3, inputting the optimized fusion feature tensor into a RaQMeNet network model, and performing a methylation site prediction task. The performance indexes of the method are greatly superior to those of the prior art, and the method has higher adaptability, stability and interpretability, can be widely applied to a plurality of bioinformatics and biological medicine related fields such as protein function annotation, disease mechanism research and drug target discovery, and has good application prospects and commercial values.

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 provides a genetically engineered bacterium and a construction method of the genetically engineered bacterium, and the genetically engineered bacterium is characterized in that a green fluorescent protein sfGFP is used for mediating protein glutaminase, PG (protein glutaminase), PGF (protein glutaminase), PGF, PGF, PGF, PGF, PGF, PGF, PGF, PGF, PGF, PGF, PGF, PGF, PGF, PGF, PGF, PGF and PGF; eC 3.5. 1.44) is efficiently secreted and expressed in engineering bacteria, and belongs to the technical field of biological engineering. The method comprises the following steps: an obtained sfGFP gene is derived from an NCBI database (GenBank numbering: CP035486.1), an obtained PG zymogen gene PP (Propeptide-Protein glutaminase) is derived from a Chryseobacterium prion prgA gene (GenBank numbering: AB046594.1) in the NCBI database, a recombinant plasmid pHT01 / sfGFP-PP is introduced into bacillus subtilis WB800N to obtain an engineering bacterium, extracellular secretion expression of PP is realized, the sfGFP-PP is activated by trypsin to obtain PG, and the PG is subjected to enzyme activation to obtain the recombinant bacillus subtilis. The expression quantity and the secretion efficiency of the PG in the bacillus subtilis are greatly improved. The recombinant PG produced by the method can generate 23.5 U / mL enzymatic activity under activation of trypsin, can be used for improving protein functional characteristics, and has relatively high industrial production and application values.

The invention provides application of ancient-Chinese health-preserving essence in preparation of a protein regulating agent, and belongs to the technical field of biological medicines. The invention provides application of an ancient-Chinese health-preserving essence or an ancient-Chinese health-preserving essence extract in preparation of a reagent for regulating protein functions. Protein comprises at least one of NCAM2, NCAM1, MAP2, VGLUT1, CDK5, DCLK1, TUBB3 and apolipoprotein E. The invention also provides an application of the ancient-Chinese health-preserving essence or the ancient-Chinese health-preserving essence extract in preparation of a reagent for regulating protein functions. In ancient and Chinese health preserving essence positive feedback regulation proteins, an important nerve synaptic function network exists and comprises proteins such as NCAM2, NCAM1, MAP2 and the like, and the proteins play an important role in biological processes such as neural development, synaptic shaping, neural signal transmission and the like. In addition, the ancient-Chinese health-preserving essence can regulate the expression of APOE. The application of the invention not only helps to deepen the scientific understanding of the pharmacodynamic effect of the ancient-Chinese health-preserving essence, but also provides a theoretical basis for the modern and precise application of traditional Chinese medicine compounds.

The invention provides a protein function topology design method based on SE (3) isovariant diffusion and ex-situ generation and a product, and relates to the technical field of protein design. According to the method, geometric deep learning and generative artificial intelligence are fused, isovariant generation and optimization of protein function sites under the action of a three-dimensional Euclidean space (SE (3) group) are achieved, and the limitation of traditional protein design on conformation sampling efficiency, function guidance and physical realizability is broken through.

The invention discloses a protein function prediction method based on multi-modal fusion and a dynamic label network, and belongs to the technical field of biological information, an adjacent matrix of a label association network can be smoothly updated in a model training process, and the protein function prediction method can be used for predicting protein functions by extracting various modal information of protein. The redundant relation among multiple modes is removed, the prediction effect of protein functions is improved, meanwhile, a training method combining a protein function association network and a tag association network is used, the influence of multiple tags on protein function prediction is considered, and protein function prediction is achieved by fusing the sequence, structure and structural domain information of protein. According to the method, the complementarity of various data is fully utilized, so that the prediction capability of the model is improved, and compared with a traditional method using a static label relationship, the scheme dynamically updates the label relationship in model training, so that the generalization capability of the model is further improved.

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 invention relates to the field of bioinformatics, and provides a protein function prediction method and system based on deep learning. The method comprises the following steps: constructing a multi-modal data set of protein; performing feature extraction on the multi-modal data set through an encoder to obtain multi-modal features; training a neural network through the multi-modal features to obtain a prediction model; and predicting the function of the to-be-detected protein through the prediction model to obtain a prediction result. The protein function prediction accuracy is improved.

The invention discloses a prediction method, and particularly relates to a protein DNA binding tendency prediction method based on a multi-modal biomolecular model, which comprises the following steps: S1, inputting a protein sequence; s2, predicting a three-dimensional structure; s3, secondary structure calculation is carried out; s4, calculating the accessibility of the solvent; s5, performing protein function annotation; s6, generating feature representation; step S7, sample representation; s8, obtaining a training set; s9, generating all protein residue samples; step S10, constructing a protein map; step S11, building a graph neural network; step S12, adjusting through a convolutional layer; step S13, training and storing the model; and step S14, outputting a PWM matrix. The invention relates to a protein DNA binding tendency prediction method based on a multi-modal biomolecular model, which can reduce the calculation cost of the existing protein DNA binding tendency prediction method and improve the recognition accuracy.

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.

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

The invention provides a method for inserting an unnatural amino acid and application thereof, the method adopts a pyrrolysine aminoacyl-tRNA synthetase mutant as an orthogonal translation element to introduce the unnatural amino acid into a protein to obtain a protein containing the unnatural amino acid, the invention also provides an in-vitro cell-free protein synthesis system for inserting the non-natural amino acid, and the system can efficiently introduce the non-natural amino acid, especially the lysine analogue non-natural amino acid. The technical bottleneck that a natural translation system is low in non-natural amino acid recognition efficiency is solved, and an efficient and controllable technical tool is provided for protein function research and biological medicine development.

The application discloses a kind of quick site analysis method and detection system of biotin labeled KRAS protein, belong to intelligent analysis technical field;Construct the site fluorescence intensity database under historical detection state, obtain the fluorescence intensity data of each amino acid site;Based on the data, construct the amino acid site-fluorescence intensity coordinate system of each sample, generate fluorescence intensity fluctuation curve;Extract the wave crest and wave trough of curve, construct fluorescence peak site pair set and valley site pair set;Obtain the mode of peak site pair and valley site pair in all samples, as high response site and low response site, calculate its probability of occurrence in new sample, and combine the peak value and valley value of real-time sample with preset threshold, output high response type, low response type or stable type classification result.The application realizes the quick, objective, intelligent discrimination of KRAS protein function state by historical data probabilistic modeling and fluctuation curve feature extraction.

Systems and methods are provided that can analyze protein sequences using natural language processing (NLP) models to, for example, detect structurally similar proteins in a database of unclassified proteins. Systems and methods for applying a secondary model to tune the NLP model during a training phase are also provided. Systems and methods for training the secondary model using a hierarchically classified database are also provided. Thus, the NLP model is tuned to output an embedding vector indicative of structural characteristics of a protein corresponding to the input protein sequence.

The invention discloses a bombyx mori protein function prediction multi-modal fusion method based on a deep residual network, and the method comprises the steps: collecting the sequences and high-resolution three-dimensional structure data of bombyx mori proteins of different varieties and at different development stages from databases of bombyx mori genomes, protein crystal structures and the like by employing a data crawling and interface calling technology; and constructing a multi-modal data source. The sequence data are cleaned and coded in a one-hot mode, and the structural data are optimized and converted into a graph structure. Sequence and structure features are respectively extracted through a convolutional neural network and a GraphSAGE graph neural network, and the features are dynamically fused through an adaptive weight fusion strategy. And building a deep residual network training model, and realizing accurate prediction of the protein function category and confidence of the bombyx mori with unknown functions. According to the method, the precision and the high robustness of silkworm protein function prediction are improved through full-process technical optimization, and an efficient tool is provided for silkworm molecular breeding and functional genomics research.

The present invention relates to the field of biomedicine and specifically relates to an anti-VEGF-anti-PD-L1 bispecific antibody, a pharmaceutical composition of same, and uses thereof. Specifically, the present invention relates to the bispecific antibody, which comprises: a VEGF-targeted first protein functional area and a PD-L1-targeted second protein functional area, wherein: the first protein functional area is an anti-VEGF antibody or an antigen-binding fragment thereof, or, the first protein functional area comprises a VEGF receptor or a fragment having a VEGF receptor function, and the second protein functional area is an anti-PD-L1 monoclonal antibody. The bispecific antibody of the present invention is capable of activating the immune system and blocking tumor angiogenesis at the same time, and provides great antitumor prospects.

The present disclosure is generally related to filamentous fungal strains comprising genetic modifications that give rise to recombinant cells having altered phenotypes, wherein such recombinant cells are particularly well-suited for growth in submerged cultures (e.g., for the large-scale production of proteins of interest). Certain embodiments of the disclosure are related to, inter alia, compositions and methods for designing, constructing, testing, screening, growing, fermenting, and the like recombinant filamentous fungal strains expressing intein-modified fitness proteins.

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 relates to the technical field of prevention and control of agricultural and forestry pests, and discloses a synergistic protein ORF76 of insect baculovirus and application of the synergistic protein ORF76. According to the invention, a synergistic protein (ORF76) is obtained from an insect biocontrol resource, i.e., a Pinus fumosa nuclear polyhedrosis virus, and protein function verification shows that the protein has a remarkable synergistic effect on a fall webworm nuclear polyhedrosis virus, an apocheima cinerarius nuclear polyhedrosis virus and a Pinus fumosa nuclear polyhedrosis virus; the compound can be used as a synergistic factor to be added into insect viruses, and has important significance on prevention and control of major forestry pests (fall webworms, spring inchworm and smoke wing pine bees).

The invention belongs to the technical field of biological agriculture, and particularly relates to Hongyoumai TaRGA4 gene as well as a coding protein and application thereof. According to the Hongyoumai TaRGA4 gene and the application thereof, the full length of a Hongyoumai TaRGA4 gene coding region is obtained from a local powdery mildew high-resistance variety Hongyoumai for the first time, the nucleotide sequence of the TaRGA4 gene coding region is disclosed, the sequence is shown as SEQ ID NO: 2, the sequence of Hongyoumai TaRGA4 protein is shown as SEQ ID NO: 3, the gene and protein functions are further researched through a gene silencing technology, and the application of the gene and the protein is developed. The gene and the protein have the function of regulating and controlling the wheat powdery mildew resistance and can be applied to improvement of the wheat powdery mildew resistance, and a new biological resource is provided for prevention and treatment of plant powdery mildew.

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 utility model relates to the technical field of protein function identification, in particular to a protein function identification device. According to the technical scheme, the pesticide spraying device comprises a mounting panel, a fixing plate, a shell and a pesticide storage box, wherein the shell and the pesticide storage box are mounted on the top end face of the mounting panel; a pump body is mounted on the bottom end surface of the inner wall of the pesticide storage box; a dropping pipette is arranged on the pump body, and one end, deviating from the pump body, of the dropping pipette is embedded and fixed in the shell; a third driving mechanism is installed on the rear end face of the shell, and a lead screw is arranged on an output shaft of the third driving mechanism. A guide rod is welded to the inner wall of the shell. A first driving mechanism is installed on the top end face of the fixing plate, and a containing frame is arranged on an output shaft of the first driving mechanism. And a test tube is sleeved in the placing rack. The utility model meets the requirements of protein function dosing identification, and avoids inconvenience and pollution caused by probing and taking out during placement during identification.

Systems, methods, and apparatus for generating a DNA / gene sequence output, given a protein sequence input. In one aspect, the protein-to-DNA mapping system includes a protein language model (embedder) that generates a high-dimensional protein embedding from a low-dimensional protein sequence input; and the system includes a protein-to-DNA translator that processes the protein embedding and generates a DNA sequence output, based on the protein embedding. The protein-to-DNA translator is a neural network (e.g., a Seq2Seq model) and is configured with translation coefficients that may be trained with a training dataset, comprising the training protein and DNA embeddings. Training the translation coefficients can include supervised learning that maps the protein embedding (input) to a DNA sequence (target). The systems, methods, and apparatus may increase protein yield / expression, improve protein functionality, enhance gene therapy efficacy, streamline synthetic biology design, and / or broaden functional genomics studies, all leading to reduced costs, better performance, and increased accessibility.

Compositions comprise statistically random heteropolymers complexed with active proteins, and are formulated and used in stimuli-responsive materials and nanoreactors composed of proteins and synthetic materials.

A protein function identification method, a system, a terminal, and a storage medium. The method comprises: acquiring a bulk protein sequence and a bulk protein function corresponding to the bulk protein sequence, inputting the bulk protein sequence into multiple deep learning pre-trained models, respectively, and outputting multiple multi-dimensional feature vectors; performing feature fusion processing on the multiple multi-dimensional feature vectors to obtain a fused feature vector, and training a bi-directional long short-term memory model on the basis of the fused feature vector and the bulk protein function to obtain a protein function identification model; and acquiring a protein sequence to be identified, inputting said protein sequence into the protein function identification model, and outputting a protein function identification result. Feature vectors of a protein sequence are extracted by using multiple different deep learning models, and a bi-directional long short-term memory model is trained to obtain a protein function identification model, such that rapid and accurate identification of protein functions can be realized.