70 results about "Protein recognition" patented technology

Disclosed are a protein encoded by a gene having a nucleotide sequence represented by any of SEQ ID NOs: 1 to 65 or a fragment thereof, an antibody recognizing the protein or antigen-binding fragment thereof, and a polynucleotide having a sequence comprising at least 12 consecutive nucleotides of a nucleotide sequence represented by any of SEQ ID NOs: 1 to 65 or a nucleotide sequence complementary thereto. The gene and the protein of the invention is useful for diagnosing and treating cancer.

The invention discloses a temperature-sensitive protein molecular engram monolithic column and a preparation method and application thereof, belonging to high molecular material preparation technology and application thereof in the field of protein separation and analysis, and particularly relating to a novel protein molecular engram monolithic column. A simple and moderate method is adopted to combine an inorganic silica gel monolithic column skeleton, protein serves as a template, and a temperature-sensitive functional monomer and cross-linking agent ingraft protein engram material on the surface of the inorganic silica gel so as to obtain the temperature-sensitive protein molecular engram monolithic column which has stronger template protein recognition capability. The temperature-sensitive protein molecular engram monolithic column prepared with the method of the invention combines the single identification characteristic of molecular engram with the characteristic of on-line separation of the monolithic column, has simple operation, moderate condition and low cost and provides a new method for removing high-abundance protein for the Journal of Proteome Research.

Method of diagnosing in an individual recent exposure to an agent which is a pathogen, vaccine or any other moiety which induces a cellular response, said method comprising determining in vitro whether the T cells of the individual recognise a protein from said agent having a length of at least 30 amino acids, to a greater extent than one or more peptide epitopes from the agent, a greater extent of recognition of the protein indicating that the individual has recently been exposed to the agent.

The invention discloses a key protein recognition method based on tensor random walking. The method comprises the steps that a protein mutual impacting network topological structure, protein structural domain information, genetic expression information based on time sequence and protein homologous information are obtained; a correlation relationship of different protein nodes in the mutual impacting of protein nodes is constructed according to the above information; hub scores of protein nodes are initialized according to the protein homologous information; a tensor model is established according to the correlation relationship of different protein nodes in the protein mutual impacting; the hub scores of each protein nodes which are obtained by iterative computation based on the tensor model are sorted, and the top n protein nodes are taken as key proteins. The method has the advantages that the method is simple and effective, and compared with other methods, tests on multiple data sets show that the method has better prediction performance on key protein recognition.

The invention relates to an algorithm for identifying a key protein based on a genetic algorithm in a PPI network. The algorithm comprises the steps of generating an initial population in a protein interaction network; calculating the fitness of individuals; performing selection operation by a roulette wheel method; performing crossing operation and mutation operation among the randomly selected individuals; and performing local optimization on multiple individual solutions. According to the algorithm, the defects of existing methods are overcome; indexes are optimized and bio-information is fused, so that the reliability is higher and lots of unnecessary calculations are reduced; and the predicted key protein can be locally optimized, so that the efficiency of key protein identification is improved, and the application range and practicality of the technology in the field of the bio-information are expanded and improved.

Disclosed is an evaluation peptide for evaluating the efficiency of a pretreatment in the quantification of a protein using a mass spectrometer, having high reliability and high general versatility. Also disclosed is an artificial standard protein comprising the evaluation peptide. Further disclosed is a method for quantifying a protein utilizing the artificial standard protein. Specifically disclosed is a method for selecting a peptide which consists of an amino acid sequence not agreeing with that in a naturally occurring protein and a variant thereof and capable of being detected by mass spectrometry and which has an amino acid that can be recognized by a protein-digesting enzyme, and using the peptide as an evaluation peptide for use in the quantification of a protein by a mass spectrometer.

The invention discloses a method for identifying a key protein by adopting an improved flower pollination algorithm. A protein-protein interaction network is converted into an undirected graph, previous Q proteins having the largest choice degree serve as pollen individuals, the PeC centrality is measured, compound information is determined, subcellular localization information is determined, pollen significance is evaluated, a pollen position is updated, and an identified key protein is output. The topological attributes of the protein network are considered and the biological characteristicsof the protein network are fused during evaluation of the pollen significance, and the key protein can be accurately identified. A simulation experiment result shows that the index properties such asaccuracy, specificity and sensitivity are more excellent. Compared with other key protein identifying methods, the key protein identifying process is achieved by combining with the topological attributes and biological characteristics of the protein network, and the key protein identifying accuracy is improved.

Provided are a method for generating a data set for integrated proteomics analysis, whereby expression level variations of both of proteins and genes can be integrally united together and, moreover, highly accurate and appropriate analysis results can be obtained compared with the existing cases where the expression variation amount of proteins or genes is singly analyzed, an integrated proteomics analysis method, a method for identifying a protein causative of a disease or the like using these methods, and a method of using the same???. The aforesaid method for generating a data set for integrated proteomics analyses comprises: a protein identity number-assigning step for assigning common identity numbers to the expression variation amount data of individual proteins; a gene identity number-assigning step for assigning common identity numbers to the expression variation amount data of individual genes; a data-binding step for binding together the set of the expression variation amount data; a data-rejecting step for rejecting, among the individual expression variation amount data constituting the thus bound data set, data showing a p-value equal to or greater than a specific level; and a data-selecting step for selecting one data, from a set of data having the same common identity number assigned thereto, on the basis of a definite requirement to thereby generate the data set to be subjected to integrated proteomics analyses. Further, the data set for integrated proteomics analyses thus generated is subjected to GO analysis and network analysis to thereby identify a protein causative of a disease, a pathological condition or the like. Furthermore, the causative protein thus identified is usable, for example, a tumor marker or a clinical target.

The invention discloses a fabrication method for a protein electrochemical imprint sensor of a modified magnetic electrode by a one-step deposition method, and relates to the technical field of electrical analysis chemistry and protein recognition sensors. The fabrication method comprises the steps of 1) preparing a graphene@Fe3O4 nanometer composite material; 2) fabricating a graphene@Fe3O4@Au modified magnetic electrode by the one-step electrodeposition method; 3) introducing boric acid, amino and carboxyl active group into a surface of the modified magnetic electrode so that a plurality ofrecognition sites are provided for protein imprint; 4) pre-assembling protein; 5) performing electrochemical polymerization on a protein imprint membrane; and 6) washing template protein with an eluent so as to build the protein molecular imprint electrochemical sensor. The magnetic modified electrode fabricated by the method is beneficial for introducing the active group, increasing the recognition sites, improving selectivity, expanding surface area and improving conductivity of electrode surface, the built protein imprint electrochemical sensor is high in sensitivity and can be used repeatedly, and target protein can be specially recognized.

The invention belongs to the technical field of gene engineering and discloses a method for knocking out fungus genes. The method includes: respectively introducing nucleotide sequences identified by Vika recombinase into the upstream and downstream of target genes in fungus genomes; identifying protein with Vika recombinase activity, shearing the nucleotide sequences identified by the Vika recombinase, and knocking out the target genes to obtain fungi with the target genes being knocked out. The method has the advantages that a Vika-vox recombinase system is successfully applied to fungus gene knocking out, knocking out of the target genes is achieved, a new method is provided for fungus gene knocking out, and function researches and performance optimization of fungus genes are benefited.

The invention discloses a polyionic liquid gel with nanopores and a method thereof. The polyionic liquid gel has a monomer structure shown as a formula (1), wherein in the formula (1), R is an N heterocyclic ring and comprises one of FORMULAE, X comprises Br-, Cl- or FORMULA and n is 6-30. The polyionic liquid gel has nanopores, comprises a three-dimensional hyperbranched topological structure, shows excellent adsorption capacity, and has a huge application potential in the field of dyes, heavy metal ion adsorption, controlled medicine release, molecular imprinting, protein recognition and thelike.

A process is provided of delivering at least one active agent cargo molecule into an neuronal cell whereby a cargo molecule is placed within a synthetic vesicle such as a liposome and a biotinylated protein such as an antibody is bound to the synthetic vesicle to form a protein bound synthetic vesicle whereby the protein recognizes a receptor expressed on the surface of a neuronal cell, and exposing the protein bound synthetic vesicle to the cell until the cargo molecule is delivered into the neuronal cell. Numerous cargo molecules are delivered by the inventive synthetic vesicle including a calpain inhibitor and a caspase inhibitor. The protein illustratively targets a cellular receptor for a ligand such as glutamate, glycine, dopamine, nicotine, muscarine, acetylcholine, or serotonin, and the like.

SimRank is used for carrying out protein similarity calculation based on a key protein identification method in an uncertain protein interaction network. A SimRank calculation problem in an uncertainnetwork is converted into SimRank calculation in a deterministic network; and then, considering the topological characteristics of the protein interaction network and the biological characteristics ofthe protein, and calculating an edge clustering coefficient, gene ontology similarity, a Pearson's correlation coefficient and a subcellular localization score to obtain an importance score. And finally, ranking the scores from large to small, and outputting the first k proteins corresponding to the scores as final results. On the basis of an uncertain interaction network, the accuracy of key protein recognition is improved by fusing biological attributes and topological characteristics, meanwhile, the prediction result is more accurate, the prediction efficiency is improved, the applicationrange of the technology in the field of biological information is widened, and the practicability of the technology in the field of biological information is improved.

The invention discloses a key protein identification method based on a capsule neural network and ensemble learning. The key protein identification method comprises the following steps: 1, extractingeight biological characteristics of protein in a protein interaction network by utilizing a Cytoscape tool; 2, extracting deep enhanced features of the eight biological characteristics by using a capsule neural network; 3, connecting the biological characteristics with the protein enhancement characteristics; 4, putting the connected characteristics obtained in the step 3 into an integrated modelMulti-enseable, training the model, and predicting a new key protein by using the trained integrated model; 5, outputting a result. Compared with the initial biological characteristics, the enhanced characteristics extracted through the capsule neural network can improve the accuracy of predicting the key protein by some machine learning models, and the accuracy of predicting the key protein by the machine learning model can be further improved by fusing the initial biological characteristics and the enhanced characteristics.

The invention discloses a method for ovarian cancer screening based on an ovarian cancer marker and a logic gate operation, and belongs to the technical field of biomedicine. The protein-nucleic acidrecognition technology, the signal amplification technology and a logic gate are combined, aptamers of CEA and CA125 are used for target recognition, signal transformation and amplification are performed through a G-quadruplex-heme complex, and a method for simultaneous detection of tumor markers is established. According to the method, an optical biosensor with high sensitivity and strong specificity is constructed for achieving the joint detection of the CEA and the CA125, and an ovarian cancer screening result is provided based on the logic gate; through the combination of optical detectionand the signal amplification technology, the detection sensitivity is improved; the universality of the detection principle makes the method also suitable for detection of different objects based ondifferent protein recognition elements.

The invention discloses a method for preparing glycosylation nanometer fiber for protein recognition and applying the same. The method comprises the following steps that: acrylonitrile group copolymer is used as a raw material; acrylonitrile group copolymer nanometer fiber with controllable diameter is prepared by means of electrostatic spinning; sugar monomer which is protected by acetyl is fixed on the surface of nanometer fiber under the catalysis of boron trifluoride etherate; acetyl group is stripped down with methanol solution of sodium methoxide; and affinity nanometer fiber for glycosylation is obtained. With high specific surface area of the nanometer fiber, high porosity and a special biological function of glycosyl, the glycosylation nanometer fiber with a high immobilization rate can be immersed directly into protein solution, or the glycosylation nanometer fiber is superposed in multilayer to arrange in a membrane exchange filter, which is used for separation and purification of the protein. The glycosylation nanometer fiber for protein recognition has the advantages of simple preparation method and, repeatable use for many times and is favorable for industrialization production and so on.

The invention relates to a key protein identification method based on protein clustering characteristics and activity co-expression. The method comprises the following specific steps: describing the clustering characteristics of a protein interaction network by utilizing an edge aggregation coefficient; setting gene activity expression by setting a threshold parameter, and describing by adopting aBoolean value; defining a calculation method based on the Boolean value of gene activity expression, and calculating the score of activity co-expression by utilizing a Jaccard coefficient; finally obtaining the key comprehensive scores based on the protein clustering characteristics and the activity co-expression, outputting a sorting result, and using the protein with the high key comprehensivescore after top sorting (top N is taken as a threshold value) as a key protein. According to the key protein identification method, the influence of gene expression data noise is eliminated, and the key protein identification method is superior to a centrality measurement method and a key protein prediction method with the same input data set in the aspects of identification accuracy, specificity,sensitivity and the like.

The invention discloses a method for predicting association relationship between metabolites and diseases based on a KATZ model. The method is characterized by converting known metabolite and diseaserelationship into a relationship network, calculating corresponding disease and metabolite similarity, constructing disease and metabolite similarity networks, predicting the relationship between metabolites and diseases through the KATZ model, verifying the accuracy of the predicted relationship through several cross validation methods, and further verifying the feasibility of the method throughcase analysis. The method can predict a new relationship between the metabolites and diseases, and a part of the relationship is verified by literatures and is not recorded by a database temporarily;verification results show that AUC index performance is excellent; and compared with other key protein recognition methods, the method improves the accuracy in mining potential relations of the prediction method by fusing the biological characteristics (semantic similarity) and topological characteristics (Gaussian kernel similarity) of the diseases.

The invention provides a method for constructing necessary protein recognition between multi-dimensional biological attribute information and PPI topological characteristics by using a deep neural network, and in order to solve the problem that gene expression data is not comprehensive, absent data is supplemented so as to improve robustness. And the convergence speed of the deep neural network is reduced by respectively constructing a PPI network topology structure, a Pearson's correlation coefficient and a homologous correlation coefficient. And finally, searching an optimal incidence relation of three features of the degree of the node, the Pearson's correlation coefficient and the homologous correlation coefficient through the deep neural network, thereby improving the recognition precision of the essential protein.

Methods for identifying a peptide by analyzing a linear readout representative of at least a portion of at least two amino acids along the peptide using a machine learning model, wherein the machine learning model is trained on linear readouts representative of a set of peptides of known sequence are provided. Methods of training a machine learning model on linear readouts representative of a set of known peptides, and systems for performing the methods of the invention are also provided.

A screening method for a specific protein in a proteome analysis comprises: (a) obtaining samples containing a protein or protein digest from a cell or tissue in a specific group and a control group; (b) analyzing the samples obtained in the step (a) with a mass spectrometer, thereby obtaining mass spectrometry data; (c) analyzing the mass spectrometry data obtained in the step (b) using an arbitrary database searching software, thereby acquiring a protein list containing items for specifying proteins and indexes for identifying the proteins, for each of the samples; (d) averaging values of the indexes for each of the items in all of the protein lists acquired in the step (c), and acquiring protein list models of the specific group and the control group, containing the average values of the indexes; (e) calculating a difference between the average values for each of the items, between the protein list models of the specific group and the control group obtained in the step (d), and acquiring one protein list in which the items are rearranged in the order of the difference between the average values; and (f) selecting a protein with a large difference between the average values, from the protein list acquired in the step (e).