84 results about "Protein pair" patented technology

Methods, apparatus, media and signals for identifying associated cell signaling proteins are disclosed. The method involves producing and storing a comparison value for each pair of the cell signaling proteins in response to data values representing physical properties of respective cell signaling proteins. The method further involves identifying cell signaling protein pairs having comparison values satisfying a condition indicative of an association between the cell signaling proteins.

The invention relates to a method and a system for predicting a protein interaction target point of a drug. The method comprises: 1) collecting a human protein interaction network and single protein target point data of the drug, and constructing an interactive protein target point data set of the drug; 2) obtaining description data of the drug and proteins; 3) constructing a bigraph for representing an interactive relationship between the drug and a protein pair, constructing a similar matrix for representing drug similarity and protein pair similarity, establishing a kernel function for correlating the similar matrix of the drug and the protein pair, and establishing a prediction model through a machine learning algorithm; and 4) performing independent set testing by utilizing unknown drug and interactive protein pair, and predicting a possibly existent unknown drug protein interaction target point, and verifying a prediction result through database and document retrieval. According to the method and the system, the search space of the drug target point can be expanded and the more specific drug protein interaction target point with the best classification performance can be obtained.

The present invention relates to the field of therapeutic methods to screen for compounds on the basis of their ability to influence Wnt activity. The screening process is applied to both a physical library of a series of compounds and a virtual library of compounds that affect Wnt activity. In one aspect, the virtual screening process could be carried out where a permutational library of small peptides is substituted for the small organic molecules. The inventive methods may be used to empirically test for effects on Wnt activity and may also be applied to any pair of proteins involved in protein-protein interactions.

The invention relates to a process for the production of well-preserving lactose-free and low-lactose milk products. The process of the invention is characterized by separating the sugars and proteins in a raw material, thermally treating them in such a manner that the plasmin enzyme system and other proteolytic enzymes are inactivated, and combining the fractions and other preparation agents into a drink with a required composition and properties.

The invention belongs to the technical field of plant processing and in particular relates to a method for improving the bioavailability of iron and zinc elements of broad beans. The broad beans are low in cost, contain abundant microelements and proteins and have a certain advantage to dietary nutrition of people. Phytic acid which is contained in cotyledons of the broad beans can be combined with proteins and metal ions to reduce the bioavailability of the proteins and the metal elements. The deficiency of iron and zinc elements has certain universality, and the iron and zinc elements play an important role in intelligence development of children and physical health of adults. Most people take vegetarian diet as staple food in China, wherein the broad beans are popular bean crops, and carrots and spinaches are popular vegetables. The carrots and spinaches contain beta-carotene; and according to the method disclosed by the invention, the fact that the bioavailability of the iron and zinc elements in the broad beans can be improved by adding the carrots and spinaches into raw broad bean flour or cooked broad bean flour is discovered by using the carrots, spinaches and the broad bean flour to do an in-vitro digestion experiment, and therefore the nutritional value in use of the broad beans is increased.

The invention, which belongs to the technical field of biological information, discloses a protein-protein interaction prediction method based on a deep forest. According to the method, pseudo amino acid composition, a mutual information descriptor, composition, and distribution, a conversion descriptor, an amino acid composition position specificity score matrix and a dipeptide composition position specificity score matrix are fused to convert a protein sequence into a numerical vector; sequence information, physicochemical property information and evolution information of the protein pair are fused as initial characteristics of a sample; an elastic network is used for feature selection, and redundant and irrelevant features are removed; and a fused optimal feature vector is inputted intoa constructed multi-granularity cascade depth forest to predict protein-protein interaction. The method is simple and effective, the deep forest can represent the high-level feature information of the protein pair, the results of the training set and the test set are obviously superior to those of other prediction methods, and a certain reference can be provided for drug target prediction and human disease treatment.

The invention discloses a recognition method of protein complexes. The method comprises following steps: constructing a weighted protein-protein interaction network, wherein weight value represents similarity between gene representing modes corresponding to proteins with codes interacting each other; recognizing nucleuses of protein complexes based on the weighted protein-protein interaction network; recognizing accessory proteins for the nucleuses of protein complexes based on the number of nuclear proteins interacting with non-nuclear proteins and weight; and combining recognized nucleuses and recognized accessory proteins to recognize the protein complexes. The method does not take internal structures of protein complexes into consideration and utilizes the feature of code interaction with high protein gene expression. Therefore, the method is capable of recognizing protein complexes with overlapping structures and the recognized protein complexes reflect actual internal structures of protein complexes.

The invention discloses a fluorescent protein iRFP-based dimolecular fluorescent fragment complementary system, and applications. According to the fluorescent protein iRFP-based dimolecular fluorescent fragment complementary system, phytochrome fluorescent protein iRFP is taken as a template, and is divided into a non-fluorescent nitrogen terminal fragment iRN97 and a non-fluorescent carbon terminal fragment iRC98 at the amino acid 97-98 sites; when fusion expression of the two fragments with protein pairs with interactions is realized, the two non-fluorescent fragments are drawn to be close to generate iRFP fluorescence; fusion expression of iRN97 with human immunodeficiency virus integrase IN, and fusion expression of iRC98 with cell protein P75 are realized respectively, the interaction between IN and p75 is studied in living cells via fluorescent complementation of iRN97 with iRC98; when a drug is capable of inhibiting the protein-protein interaction, it is impossible for iRN97 and iRC98 to be drawn to be close, so that fluorescence recovery is inhibited. The fluorescent protein iRFP-based dimolecular fluorescent fragment complementation system is a simple, effective, and convenient evaluation system of drugs used for inhibiting protein interactions.

The invention discloses a protein folding identification method based on deep metric learning. The method comprises the following steps of encoding protein to obtain digital expression of a protein sequence, inputting the digital expression of the protein sequence into an SSA model to obtain a potential relational graph of protein residues, and fixing the relational graph to a set size, inputtingthe relational graph into a trained convolutional neural network, and obtaining the output of the previous layer of the classification layer as a depth feature, inputting the depth features into a trained twin network to obtain final protein features, and calculating the Euclidean distance between the query protein and the template protein based on the protein characteristics, and allocating the folding type of the template protein closest to the query protein to the query protein. A twin network is used, so that the distance between protein pairs of the same folding type is closer, and the distance between protein pairs of different folding types is farther.

A method for cross-linking albumin for use as a sealant or glue for a biological system, for example to induce hemostasis and/or prevent leakage of any other fluid from a biological tube or tissue, such as lymph for example. The cross-linked albumin may optionally and preferably be applied as part of a bandage for example. In other embodiments, the present invention provides a method of enzymatically cross-linking globular proteins, by altering the structure of the protein to improve the accessibility of the protein to the cross-linking enzyme.

The invention relates to the field of biology and particularly relates to a method for determining the interaction between proteins based on random projection integrated classification. The method comprises A, protein data screening, B, substitution matrix characterization, C, discrete cosine transformation, D, establishment of random projection integrated model and E, model determination. The method can acquire a prediction model of interaction between proteins according to classification characteristics of interaction between proteins and the prediction model is used for detecting interaction between proteins related to diseases so that the problem that the prior art cannot predict interaction between proteins and disease correlation and prediction of interaction between proteins and disease correlation is realized. The method is suitable for screening animal and strain protein pairs, builds a random projection integrated model for later analysis through substitution matrix characterization and discrete cosine transformation and provides accurate effect display and expression. Compared with the traditional method, the method provided by the invention has the better accuracy rate,sensitivity, a positive predictive value and stability and accuracy of Matthews correlation coefficient measurement.

It is an object of the present invention to obtain a labeled protein, and specifically, to separate a labeled protein and the same unlabeled protein. There is provided a labeled protein including: a protein to be labeled having a target protein, at least one or more affinity interaction domains for binding to an affinity support, and at least one or more labeling sites; and a labeling reagent binding to at least one of the labeling sites; wherein the affinity of the labeled protein for the affinity support is difference from that of the protein to be labeled for the affinity support.

The invention relates to a protein structure prediction method and device based on a multi-task time domain convolutional neural network. The method comprises the steps of: obtaining a target gene sequence and a protein database; establishing a DNA RNAamino acid ternary sequence data set corresponding to each protein according to the genetic code table and a protein database; establishing a multiple regression equation according to the residue depth and physicochemical properties of amino acids in the protein database to obtain statistical depth characteristics of each protein; clustering theternary sequence data set and mapping the ternary sequence data set into a multi-dimensional feature vector; taking the multi-dimensional feature vector and the statistical depth feature of the protein as the input of a multi-task time domain convolutional neural network, and training the multi-task time domain convolutional neural network; and predicting the protein structure by utilizing the statistical depth characteristics of the protein. According to the invention, the statistical depth characteristics of the protein are combined with the multi-task time domain convolutional neural network, so that the complexity of the model is reduced, and the generalization and the fitting degree are improved.

The invention relates to a real-time living cell structural mechanics detection method and application of the method, and belongs to the technical field of bioengineering. The method disclosed by the invention comprises the following steps: selecting fluorescent protein pairs capable of being subjected to fluorescence resonance energy transfer, connecting the protein pairs to angle positions by using short peptide chains, and constructing a mechanical detection probe by utilizing the fluorescence resonance energy transfer caused by the angle change of the protein pairs; and integrating the probe into the cell skeleton proteins by adopting a molecular cloning method and by connecting the probe with a skeleton protein. The fluorescence energy transfer is detected, so that the mechanical change of cell skeleton transfer can be estimated. The lengths of short peptide chains are changed, the initial angles of the protein pairs can be regulated, the sensitivity of the fluorescence probe is improved, and research on a mechanism related to the cell structure mechanics is effectively realized. The improved probe has the detection characteristics of slight influence on a culture medium, rapidness, micro amount, sensitivity, accuracy and high flux and can be used for constructing a cell platform for screening drugs related to the cell structure mechanics.

The embodiment of the invention discloses a drug target relationship prediction method and device, computer equipment and a storage medium, and belongs to the technical field of machine learning. The method comprises the following steps: determining a target drug molecule and a target protein; obtaining a target drug identifier corresponding to the target drug molecule and a target protein identifier corresponding to the target protein; and performing interaction relationship prediction based on the target drug identifier, the target protein identifier and the knowledge graph to obtain a relationship prediction result. At least one kind of omics data is introduced, the knowledge graph is formed in combination with the known drug target interaction pair, and drug target relationship prediction is performed based on the knowledge graph, so that information sources of relationship prediction are enriched, the defect that relationship prediction is performed only by depending on known prior knowledge such as drug protein interaction is overcome, and the prediction accuracy of the correlation between the drug molecules and the proteins is improved.

According to the invention, the affinity of VDAC3 protein on oxalic acid or calcium salt is proved by experiments. The calcium concentration can be reduced due to formation of organic acid calcium, such as calcium oxalate and the like, and related phenomena, such as spasm and the like, can be caused by reduction of the calcium concentration and the hard and insoluble organic acid calcium salt. Continental or offshore earthquake can be predicted accurately according to inhibition behavior activities and abnormal phenomena that animals suffer from spasm, refuse food, cannot stand and are easy to capture, water birds refuse to go in the water, and seabirds fear spasm and fly towards the mainland; therefore, the invention also provides application of the VDAC3 polypeptide to earthquake prediction, and a simple, convenient and effective earthquake prediction method is established and has great benefit of preventing and reducing natural hazard in the country and even the world.

The invention discloses plant heat resistance related protein TaXPD and coding gene and application thereof. The protein provided by the invention is a1) or a) or a3: a) amino acid sequence and is a protein as shown in a sequence 2 in a sequence table; a) is fusion protein which is obtained by connecting label to an end N or/and an end C of the protein as shown in the sequence 2 in the sequence table; and a3) is protein which is related to plant heat resistance and is obtained by substitution and/or deficiency and/or addition of one or several amino acid residues of the protein as shown in a1) or a2). Experiments prove that the coding gene of the protein is guided into an arabidopsis mutant uvh6-1 to obtain a transgenic plant of which the survival rate is increased and/or the extension length of a hypocotyledonary axis is increased and/or the ion leakage rate is reduced. Therefore, the protein provided by the invention has important theoretical significance and practical value on cultivation of plants of which heat resistance are improved.

Disclosed are a system and a method for statistically predicting an interaction probability between proteins, and more particularly to a system and a method for predicting an interaction probability between an unknown protein pair based on information obtained by analyzing differences between interacting and non-interacting sets of protein pairs from the view point of domain combination. The prediction method includes the steps of (a) obtaining appearance frequency information of a designated domain combination selected from each of interacting and non-interacting sets of protein pairs, and storing the obtained appearance frequency information, (b) determining a probability equation applied to predict the interaction between two proteins using the stored appearance frequency information of the domain combination, and (c) obtaining an interaction probability value between the two proteins from the determined probability equation.