646 results about "Protein structure" patented technology

Nonlinear generalized dynamic regression analysis system and method of the present invention preferably uses all available data at all time points and their measured time relationship to each other to predict responses of a single output variable or multiple output variables simultaneously. The present invention, in one aspect, is a system and method for predicting whether an intervention administered to a patient changes the physiological, pharmacological, pathophysiological, or pathopsychological state of the patient with respect to a specific medical condition. The present invention uses the theory of martingales to derive the probabilistic properties for statistical evaluations. The approach uniquely models information in the following domains: (1) analysis of clinical trials and medical records including efficacy, safety, and diagnostic patterns in humans and animals, (2) analysis and prediction of medical treatment cost-effectiveness, (3) the analysis of financial data, (4) the prediction of protein structure, (5) analysis of time dependent physiological, psychological, and pharmacological data, and any other field where ensembles of sampled stochastic processes or their generalizations are accessible. A quantitative medical condition evaluation or medical score provides a statistical determination of the existence or onset of a medical condition.

The present invention relates to kinase polypeptides, nucleotide sequences encoding the kinase polypeptides, as well as various products and methods useful for the diagnosis and treatment of various kinase-related diseases and conditions. Through the use of a bioinformatics strategy, mammalian members of protein and lipid kinase families have been identified and their protein structure predicted.

The invention discloses a mussel mucoprotein liquid product as well as a preparation method and an application thereof. One or more of a modifier, an additive and a crosslinker are added into mussel mucoprotein to form a new product which can serve as a wound repair product, a wound protection product, a medical biological bonder product, a medical coating product, an industrial coating product, a biochemical reagent product or the like. According to the mussel mucoprotein liquid product and the preparation method of the mussel mucoprotein liquid product, the modifier, the additive and the crosslinker are added into the mussel mucoprotein, so that the stability of a protein structure is enhanced, the protein molecular weight is increased, and the mussel mucoprotein liquid product is widely applied in various fields.

A system for making molecules, and proteins in particular, suitable for detection by a surface-selective nonlinear optical technique. A first use of the invention is for determining a protein's structure in real space and real time. A second use of the invention is to detect a protein or its activity (conformational change). A third use of the invention is for drug screening. A further aspect of the present invention is measuring probe tilt angle orientation in an oriented protein.

The invention relates to a micromolecule walnut peptide and a preparation method thereof. The preparation method sequentially comprises the following steps of protein extraction, twice enzymolysis, filtering, purification, concentration and drying. In the twice enzymolysis process, alkaline protease with the mass being 1-3% that of the walnut protein is added into a walnut protein extracting solution for once enzymolysis; enzyme deactivation is performed on a once enzymolysis solution after the once enzymolysis is finished; one or more kinds of proteinase including the flavored proteinase, the neutral proteinase and the papain with the mass being 1-3% that of the walnut protein are added after the once enzymolysis solution obtained after enzyme deactivation is performed is cooled for secondary enzymolysis. According to the preparation method, the twice enzymolysis method is adopted, due to the specificity of the structure of the walnut protein structure, sufficient enzymolysis cannot be performed on the walnut protein through enzymolysis of one proteinase, and the twice enzymolysis method is adopted for ensuring that the enzymolysis is brought into play on the most suitable condition of each proteinase, and therefore the obtained walnut peptide content is high.

This invention provides magnetic nanoparticles, which when placed in a magnetic field are selectively heated at a certain frequency of the magnetic field, as a function of their size, composition, or both. The invention also provides for use of such nanoparticles, in applications including, inter alia, selective nanoparticle heating and applications thereof, hyperthermia induction in cells or tissue, remote alteration of protein structure and/or drug delivery.

The invention relates to a forecasting method for a multi-stage differential evolution protein structure based on abstract bulge estimation. The method comprises the following steps: firstly, calculating the distance from each conformation individual in a current colony to a new conformation and performing ascending sorting according to the distance; then selecting the part of the new conformation individual close to a abstract bulge lower-limit estimation support surface of the conformation individual, thereby acquiring an energy lower-limit estimation value of the new conformation individual; calculating an average estimation error between the energy lower-limit estimation value of all the new conformation individuals and a practical energy value; dividing the whole algorithm into a plurality of optimizing stages according to the change in the average estimation error; judging the stage of the present iteration according to the average estimation error in the last iteration; and designing different strategies for all the stages and generating the new conformation individual. The forecasting method for the multi-stage differential evolution protein structure based on the colony abstract bulge estimation provided by the invention is high in forecasting precision and low in calculation cost.

The invention relates to an analytical method for researching protein structure or protein-protein interaction. The method comprises the following steps: performing crosslinking and enzymolysis on a protein composite in a cell by using a crosslinking agent with reactive groups on two sides and a breakable group, and taking a part of the enzymatic hydrolysate for a derivatization reaction for mass spectrometry; after breaking the crosslinking agent by means of a chemical method for the other part of the enzymatic hydrolysate, enriching peptide sections with an enriching material, and performing mass spectrometry on the enzymatic hydrolysate without the enriched peptide sections; determining the crosslinked peptide sections according to a library searching result so as to establish a peptide section library; finding out a candidate peptide section from the peptide section library according to N-terminal amino acid information of the crosslinked peptide section determined in the mass spectrogram of the crosslinked peptide section; and determining the crosslinked peptide section sequence by combining the mass spectrogram m/z of the crosslinked peptide section and the characteristic ions of the peptide section so as to obtain the protein structure and protein-protein interaction information. The method has the advantage of being simple to operate, and is applied to structural analysis of proteins and analysis of protein-protein composite interaction.

A method and device to assist in the determination of protein domain boundaries is described. In particular the device is designed to provide a high throughput of proteolytic digestion of proteins to identify domains and their boundaries, for use in protein structure determination, in a manner that is amenable to automation. Proteases are immobilized in a convenient format such as a microtitre plate and preferably arranged in a matrix thereby allowing for simultaneous degradation of a protein by a number of proteases at a number of concentrations.

The invention is directed to a model system for structure-activity relationship analysis of peptide or protein molecules involved in important biological processes. Provided by the invention are combinatorial peptide libraries comprising peptides with a novel “tryptophan zipper” scaffold (trpzip) that forms stable β-hairpin structure in solution. Methods of selecting and using such scaffold are provided herein, which are useful for mimicking native protein structures and interactions and designing therapeutic agents. Thus, the invention has profound utility for biological studies and drug development.

The invention provides a novel coronavirus antigen epitope and application thereof. A super computer is used for simulating S, E, M and N protein structures of the novel coronavirus, and novel coronavirus epitopes SEQ ID NO 1-38 are obtained through calculation. The epitopes have very good immunogenicity; an antibody generated by inducing a part of epitope polypeptide has the effect of neutralizing the novel coronavirus; the antigen epitope can be combined with antibodies in serum of novel coronavirus patients at home and abroad, has the potential of resisting various novel coronavirus strains, and also has the potential of identifying and applying different strains. The epitope provided by the invention can be used for (1) research and development of novel coronavirus vaccines and universal coronavirus vaccines such as MERS viruses and SARS viruses; and (2) preparing a novel coronavirus antibody, and further detecting and typing viruses and treating diseases caused by the viruses. Thenovel coronavirus antigen epitope has a wide application prospect in the aspects of prevention of coronavirus and propagation and outbreak thereof, and detection and diagnosis of virus strains.

The invention discloses a drug target affinity prediction method based on deep learning, and relates to the technical field of drug target affinity prediction. The method comprises the steps of: obtaining a drug compound and target protein data from a Davis data set and a KIBA data set; encoding the compound, and representing the protein by using a position specificity scoring matrix; inputting acompound label code into a CNN model, and performing feature extraction on the compound to obtain molecular representation of the compound; inputting the position specificity scoring matrix of the protein into an LSTM model, performing feature extraction on a protein sequence, and learning an order relationship between amino acids in a protein structure and a relationship between residues on the protein sequence to obtain the sequence representation of the protein; and simultaneously inputting the molecular representations of the compounds and the sequence representations of the proteins intoa fully linked layer to predict the affinity of the interaction of the compounds and the proteins. The method can predict the affinity relationship between the drug and the target more accurately.

The present invention discloses a method for predicting a secondary structure of the protein based on multiple evolution matrices. The method comprises: downloading a protein NR database and a BLAST program local software package to generate a position-specific scoring matrix PSSM of a given protein sequence, carrying out parameter adjustment on the PSI-BLAST program to obtain evolution matrices of different divergence degrees of the protein sequence; processing all eigenvectors in the evolution matrices to form multiple evolution matrix features; taking the multiple evolution matrix features as input of a classifier and evaluating classification accuracy to obtain an optimization model; and for the protein with an unknown structure, inputting the optimization model, and predicting the secondary structure of the protein. According to the method disclosed by the present invention, for a protein sequence, multiple matrices with different evolutionary divergence degrees are simultaneously used to express the protein sequence, so that the protein structure information is more fully expressed, the possibility of residue replacement is considered more comprehensively, accuracy for predicting the secondary structure of the protein is improved, and the encoding method is simple and effective.

This invention provides a method for protein structure alignment. More particularly, the present invention provides a method for identification, classification and prediction of protein structures. The present invention involves two key ingredients. First, an energy or cost function formulation of the problem simultaneously in terms of binary (Potts) assignment variables and real-valued atomic coordinates. Second, a minimization of the energy or cost function by an iterative method, where in each iteration (1) a mean field method is employed for the assignment variables and (2) exact rotation and/or translation of atomic coordinates is performed, weighted with the corresponding assignment variables.