21 results about "Protein network" patented technology

The invention relates to the technical field of bioinformatics, in particular to a biological information analysis system based on a large model technology, which comprises a data analysis calibration module, a problem disassembly module, an analysis task arrangement module, a result mapping module and a feedback iteration module. According to the method, genome comparison and clinical phenotype timestamps are dynamically calibrated, time sequence dislocation deviation is eliminated, base complementary pairing is combined with protein network anomaly screening, low-abundance collaborative variation capture is enhanced, genotype-phenotype discrete distribution quantifies and unifies multi-modal data benchmark, and the problem of multi-source heterogeneous standardization deficiency is solved; the method comprises the following steps: classifying and integrating pathogenic gene semantic weights by structural variation, balancing a statistical threshold and a biological function, dynamically optimizing an analysis sequence, synchronously covering a key mutation region, improving function annotation of a non-coding region, integrating gene expression clustering and protein network topology in a three-dimensional distribution manner, breaking through two-dimensional space limitation, performing closed-loop feedback to correct a threshold iteration elimination rule, and finally obtaining a high-quality gene expression cluster. And the genetic heterogeneity false positive rate is reduced.

The invention provides a biological signal transduction pathway analysis method based on a protein network, which comprises the following steps: acquiring omics data, and preprocessing the omics data to obtain preprocessed omics data; constructing a cell specific protein interaction network according to the preprocessing omics data; extracting topological structure characteristics from the cell specific protein interaction network; obtaining a signal transduction path according to the topological structure features and a network topological feature prediction model; and obtaining a signal transduction path analysis result according to the signal transduction path. According to the method, multi-omics data and a machine learning algorithm are combined, a complex signal transduction mechanism can be revealed from a global perspective, and the prediction accuracy is improved.

The embodiment of the invention provides a drug optimization method and system based on the overall effect of a protein network. The method comprises the following steps: constructing a protein interaction network related to a target disease, and dividing each protein target into a risk protein set and a protection protein set; respectively calculating first binding affinity data of the candidate drugs and each protein target in the risk protein set, and generating a first network comprehensive score based on the first binding affinity data; respectively calculating second binding affinity data of the candidate drugs and each protein target in the protection protein set, and generating a second network comprehensive score based on the second binding affinity data; calculating network confrontation scores of the candidate drugs according to the first network comprehensive score and the second network comprehensive score; and determining whether the candidate drug is a preferred drug based on the network adversarial score. The method can overcome the defect that a single-target drug is insufficient in curative effect due to a network compensation effect, so that safer and more effective candidate drugs are screened out.

The invention provides a multi-modal data fusion analysis method and system based on a protein network, and the method comprises the steps: obtaining multi-modal data, and carrying out the preprocessing, including noise filtering, normalization and feature extraction, and obtaining the feature vector of each modal; the feature vectors are mapped into protein network nodes, the connection probability is calculated based on the similarity between the nodes, a protein network topological structure is constructed, and cross-modal interaction can be achieved through the protein network topological structure; optimizing the protein network topological structure through a dynamic network evolution model, and updating network topological characteristics to obtain a protein network; and fusing the features in the protein network in combination with a modular fusion technology and a cross-modal alignment loss function to generate a multi-modal data fusion result. According to the method, efficient integration and semantic understanding of multi-modal data can be realized, and the robustness and interpretability of data fusion are improved.

A fermented milk product, wherein the fermented milk product comprises:a porous protein network;a first, capsular, preferably negatively charged, exopolysaccharide; anda second, non-capsular, preferably neutral, exopolysaccharide.A starter culture comprising a first, preferably negatively charged, capsular, exopolysaccharide producing lactic acid bacterial strain and a second, preferably neutral, non-capsular, exopolysaccharide producing lactic acid bacterial strain, wherein the weight ratio of the weight of the first exopolysaccharide producing lactic acid bacterial strain to the weight of the second exopolysaccharide producing lactic acid bacterial stain in the starter culture lies in the range from equal to or more than 1:1 to equal to or less than 100:1, more preferably in the range from equal to or more than 10:1 to equal to or less than 100:1; and a use thereof and process using such.

The invention discloses an electrochemical sensor for glycoprotein detection and a glycoprotein detection method. The method comprises the following steps: anchoring an aptamer-protein conjugate on the surface of a gold electrode, and then assembling an anti-fouling peptide around the aptamer-protein conjugate to form an imprinted self-assembled monolayer film. And removing the combined protein through an acid solution to form a biocompatible imprinting cavity capable of being used for recombining a target object. The imprinted cavity can eliminate non-specific adsorption and enhance targeted binding efficiency. The sensor can be used for directly detecting carcino-embryonic antigens with the concentration as low as 0.1 ng / mL through an electrochemical impedance spectroscopy method. Besides, homodimer concanavalin A (ConA) is used as a recognition element and a cross-linking agent, homodimer glucose oxidase (GOx) is assembled on the surface of the electrode in situ, a protein network can be formed, and enzyme catalysis signal amplification detection is achieved. Through in-situ formation of a ConA-GOx assembly, the sensitivity is improved by 100 times.

The invention belongs to the technical field of dairy product processing. The invention relates to a milk pudding, in particular to a gas-containing milk pudding and a preparation method thereof. The strain composition obtained by compounding specific strains is used for fermentation of the gas-containing milk pudding, a protein network structure can be formed, a stable semi-solid state is naturally formed on the premise that no gel is added, the stable texture of the gas-containing milk pudding is maintained, and therefore the purpose of zero addition is achieved; and the naturalness and health attributes of the product are improved. Besides, the milk product is combined with a'brake taste 'for the first time, the gas-containing milk pudding is endowed with stimulation and refreshing taste similar to carbonated drinks by utilizing the gas production effect of the strain composition, the sensory experience of the milk product is greatly enriched, the limitation of single soft and smooth taste of the traditional milk pudding is broken through, and the requirements of consumers on interesting foods are met. And the market prospect is wide.

The invention provides a protein structure prediction method based on a protein network, and the method comprises the steps: obtaining protein sequence data and three-dimensional structure data, and constructing the protein network; extracting topological characteristics according to the protein network, wherein the topological characteristics comprise edge weight, node degree, clustering coefficient, betweenness centrality, path length and modularization coefficient; based on the topological features and the three-dimensional structure data, training at least three machine learning models to construct a prediction model, the prediction model being capable of identifying a mapping relationship between the topological features and a protein structure; obtaining topological characteristics of to-be-detected protein, inputting the topological characteristics into the trained prediction model, and outputting to obtain a preliminary prediction three-dimensional structure of the to-be-detected protein; and optimizing the preliminary predicted three-dimensional structure through an optimization algorithm, and outputting to obtain a predicted three-dimensional structure of the to-be-detected protein. According to the method, the protein structure prediction efficiency and accuracy can be effectively improved, and the calculation cost is reduced.

The invention discloses chenopodium quinoa meal replacement powder and a preparation method thereof, and relates to the field of meal replacement powder, and the chenopodium quinoa meal replacement powder is realized through the steps of ultra-micro crushing of chenopodium quinoa, multi-component synergistic mixing, twin-screw extrusion puffing, spray drying and the like. The chenopodium quinoa willd is subjected to shelling and cleaning and then sequentially subjected to hammer type crushing and airflow crushing, and the fineness D50 is controlled to be 120-150 microns so as to improve the cell wall breaking rate; the preparation method comprises the following steps: mixing chia seed powder, flaxseed powder, resistant starch and other raw materials in proportion, and fully dispersing by a two-dimensional motion mixer; the mixed material enters a twin-screw extruder, starch gelatinization and fiber-protein network construction are completed under the conditions that the temperature of a melting section is 155-165 DEG C and the temperature of a die head section is 175-185 DEG C, and powder of a porous structure is formed through spray drying after extrusion. Experimental verification shows that the meal replacement powder prepared by the process is superior to the prior art in comprehensive performance in the aspects of satiety duration, DPPH free radical scavenging rate and brewing stability, nonlinear optimization of key parameters is realized through a multi-molecule synergistic effect, and the meal replacement powder has good nutrition retention and application values.

The application relates to a low-temperature vacuum fresh noodle extruding mechanism and belongs to the technical field of food machines, which comprises a frame part serving as a support body and used for fixing and supporting various components of the whole fresh noodle extruding mechanism, a power part arranged at the rear end of the bottom end of the frame part and used for providing power for an extruding part so that the extruding part realizes the conveying and extruding of noodle flocculus, and the extruding part used for realizing the fresh noodle extruding process under low-temperature vacuum conditions, conveying the noodle flocculus through spiral extrusion, simultaneously extracting the air in the noodle flocculus through a vacuum pump, and controlling the temperature of the noodle flocculus through a cold water cavity. The fresh noodle produced by the method of controlling low temperature and extracting the air in the noodle flocculus in the spiral extrusion conveying process has more advantages, and the taste, cooking loss, elasticity, protein network structure and the like are greatly improved.

The invention belongs to the technical field of food processing, and relates to an application of a composite modified starch-based Pickering emulsion in 3D printing of surimi. Through a composite modification strategy of OSA modification and ball milling, the obtained composite modified starch is smaller in particle size and stronger in particle flexibility, and can be rapidly adsorbed and arranged on an oil-water interface to form a compact interface film, so that the Pickering emulsion has excellent stability; the composite modified starch-based Pickering emulsion is added into surimi to serve as printing ink, so that the rheological property and the extrudability of the surimi can be remarkably improved, and the printing shape fidelity is improved; starch particles in the emulsion absorb water to swell and fill protein network pores, so that the network structure of the minced fillet gel is enhanced, the water binding capacity is remarkably improved, the cooking loss is reduced, and the gel strength and microstructure uniformity are improved.

The invention relates to the technical field of gargle preparation, in particular to an oral gargle based on synthetic biological materials. Comprising the following steps: preparing a pre-crosslinked mussel protein adhesion network precursor solution, preparing a temperature-sensitive gel matrix, preparing a liquid main agent, preparing a nano-emulsion and preparing the oral gargle based on the synthetic biological material. Through sequential combination of tea polyphenol pretreatment and immobilized tyrosinase catalysis, the recombinant mussel byssus protein is subjected to directional chemical modification, in a weakly alkaline buffer solution, a catechol structure of tea polyphenol can be combined with a protein chain to play a role in pre-organization and stabilization, and the tea polyphenol plays a role in improving the stability of the mussel byssus protein while participating in network construction. The inherent antibacterial and antioxidant functions are integrated into a material matrix in situ, a mechanical stability foundation is created for the subsequent compounding of a protein network and temperature-sensitive hydrogel, the uniform coexistence of a protein phase and a hydrogel phase is ensured, and the wet adhesion strength of the protein material is essentially improved.

The present application relates to the technical field of bioinformatics, in particular to a biological information analysis system based on large model technology, which comprises a data analysis calibration module, a problem disintegration module, an analysis task arrangement module, a result mapping module and a feedback iteration module.In the present application, dynamic calibration of genome alignment and clinical phenotype timestamp eliminates time sequence misplacement deviation, base complementary pairing combined protein network abnormal screening enhances low-abundance collaborative variation capture, genotype-phenotype discrete distribution quantization unifies multi-modal data benchmark, solves multi-source heterogeneous standardization loss, classifies and integrates pathogenic gene semantic weight, balances statistical threshold and biological function, dynamically optimizes analysis sequence to synchronously cover key mutation area, improves non-coding region function annotation, integrates gene expression clustering and protein network topology in three-dimensional distribution, breaks through two-dimensional space limitation, and reduces genetic heterogeneity false positive rate through closed-loop feedback correction threshold iteration elimination rule.

The present invention relates to compositions and methods for subpopulation-specific modulation of cell function through spatiochemical and spatiotemporal control over intracellular regulatory protein networks at the level of expressed gene products of nucleic acid-binding protein (NABP) genes. Subpopulation-specific cytoactive agents of the invention are used in therapeutic compositions that up- or downregulate disease-associated intracellular regulatory protein genes at the RNA and protein level for prevention and treatment of plant, animal and human diseases. Applications in human clinical medicine include biopharmaceutical preparations and associated treatment methods for genetic, degenerative, neurological, cellular and immune disorders resulting from dysregulation of endogenous NABP regulatory networks. Therapeutic compositions designed or selected for use in plants and nonhuman animals are applied to the fields of agriculture and veterinary medicine. Also provided are research kits for subpopulation-specific modulation of protein activity and the corresponding potential to discover novel therapeutic compositions across fields. More specifically, the disclosed compositions and methods selectively up- or downregulate at least a first subpopulation of a cellular protein or therapeutic target without substantially altering the activity of at least a second subpopulation of the cellular protein or therapeutic target.

This invention discloses a Qingjin Huatan Decoction and its application in regulating the serum inflammatory factor protein network of patients with AECOPD and phlegm-heat accumulation in the lungs, belonging to the field of biomedical technology. The Qingjin Huatan Decoction of this invention is composed of the following raw materials in parts by weight: Scutellaria baicalensis 10-20 parts, Gardenia jasminoides 6-12 parts, Morus alba root bark 6-12 parts, Trichosanthes kirilowii 10-14 parts, Fritillaria thunbergii 6-12 parts, Ophiopogon japonicus 6-12 parts, Platycodon grandiflorus 10-20 parts, Anemarrhena asphodeloides 6-12 parts, Citrus reticulata peel 10-20 parts, Poria cocos 10-20 parts, and Glycyrrhiza uralensis (processed) 4-8 parts. This formula is consistent with the core pathogenesis of AECOPD and phlegm-heat accumulation in the lungs, with appropriate warming and cooling properties, and a combination of tonifying and purging effects. It can inhibit the proliferation of pathogenic bacteria in the intestines, correct intestinal flora imbalance, achieve intestinal-lung harmony through the intestinal-lung axis, comprehensively improve patients' clinical symptoms, and enhance clinical suitability.

The present invention relates to methods and compositions for the high throughput screening of protein-protein interactions in yeast liquid culture. Protein fusions non-native to yeast may be expressed to replace endogenous sexual agglutination proteins and mediate library-by-library interrogation of protein interactions. The methods and compositions of the invention can be utilized for the characterization of protein interaction networks in high throughput for both binding affinity and specificity, which is crucial for understanding cellular functions, screening therapeutic candidates, and evaluating engineered protein networks.

The invention discloses a biomarker of amyotrophic lateral sclerosis. On the first aspect, the invention provides application of a substance for detecting a biomarker in preparation of a diagnostic product for amyotrophic lateral sclerosis, and the biomarker comprises at least one of Lyz2, Mup20, Lgals3, Sfn, Apod, Apoe, C1QA, C1QB, SPA3N, CD44 and MT2. According to the scheme, proteomics detection is carried out on medulla oblongata and spinal cord areas of ALS mice and WT mice, a spinal cord and medulla oblongata differential protein network of an ALS pathological model is established, potential biomarkers are screened out, and ALS diagnosis is facilitated through research on the biomarkers.

The present invention relates to a prediction method for mining protein interaction types based on deep learning. The present invention constructs a protein interaction type prediction model SE3NET-PPI, which only requires protein sequence information to complete end-to-end training, converts protein 3D structural information into a SE(3) invariant matrix diagram, and uses a convolutional neural network CNN and a pyramid pooling technology SPP to extract structural features; constructs a protein interaction network based on protein pairs in the protein interaction database, and then embeds the protein sequence through a pre-trained model layer to obtain protein node features and the aforementioned protein interaction network is input into a homogeneous graph neural network GIN to extract the topological information structure of the protein network. The above features are input into the MLP after feature fusion, and the prediction results of the corresponding action categories of the protein pairs are output; the sequence information, structural information and topological information of the PPI network of the protein are fused to predict the protein interaction type, thereby improving the prediction accuracy.

The invention provides an AI chip architecture design method based on a protein network and an AI chip. The method comprises the steps of obtaining protein structure data of human brain neuron connection, simulating the human brain neuron connection, constructing to obtain a protein network model, and calculating to obtain connection strength and a signal transmission mechanism between neurons; mapping connection strength and a signal transmission mechanism to a programmable resource of the FPGA, and dynamically adjusting a chip architecture through a configuration file updating mechanism of the FPGA; the protein network model is divided into a plurality of sub-networks, and the sub-networks are distributed to different calculation units for parallel processing; and dynamically adjusting the connection strength between neurons and a signal transmission mechanism according to input data and constraint conditions by adopting an adaptive learning algorithm, and carrying out performance evaluation on the adjusted chip architecture to obtain an AI chip architecture which passes the evaluation. According to the method, energy consumption can be reduced, the energy efficiency ratio and parallelism can be improved, and the architecture and functions can be dynamically adjusted through adaptive learning.