35 results about "Proteomics" patented technology

本发明实施例公开了一种基于蛋白质组学的脓毒症休克患者液体管理策略预测方法。所述方法包括：收集脓毒症休克患者的血浆样本，检测蛋白质标志物的表达水平，所述蛋白质标志物为GAL、NFASC、MICB_MICA、MAP2K6、JCHAIN和CSF3的组合；基于亚组识别模型计算效益得分，获得脓毒症休克患者的液体管理策略结果。本发明通过收集脓毒症休克患者的临床数据和蛋白质表达数据，采用LASSO回归分析，构建基于上述蛋白质标志物的亚组识别模型，进而获得效益评分，基于效益评分能预测患者液体管理策略方案的适用性，为脓毒症休克患者的液体治疗提供了可靠的方案，实现个体化和精准的液体治疗，在临床具有较好的应用前景。

The present invention relates to the field of biochemistry, more particularly to proteomics, more particularly to protein sequencing, even more particularly to single molecule peptide sequencing. The invention discloses means and methods for single molecule protein sequencing and / or amino acid identification using cleavage inducing agent. Said cleavage inducing agents which are not specific for one particular amino acid, cleave polypeptides step by step from the N-terminus onwards and provide information on the identity of the cleaved amino acids based on the kinetics of said reaction.

The application discloses a method for collecting and purifying mouthpart secretion of a predatory natural enemy Orius minor, and belongs to the technical field of biology. The method comprises the following steps: constructing a double-layer membrane sandwich artificial feeding device, encapsulating a nutrient solution in a sandwich space between a first membrane layer and a second membrane layer, and using the feeding drive of the Orius minor to make the Orius minor actively pierce the membrane layer and feed the nutrient solution, so that the mouthpart secretion is released into the nutrient solution in the natural feeding process; after the nutrient solution containing the secretion is collected, ultrafiltration centrifugation technology is used for concentration and purification, and a high-purity mouthpart secretion protein sample is obtained. The natural feeding state of the Orius minor is simulated, the collected secretion can more truly reflect the functional protein released by the Orius minor in the predation process, meanwhile, the integrated design of collection and purification effectively removes small molecular impurities in the sample, and the sample can be directly used for high-precision analysis such as proteomics.

The present disclosure is directed to methods for measuring proteomes of single cells. In one aspect, methods comprise disposing cells on a surface, synthesizing hydrogel chambers around each of the cells, lysing the cells to release intracellular proteins for adsorption onto the surface bounded by the hydrogel chambers, depolymerizing the hydrogel chambers, and quantifying the adsorbed proteins with detection antibodies to determine the single cell proteomes.

A method processes an experiment design definition and resulting omics data containing measurements for multiple analytes. For each analyte, the method fits a model to estimate a first variability and computes a first quality metric per measurement. The method aggregates per-analyte measurements into a composite quality metric, fits a function relating the composite quality metric to the first variability, and uses the function to transform both variables. The method fits a model to the transformed data to obtain a second estimated variability for each analyte and computes residuals from the first and second estimates, followed by variance moderation of the residuals. The method then reads a second dataset with samples for multiple analytes, computes a second quality metric for each analyte, transforms the second quality metric using the function, and applies the fitted model to output a variance metric for each analyte in the second dataset.

The application discloses a research method of a potential protein target point of Guangxi Yaoshan sweet tea against diabetic nephropathy based on proteomics, relates to the technical field of biological medicine, and solves the problem of lacking research on the target point of the action of Yaoshan sweet tea on the prevention and treatment of diabetic nephropathy from the perspective of proteomics. The method comprises the following steps: determining a characteristic active ingredient, namely myricetin, of Yaoshan sweet tea for intervention on diabetic nephropathy, obtaining a PPI network of network pharmacology of the intersection target point of the characteristic active ingredient of Yaoshan sweet tea and diabetic nephropathy; preparing a myricetin solution, preparing a cell disease model and grouping intervention, collecting and pre-treating a cell protein sample, performing mass spectrometry, pre-processing proteomic mass spectrometry data, screening a potential protein target point, identifying and analyzing an expression change trend of the potential protein target point, and analyzing a core protein target point and a key signal path. The application directly locates the key protein target point of Guangxi Yaoshan sweet tea on diabetic nephropathy from a protein expression level, which is more intuitive and more accurate.

The application discloses a kind of bifunctional magnetic nano material and its preparation method and application.The nano material described in the application is Fe3O4@ZrO2@TiO2@mSiO2-L-Cys, with spherical structure;The material includes successively from inside to outside: the inner core is the magnetic nanoparticle of ferriferrous oxide;Intermediate layer is zirconium dioxide and titanium dioxide composite metal oxide layer;Sub-outer layer is mesoporous silica layer;The outermost layer is the L-cysteine molecular layer grafted by zirconium-sulfur chemical bond;The preparation method described in the application adopts solvothermal method to synthesize magnetic core, functional layer is coated by hydrothermal reaction, co-condensation method constructs mesoporous shell, and hydrophilic group is introduced by chemical grafting.The bifunctional magnetic mesoporous nano material prepared in the application is applied to the effective enrichment of glycopeptide and phosphopeptide in standard protease solution mixture, and the detection sensitivity of phosphopeptide and glycopeptide reaches 1 fmol / μL, which has wide prospect in glycopeptide and phosphopeptide proteomics analysis research.

The application relates to a screening and determining method of an ischemic cerebral stroke animal model biomarker of qi deficiency and blood stasis syndrome, which integrates proteomics and transcriptomics technologies, systematically screens differentially expressed proteins and genes related to the syndrome, and on the basis, constructs various disease animal models, verifies candidate targets by adopting ELISA, simultaneously combines qPCR technology, multi-dimensionally confirms expression changes in the models, further draws ROC curves, calculates AUC values, sensitivity and specificity and other diagnostic performance indexes, evaluates diagnostic potential, and further knocks down and overexpresses differentially expressed genes, deeply excavates potential biomarkers with diagnostic values, and provides scientific bases for objective diagnosis of ischemic cerebral stroke qi deficiency and blood stasis syndrome and exploration of molecular mechanisms.

The present application relates to the technical field of protein genomics analysis, and particularly relates to a protein gene multiomics analysis method, which comprises: S1. sample collection and pretreatment: tumor tissue and paired normal adjacent tissue are selected as samples, and the samples are frozen and crushed; S2. sample multiomics detection: the frozen and crushed samples are subjected to genomics detection and proteomics detection; S3. sample multiomics data processing and analysis: the genomics detection result and the proteomics detection result of the samples are obtained, and tumor molecular characteristics are identified through multiomics data integration analysis.The present application carries out systematic protein genomics analysis on LCNEC tumor tissue and NATs. The research not only reveals the genomic abnormality characteristics of LCNE and disease-related molecular phenotypes, but also deepens the scientific cognition of patient stratification logic in targeted therapy strategies. Further research finds that IL33, as a new key therapeutic biomarker, is related to T cell infiltration and has anti-tumor activity.

Aptamer detection techniques with chemistry attachment with proximity hybridization and extension are described in an aptamer-based assay. In an embodiment, capture oligonucleotides can be used such that the capture oligonucleotides attach to spatially-fixed analytes in a fixed tissue sample. Aptamers that hybridize to the capture oligonucleotides are tagged with an extension sequence that can then be used for subsequent capture and spatial tagging steps.

This invention discloses a protein sequence spatial compression and functional optimization method based on a large-scale model, belonging to the fields of artificial intelligence and proteomics. This invention mines potential amino acid substitution sites in consensus sequences and then controls the sequential substitution process using a large protein language model, thereby maintaining the functional stability of proteins during sequence substitution and subsequently screening for substitution combinations that effectively enhance protein function. Introducing a large protein model transforms protein sequences into embedding vectors representing protein structure, function, and physicochemical properties. By analyzing the embedding vectors during the substitution process, it is possible to prevent new proteins from deviating from their original function and basic structure due to substitution. This invention combines consensus substitution identification with large-scale model analysis, effectively compressing the sequence space of amino acid substitutions, thereby significantly improving the efficiency of protein design and modification.

The application discloses a kind of magnetic material for synchronously enriching glycopeptide and phosphopeptide and its preparation method and application.The nanomaterial Fe3O4@nSiO2@TiO2@mSiO2-L-Cys described in the application;The material includes successively from inside to outside: the magnetic core of ferriferrous oxide;Non mesoporous silica intermediate layer coated on the surface of the core;Titanium dioxide functional layer coated on the surface of the intermediate layer;And the outermost layer is the ordered radial dendritic mesoporous silica shell layer modified by L-cysteine;The preparation method described in the application adopts solvothermal method to synthesize magnetic core, functional layer is coated by hydrothermal reaction, co-condensation method is used to build mesoporous shell layer, and hydrophilic group is introduced by chemical grafting.The prepared magnetic material of the application is applied to the effective enrichment of glycopeptide and phosphopeptide in standard protease hydrolysis mixed liquor, and the detection sensitivity of phosphopeptide and glycopeptide reaches 0.1fmol / µL and 1fmol / µL respectively, which has wide prospect in glycopeptide and phosphopeptide proteomics analysis research.

This application relates to proteomics extraction technology, specifically to a proteomics pretreatment device and an automated reaction system including the same. More specifically, it relates to a stacked proteomics pretreatment device adapted to an automated platform and an automated reaction system including the same. This application provides a proteomics pretreatment device, comprising: a carrier, and, located inside the carrier, a fastening layer, an ion exchange layer, a support layer, an extraction layer, and a leak-proof layer stacked from top to bottom; wherein the support layer comprises glass beads with an average particle size of 100-200 μm. Using the proteomics pretreatment device of this invention can reduce air bubbles while improving the uniformity of experimental data. The proteomics pretreatment device provided by this invention can achieve automated operation based on automated devices, improving sample processing throughput while also improving the accuracy of quantitative analysis.

The application belongs to the technical field of preparation of immobilized enzyme, and particularly relates to a preparation method of immobilized trypsin applicable to proteomics research; in the application, two polyelectrolytes with opposite charges are used to perform layer-by-layer self-assembly modification on the surface of sodium citrate-gold nanoparticles, the two modifiers have a large number of charges, and the dispersion and stability of the gold nanoparticles in the solution are significantly improved; and the trypsin is fixed on the surface of the gold nanoparticles through a covalent bonding method. The trypsin-gold nanoparticle copolymer prepared by using the method has strong stability, and the immobilized trypsin almost does not leak. In addition, the surface of the gold nanoparticles modified by sodium polyacrylate in the preparation process has a large number of carboxyl groups, and a large amount of trypsin fixing and binding sites are provided, and the enzyme loading capacity is significantly increased. The immobilization of the trypsin solves the problems of autolysis and self-aggregation of the natural enzyme during application and storage, catalytic activity reduction, and difficult reuse.

This invention discloses a single-sample multi-omics component extraction method for multi-omics joint analysis. The method includes: adding nucleic acid extraction solution to the sample, transferring it to a DNA adsorption column, centrifuging, and collecting the filtrate; washing the DNA adsorption column; eluting DNA; adding ethanol to the filtrate, transferring it to an RNA adsorption column, centrifuging, and collecting the filtrate; washing the RNA adsorption column; eluting RNA; and extracting proteins. This method integrates centrifugation column-based DNA / RNA co-extraction technology, a zinc ion-mediated protein precipitation and capture strategy, and an efficient Fe-IMAC phosphorylated peptide enrichment process. It achieves efficient protein precipitation and in-situ washing, thoroughly removing chemical components that interfere with mass spectrometry detection. It simultaneously achieves high-quality nucleic acid extraction and deep proteomics and phosphorylated proteomics coverage in a single sample, effectively solving the bottlenecks of traditional methods such as limited sample size, complex procedures, and poor reproducibility.

The application discloses a method for extracting mitochondria-associated membranes (MAM) and other subcellular components from podocytes, which comprises the following steps: pretreating the podocytes, crushing the podocytes by low-power ultrasonic, separating the crude mitochondrial components by differential centrifugation, and then purifying the high-purity MAM and other subcellular components by density gradient centrifugation and ultracentrifugation. The method replaces the traditional homogenizer grinding with low-power ultrasonic crushing, improves the MAM extraction yield by controlling the ultrasonic parameters, appropriately simplifies the differential centrifugation steps, increases the yield while ensuring the purity, sequentially separates the unbroken cells, the crude mitochondria, the cytoplasm and the endoplasmic reticulum components, and finally obtains the high-purity MAM and other subcellular components by density gradient centrifugation and ultracentrifugation purification. The method effectively improves the yield of the podocyte separation and extraction of the MAM and other subcellular components, reduces the cost, and provides technical support for the research on the podocyte MAM proteomics.

The application belongs to the technical field of biological medicine, and particularly relates to application of VSIG4 as a biomarker in evaluation of risk of lung fibrosis progression. The application first discovers and verifies that VSIG4 is significantly highly expressed in ILD patients with active inflammatory response, and the high expression is closely related to a signal of continuous progression of the disease by integrating peripheral blood exosome proteomics data and lung tissue single-cell transcriptome data. Based on the discovery, the application provides a kit and a detection method for detecting a VSIG4 protein level in peripheral blood. Data show that the PPF patients can be effectively identified by detecting the content or level of the exosome protein VSIG4 in peripheral blood, and the method has high sensitivity and specificity. The application provides a new non-invasive solution for early risk prediction and disease progression monitoring of lung fibrosis, and has important clinical value.

The application discloses a kind of based on deep learning's pathological image proteomics analysis method and system, it is related to bioinformatics, the method includes: using laser microdissection and mass spectrometry constructs paired image-protein dataset;Adopt pre-training VGG16 convolutional neural network to extract image deep feature, fusion cell morphological feature constructs whole proteome regression mapping model;Through U-Net single cell recognition and iterative deconvolution algorithm output cell level protein expression matrix;Combining mixed semi-supervised learning strategy handles label scarcity problem;Based on protein expression matrix, dimension reduction clustering, spatial heterogeneity quantification (CV+SHI) and visual analysis are carried out.The application realizes the regression mapping of pathological image to whole proteome (>2800) for the first time, breaks through the flux bottleneck of traditional virtual staining, realizes true single cell resolution proteomics, and provides new means for precision medicine.

This invention discloses the application of the TP63 gene or protein in the preparation of drugs for treating benign prostatic hyperplasia (BPH), belonging to the field of biomedical technology. The application of the TP63 gene or protein in the preparation of drugs for treating BPH involves precise screening of the target TP63 gene or protein based on subtype-specific proteomics data, combined with dual verification of "drugability" and "clinical relevance," enabling its transformation into a clinical drug; and possessing a complete chain of evidence for multi-cell line functional verification, fully demonstrating the effectiveness of the target intervention.

This invention provides the application of FAM205A in the preparation of a male infertility detection kit, detecting FAM205A gene abnormalities and / or abnormal FAM205A protein expression levels. By integrating proteomics analysis, functional validation, and a gene knockout mouse model, this invention demonstrates that FAM205A deficiency disrupts sperm morphology, spermatogenesis, motility, and fertilization capacity through a dual mechanism, making FAM205A a reliable diagnostic biomarker for idiopathic male infertility. FAM205A gene abnormalities can be detected through whole-genome sequencing, and abnormal FAM205A protein expression levels can be detected using antibodies that specifically bind to the FAM205A protein, leading to the development of a male infertility detection kit.

This invention discloses a method for evaluating the simulation accuracy of breast milk fat globule membrane proteins (MFGMs) based on multidimensional similarity weighted fusion. The method includes: constructing a breast milk MFGM proteomics database and acquiring proteomics data of the samples to be evaluated; obtaining a reference matrix based on the breast milk MFGM proteomics database through row and column filtering, performing a logarithmic transformation on the reference matrix, calculating the mean and standard deviation of each feature in the reference matrix, and constructing a Z-score matrix and a deletion mask matrix corresponding to the reference matrix; calculating the abundance distribution similarity and deletion pattern similarity of the samples to be evaluated; setting a shrinkage coefficient based on the actual number of overlapping features, filtering a core protein set according to a set threshold, and comparing to determine the types of missing MFGM proteins in the samples to be tested; constructing Top-K neighbor centers, calculating the feature deviation contribution and abundance difference of the samples to be evaluated, and outputting key defective proteins and formulation optimization suggestions based on preset feature importance indicators. This improves the accuracy of similarity simulation.

The present application relates to a kind of D-protein multidimensional structure information accurate prediction method, belong to protein post-translational modification identification technical field.The method solves the problem that traditional mass spectrometry cannot directly identify the molecular weight of D-type amino acid modification unchanged, and the problem of low flux and high cost of existing experimental method.The technical points are: constructing a multi-species mass spectrometry annotation dataset, training an integrated deep learning model capable of simultaneously predicting peptide D-modification probability, retention time RT and collision cross-sectional area CCS, and verifying the reliability of the model through cross-species validation and independent thermal proteomics experiment, ultimately achieving rapid, multidimensional calculation-assisted identification of D-modification in target sample.

This invention discloses a method and device for predicting disease risk based on plasma proteomics and waist circumference, belonging to the field of medical data processing. Utilizing 2,920 plasma proteins from 52,879 participants in the UK Biobank, this invention constructs a proteomics-based waist circumference proWC score using LASSO regression, and derives the protein-anthropometric difference index proWCΔ to quantify the difference between proteomics-based waist circumference and measured waist circumference. Based on the proWC score and proWCΔ, disease risk association analysis, mortality prediction, causal mediation analysis, and dietary association analysis were conducted across 14 organ systems, demonstrating that proWC has superior clinical predictive value compared to waist circumference alone. Furthermore, based on proWC, a hidden high-risk subgroup was identified—patients with normal measured waist circumference but a near-overtly obese risk of diabetes-related death. By constructing proWCΔ and proWC, this invention elevates the assessment of central obesity from morphological measurements to the molecular level, providing a new molecular framework for precise risk stratification of central obesity.

This invention discloses a method for enriching protein palmitoyltransferase substrates, belonging to the field of biochemical analysis technology. This invention utilizes a tandem enrichment method integrating APEX2-based proximity labeling technology and Acyl-RAC-based palmitate-assisted capture technology to enrich specific protein palmitoyltransferase substrates. These substrates are then identified using proteomics techniques. This invention improves the specificity of large-scale identification of protein palmitoyltransferase substrates at the cellular level, accelerates the construction of palmitoyltransferase-substrate interaction networks, and advances research progress in palmitoyltransferase modification.

This invention discloses a magnetic bead-based low-abundance protein enrichment kit and its extraction and enzymatic digestion method, relating to the field of biomedical technology. The kit includes a lysis buffer, magnetic bead washing buffer, reducing alkylation solution, two equilibration solutions, and a termination reagent. The extraction and enzymatic digestion method comprises the following steps: S1, low-abundance protein enrichment; S2, proteolytic digestion. This magnetic bead-based low-abundance protein enrichment kit and its extraction and enzymatic digestion method can rapidly and efficiently capture low-abundance proteins from bodily fluid samples, solving the problems of low digestion efficiency and frequent failures. Protein extraction is performed using an enzyme-compatible buffer, allowing the protein to be digested in the buffer, simplifying the operation. In particular, the application of multifunctional magnetic beads not only improves the enrichment efficiency of low-abundance proteins but also enhances the flexibility and throughput of analysis, opening up new prospects for proteomics research.

This application relates to proteomics extraction technology, specifically, to a proteomics extraction kit. This application provides a proteomics extraction kit, comprising: a kit and reagents, the kit including a first kit and a second kit; wherein, the first kit is a proteomics extraction device, the proteomics extraction device including a solid-phase extraction layer, wherein the total volume of the solid-phase extraction layer is ≥10 mm². 3 The second kit is a drying device; the reagents include an elution buffer, which includes methanol; by using the kit of the present invention, the volume of the extraction layer is increased to adapt it to the one-time extraction of large-capacity proteins. At the same time, due to the increase in the volume of the extraction layer, the second kit and the collection solution are adapted accordingly. By drying the extraction layer and using a specific collection solution, large-capacity proteins can be extracted in one go while ensuring their recovery rate.

This application discloses a predictive model, method, and system for cardiovascular complications in peritoneal dialysis patients based on glycated proteomics and machine learning. This application utilizes techniques such as glycated peptide enrichment, high-throughput glycated proteomics detection, targeted absolute quantification, and machine learning modeling to construct a predictive model suitable for assessing the risk of CVD complications in peritoneal dialysis patients. This model organically integrates glycated peptide biomarkers with clinical indicators, enabling early identification and stratified management of cardiovascular risk in peritoneal dialysis patients, and has good clinical application potential.

The application provides a raw spectrum processing method and device, computer equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: extracting a plurality of chromatographic peaks from a raw spectrum of proteomics based on a target peptide library; determining a plurality of spectral line feature matrices based on the plurality of chromatographic peaks; performing feature extraction on the plurality of spectral line feature matrices respectively based on a deep learning model to obtain a plurality of peptide feature vectors; and determining typing information of the raw spectrum based on the plurality of peptide feature vectors, wherein the typing information is used to indicate the probability of the raw spectrum corresponding to a target disease type. The above technical solution can automatically extract features in the raw spectrum of proteomics without human intervention, the extracted features are more accurate and the extraction efficiency is high, thereby significantly improving the accuracy and analysis efficiency of the analysis result.