183 results about "Proteomics" patented technology

Deep learning tools such as convolutional neural networks (CNNs) and transformers have spurred great advancements in computational biology. However, existing methods are constrained architecturally in context length, computational complexity, and model size. This application introduces a sub-quadratic architecture for modeling, which combines projected gated convolutions and structured state spaces to achieve local and global context with, for example, single-nucleotide resolution. These models outperform CNN-, GPT-, BERT-, and long convolution-based models in many tested genomics tasks without pre-training and with 4×-781× fewer parameters. In the proteomics domain, these models similarly outperform pretrained attention-based models, including ESM-1B and TAPE-BERT, on remote homology prediction without pre-training and while using 3,308×-23,636× fewer parameters.

The invention relates to the technical field of biomedicine, in particular to a method for constructing a plasma ctDNA organ distribution characteristic spectrum of advanced colorectal cancer. The method comprises the following steps: collecting a peripheral blood sample at multiple time points, separating plasma by adopting a double-centrifugal method, and extracting circulating tumor DNA (Deoxyribose Nucleic Acid); carrying out whole exome sequencing based on ctDNA to obtain genome variation information and calculating variation allele frequency, and synchronously detecting the expression quantity of immune-related proteins by adopting an Olink proteomics technology; integrating the genome data, the protein expression data and the clinical pathological features, and constructing a multi-dimensional feature data matrix; and taking the organ metastasis condition confirmed by iconography as a supervision label, training a model by applying a machine learning algorithm, screening key prediction factors, constructing a quantitative prediction model, and finally generating a visual organ metastasis tendency prediction map. According to the method, early and accurate prediction of the advanced colorectal cancer organ metastasis tendency is realized through multi-omics data collaborative analysis and machine learning modeling.

The invention relates to the technical field of bioinformatics, in particular to a method for typing acute respiratory distress syndrome biological subtypes. The method comprises the following steps: a) acquiring multi-omics data and carrying out standardized preprocessing; the multi-omics data comprises transcriptomics data, proteomics data and metabonomics data of a biological sample source; b) constructing a similarity network of each group by using a similarity fusion network (SNF), and obtaining a uniform sample similarity matrix through multi-group network fusion and iteration; multiple collaborative principal component analysis (MCIA) is adopted to carry out dimension reduction on multi-omics data so as to realize visualization of a clustering result; carrying out multi-omics joint discrimination modeling under the guidance of SNF clustering by using a data integration analysis (DIABLO) method so as to identify key feature variables; and carrying out biological subtype classification based on the clustering result of the steps.

The invention relates to the technical field of tumor markers, in particular to a pancreatic cancer early screening marker combination based on blood protein and application of the pancreatic cancer early screening marker combination. Through proteomics analysis and bioinformatics screening, 12 kinds of proteins such as NME3, HPGDS, CEACAM5, TNFSF12, SCG3, VTCN1, GFRA1, KRT19, TMPRSS12, NELL2, KAZALD and MMP12 are determined to serve as the potential early screening markers of the pancreatic cancer. Researches show that the combination of the proteins can effectively distinguish pancreatic cancer patients from non-pancreatic cancer patients, and has good sensitivity and specificity. A screening model constructed based on the protein combination can be applied to auxiliary diagnosis, risk assessment and population screening of pancreatic cancer, and has important clinical transformation value.

The invention discloses a method for annotating a vesicle cell subset based on monovesicle membrane proteomics, and belongs to the technical field of vesicle cell annotation. The method comprises the following steps: 1, carrying out exosome collection on a plasma sample to obtain a sample; 2, coding and labeling the antibody probe, and adding a protein tag and a labeled DNA sequence into the antibody probe to obtain a labeled antibody probe; 3, preparing a corresponding combined product; 4, adding a labeled antibody probe into the sample to obtain an exosome compound; step 5, capturing an exosome conjugate through CTB; 6, adding the binding product into the exosome conjugate to generate a sequence; 7, constructing a sequence library and sequencing to obtain a sequencing sequence; 8, removing a sequencing sequence with low expression quantity to obtain an exosome expression data matrix, and standardizing the exosome expression data matrix; 9, carrying out clustering analysis to find out an exosome core subgroup; and step 10, determining the source of the exosome core subgroup, and annotating the single vesicle subgroup.

The invention discloses a kit and a method for detecting pepsin based on polypeptide, and belongs to the technical field of proteomics. The amino acid sequence of the polypeptide is as shown in SEQ ID No. 1. The polypeptide is obtained based on enzymolysis of pepsinogen PGA4 and screening, the polypeptide and isotope labeled polypeptide are utilized to establish a standard curve, a biological sample is further subjected to enzymolysis to obtain a peptide fragment sample, and pepsin in the biological sample can be quantified by detecting the content of the polypeptide in the peptide fragment sample. By utilizing the method disclosed by the invention, the pepsin in the biological sample can be rapidly quantified, so that pathological reflux and physiological reflux are diagnosed and distinguished, and the accuracy rate is high.

A diagnostic platform for detecting and analyzing brain tumors includes a portable fiber Surface Enhanced Raman spectroscopy (SERS) module, a removable fiber probe for intraoperative use, disposable nanoplasmonic cartridges for individual sample loading, and an updatable spectral library collected from healthy and diseased samples. The platform is designed to analyze blood and tissue signatures and employ artificial intelligence (Al) models to classify SERS spectra based on an updatable proteomics and SERS spectral library encompassing distinct brain tumor types. The platform is further configured to utilize artificial intelligence (Al) algorithms for the analysis and classification of the recorded SERS spectra.

The invention belongs to the technical field of biological materials and biological information, and provides a space phosphorylation modification omics detection method. According to the detection method disclosed by the invention, proteomics analysis can be carried out on trace sample phosphorylation, 4259 phosphorylation sites can be identified by 5 micrograms of peptide fragments, and the credibility of 3506 sites is greater than 0.75.

The invention discloses a multi-layer heterogeneous network unicellular organism network inference method based on meta-path enhancement, which mainly comprises a gene regulation knowledge base enhanced multi-layer heterogeneous network construction module for integrating an external gene interaction network and multiple omics data such as scRNA-seq, scATAC-seq, ST and the like; constructing a single-cell multi-omics multilayer heterogeneous network containing cell-cell, cell-gene and gene-gene relationships, and fusing spatial constraints to consider cell positions and tissue structures; and the feature enhancement module based on the meta-path explores complex semantics of the network by designing a multi-hop meta-path mode, designs an adaptive multi-view learning framework and a multi-round enhancement mechanism, and optimizes feature representation by using cell-gene interaction and cross-modal attention fusion. The unicellular biological network can be effectively deduced, the deduction accuracy and biological interpretation are remarkably improved, the method plays an important role in understanding the cell biological process, developing and treating diseases and the like, has good expandability, and can further integrate multi-modal omics data such as proteomics and metabonomics.

The invention discloses a disease-related anomaly localization protein prediction method based on deep learning, and the method comprises the steps: carrying out the protein prediction based on the proteomics expression data of normal and disease samples and a known protein interaction network in a normal state; respectively constructing a protein interaction network under the activity characteristics of the sample pathway and the disease state; the proteomics expression data and the pathway activity characteristics are fused through a cross attention mechanism, and protein characterization characteristics with pathway perception ability are constructed; respectively predicting protein subcellular localization under the normal and disease states by using a graph attention network model based on the protein characterization characteristics and the protein interaction network corresponding to the normal and disease states; disease-related abnormal localization proteins are identified by comparing predicted protein subcellular localization in normal and disease states. The method can efficiently and accurately identify the abnormal localization protein related to the disease, and has important scientific research value and application prospect.

The invention discloses a breast milk fat globule membrane protein simulation degree evaluation method based on multi-dimensional similarity weighted fusion, and the method comprises the steps: constructing a breast milk MFGM proteomics database, and obtaining the proteomics data of a to-be-evaluated sample; the method comprises the following steps: obtaining a reference matrix through row screening and column screening based on a breast milk MFGM proteomics database, performing logarithmic transformation on the reference matrix, calculating a mean value and a standard deviation of each feature in the reference matrix, and constructing a Z-score matrix and a missing mask matrix corresponding to the reference matrix; calculating abundance distribution similarity and missing mode similarity of the to-be-evaluated sample; setting a shrinkage coefficient based on the actual overlapping feature number, screening a core protein set according to a set threshold value, and comparing to obtain the missing MFGM protein type of the to-be-detected sample; and constructing a Top-K neighbor center, calculating the feature deviation contribution and abundance difference of the to-be-evaluated sample, and outputting key defect proteins and formula optimization suggestions according to preset feature importance indexes. And the similarity simulation accuracy is improved.

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 invention discloses a method for synthesizing millions of unique DNA tags through primer combination and application of the method, and relates to the field of molecular biology. According to the method, partial complementary pairing characteristics of F, X and Y primers are utilized, and under the synergistic effect of T4 DNA polymerase and ligase, millions of unique DNA tags are generated through one-time reaction through annealing-filling-ligation three-step reaction. The label structure comprises 5'end functional modification sites (such as amino and biotin) and a customizable sequencing joint, and is suitable for high-throughput labeling scenes such as single-cell multiomics, space proteomics and antibody coupling (AOC). Compared with a traditional one-by-one synthesis method, the method has the advantages that the single-tag synthesis cost is reduced by 80% or above, a reaction system is compatible with automatic operation, the tag combination complexity can reach 5.6 * 10 (based on 384 * 384 * 384 primer combination), and an efficient solution is provided for large-scale molecular marking.

The invention discloses an application of a reagent for detecting AP3S2 in preparation of a diagnostic reagent for neoadjuvant immunochemotherapy resistance of gastric cancer in a local progression stage, which comprises the following steps: screening differential expression proteins in neoadjuvant immunochemotherapy resistance tissues of gastric cancer in the local progression stage through proteomics; a new marker is provided for diagnosis of the local progression stage gastric cancer immunochemotherapy drug resistance, and a basis is also provided for development and future development of a local progression stage gastric cancer immunochemotherapy drug resistance diagnosis method.

The present application relates to a kind of individual future health state prediction method, device and medium based on proteomics, wherein the method comprises the following steps: obtaining proteomics data and preprocessing;Shared network construction: construct individual past and future comorbidity condition prediction neural network based on twin network framework;Trunk network construction: construct health-specific outcome prediction neural network based on multilayer perceptron method;Health assessment network integrates the shared network and trunk network architecture, and extracts features thereof to fuse and fine-tune in latent space, updates fine-tuning network parameters by further training, and outputs the risk assessment probability of multiple health-specific outcomes in the future.Compared with prior art, the present application focuses on proteomics data processing and modeling method, uses past and future health estimates as prior information, and realizes individual health condition assessment with multiple diseases and death as outcome.

The application discloses a marker for determining the severity of IgAN kidney tissue lesions and application thereof, and inventors find that ACTN4, ACADS and COL1A1 are significantly differentially expressed proteins in kidney tissues. The significant down-regulation of ACTN4 may cause the change of actin cytoskeleton of IgAN glomerular podocytes; the significant down-regulation of ACAD may indirectly participate in the occurrence process of abnormal structure and function of IgAN renal tubular epithelial cells by affecting fatty acid metabolism in the cells; and the significant up-regulation of COL1A1 may participate in the accumulation of extracellular matrix in the renal interstitium of IgAN, and play a certain role in promoting the renal interstitial fibrosis. The combination of ACTN4, ACADS and COL1A1 has good prediction efficiency for the diagnosis of the severity of IgAN kidney tissue lesions, and the area under the ROC curve is 0.815, P 0.043. In addition, the immunohistochemical results also confirm the expression trend of the three proteins ACTN4, ACADS and COL1A1 in the corresponding kidney tissue substructures in proteomics.

The invention relates to the technical field of forest tree hybridization, and discloses a forest tree hybridization parent precise matching method based on multi-omics analysis, which comprises the following steps: S1, obtaining multi-omics data: performing genome sequencing, transcriptome analysis, proteomics analysis and metabonomics analysis on forest tree population individuals; a plurality of omics data such as genetic variation sites, gene expression quantity, protein expression abundance and metabolite spectrums are obtained. According to the forest tree cross parent accurate matching method based on multi-omics analysis, forest tree genetic characteristics are analyzed comprehensively through multi-omics data, genomics, transcriptomics, proteomics and metabonomics data are deeply fused, genetic factors closely associated with target traits are accurately identified, and the accuracy of forest tree cross parent matching is improved. According to the method, the scientificity of parent matching in forest tree cross breeding on the molecular level is remarkably improved, the fuzziness and uncertainty of traditional judgment only according to phenotype and experience are abandoned from the source, the parent matching accuracy is greatly improved, and the breeding work is more targeted and efficient.

The invention belongs to the technical field of proteomics detection, and particularly relates to a method for detecting mouse liver neomembrane proteome based on an orthogonal translation system and a mouse strain containing an SORT-KASM module. Comprising the following steps: (S.1) constructing a conditionally expressed SORT-KASM transgenic mouse strain; (S.2) activating the expression of the SORT-KAS M by a tissue specific Cre system; (S.3) giving unnatural amino acid ingestion to the mouse; and (S.4) carrying out biotin labeling coupling on non-natural amino acids in the proteome through an azide-alkyne click reaction. According to the detection method, the space and time high resolution of the liver tissue is achieved, the membrane protein has the high-coverage marking capacity, the low-abundance newborn protein has the high-sensitivity marking capacity, good stability is achieved, and the membrane protein participates in the important physiological processes such as substance transfer, energy metabolism and steady state maintaining; the innovation of the detection method provides a new thought for researching the liver cell membrane proteome.

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 present invention provides a multiomics approach, which integrate single-cell RNA-sequencing (scRNA-seq) and spatiotemporal transcriptomics (ST) offering potential for dissecting transcriptional networks and revealing cell-cell interactions involved in biomolecular pathomechanisms. The present invention also provides a multimodal approach combining high-throughput scRNA-seq and ST to elucidate XLRS-specific transcriptomic signatures in two XLRS-like models with retinal splitting phenotypes, including genetically engineered (Rs1emR209C) mice and patient-derived retinal organoids harboring the same patient-specific p.R209C mutation. Through multiomics transcriptomic analysis, the endoplasmic reticulum (ER) stress / eIF2 signaling, mTOR pathway, and the regulation of eIF4 and p70S6K pathways as chronically enriched and highly conserved disease pathways between two XLRS-like models are identified. Western blots and proteomics analysis validated the occurrence of unfolded protein responses, chronic eIF2α signaling activation, and chronic ER stress-induced apoptosis. Furthermore, therapeutic targeting of the chronic ER stress / eIF2α pathway activation synergistically enhanced the efficacy of AAV mediated RS1 gene delivery, ultimately improving bipolar cell integrity, postsynaptic transmission, disorganized retinal architecture and electrophysiological responses. Collectively, the complex transcriptomic signatures obtained from Rs1emR209C mice and patient-derived retinal organoids using the multiomics approach provide opportunities to unravel potential therapeutic targets for incurable retinal diseases, such as XLRS.

A method for predicting one or more sample metric values by machine learning is provided. The method comprises determining a plurality of characteristics from raw data obtained by analysing a proteomics sample in a liquid chromatography mass spectrometer, LC-MS. The method further comprises predicting one or more sample metric values by processing the plurality of characteristics using one or more trained machine learning models.

The invention provides a proteomics complex post-translational modification large-scale rapid identification method which comprises the following steps: preprocessing original protein data of a sample to obtain a primary signal peak cluster and a candidate secondary signal peak set corresponding to each primary signal peak; determining a first-level signal peak and a second-level signal peak pair based on the first-level signal peak cluster and the candidate second-level signal peak set; based on a pre-trained deep learning model and the peak pair of the first-level signal peak and the second-level signal peak, obtaining a matching score of the peak pair of the first-level signal peak and the second-level signal peak; and constructing a target pseudo secondary spectrogram based on the matching score, and performing open search on the target pseudo secondary spectrogram to obtain an identification result containing post-translation modification. According to the method, deconvolution can be carried out on the complex secondary spectrogram under methods including independent collection, the target pseudo secondary spectrogram which can be directly used for open type search is generated, and then accurate identification and deep analysis of accidental modification signals are achieved.

This invention relates to the construction and application of an exosome purification device. The purification device utilizes the differences in size and charge between exosomes and other substances, combining the tangential flow of the sample within the device with the dual effects of membrane sieving and an electric field to remove contaminants and achieve high-purity exosome enrichment. The device includes a pre-filtration section and an exosome purification section. This device is simple to operate, can handle large sample volumes, has a short processing time, is less prone to membrane clogging by contaminants, and achieves high purity exosome separation with minimal structural damage. It can meet the separation and purification needs of exosomes from different sources and can be further applied to research on exosome proteomics, drug loading, activity, and biological functions.

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 invention relates to a novel chemical cross-linking agent with mass spectrum fragmentable trehalose disaccharide as a skeleton structure and a preparation method thereof. The cross-linking agent disclosed by the invention has the following characteristics: 1) trehalose disaccharide is used as a skeleton structure, so that the cross-linking agent has excellent biocompatibility; 2) the trehalose skeleton has a pair of symmetrical mass spectrum fragmentable glucosidic bonds, so that a cross-linked peptide fragment can be simplified into a conventional peptide fragment modified by a cross-linking agent fragment; 3) the enrichment of the cross-linked peptide fragment can be realized by the trehalose skeleton under the condition of not adding an enrichment handle; and 4) active groups of the cross-linking agent comprise but not limited to a plurality of reactive groups such as succinamide ester, diaziridine, phenylsulfonyl fluoride, hydrazide group, amino group, hydroxylamine group and the like, and chemical cross-linking of a plurality of amino acids except lysine is realized. The trehalose cross-linking agent disclosed by the invention is applied to the field of proteomics, and provides technical support for realizing large-scale analysis of a protein complex in a complex sample, spatial structure analysis of protein and a protein-protein interaction network.

The invention provides a pretreatment method for mass spectrometric detection of plasma protein and a kit. A plurality of plasma treatment means are combined by comprehensively applying acetonitrile; the serum specifically comprises 10% of whole plasma, 20% of acetonitrile precipitate, 10% of 40% acetonitrile precipitate, 20% of 50% acetonitrile supernatant, and 20% of supernatant obtained by removing albumin after precipitating the plasma with 10% and 20% of polyethylene glycol respectively. Deep proteomic analysis is carried out on the redistributed plasma sample, 5441 proteins are identified from 5336 genes, and proteins with the concentration as low as nanogram / upgrade are detected. On the premise that common abundance proteins are not removed, more than 5000 proteins are successfully identified under the conditions of about 100 non-tandem components and 2-hour gradient operation, and the maximum scale of mass spectrum-based plasma proteomics research is achieved.

Provided are methods, systems and computer program product embodiments for analyzing multi-omic data using a tensor regression model for genome-wide association studies in the life sciences. The unique structure of tensor covariates is leveraged to find associations between the omics data and complex diseases. Within this framework, the excessive dimensionality is reduced to a manageable level, leading to efficient estimations and predictions. The method is superior to using classical regression techniques in genome-wide association studies, which are challenged by analyzing multi-dimensional and uniquely structured data from the health and life sciences, in which covariates can take on more intricate forms such as multi-dimensional arrays. Embodiments have multiple uses in genomics, proteomics, metabolomics, multi-omics data integration, drug discovery, personalized medicine and predictive modeling, demonstrating the versatility and importance of tensor regression models to understand the associations between omics data and complex diseases.

The application relates to the technical field of protein-related data processing, in particular to a biological information processing system and method applied to synthetic biology. The method comprises the following steps: acquiring real-time proteomics data, performing polymorphic context decoding, and obtaining a protein expression context matrix; analyzing the interaction of behavior characteristics in the protein expression context matrix, and obtaining a multi-scale causal structure graph; performing structure-function transformation rule extraction on the multi-scale causal structure graph, and obtaining a function mapping unit set; evaluating the function mapping unit set, thereby forming a configuration decision graph; simulating the path in the configuration decision graph, performing path adaptability scoring, and obtaining a path adaptability feedback table; and optimizing the path adaptability feedback table, thereby obtaining an optimal expression configuration set. The application helps to improve the accuracy, stability and controllability of protein information in the structural analysis, function identification and regulation path construction process.

The invention provides a construction method for a tissue paraffin section space proteomics sequencing library. The method comprises the following steps: providing a solid phase carrier and an antibody-nucleic acid conjugate; attaching a tissue paraffin section sample to the solid-phase carrier, and carrying out section baking, dewaxing, hydration, decrosslinking and sealing treatment to obtain a sealed solid-phase carrier; incubating the antibody-nucleic acid conjugate and the closed solid-phase carrier to obtain a protein capture solid-phase carrier; carrying out permeabilization treatment and DNA synthesis on the protein capture solid phase carrier to obtain cDNA; releasing the cDNA from the solid-phase carrier, collecting the cDNA, and amplifying to obtain the sequencing library.

The invention relates to the technical field of biological detection, and provides an integrated gradient spinning fiber membrane for multi-omics analysis and a preparation method of the integrated gradient spinning fiber membrane. According to the fiber membrane, a coaxial electrostatic spinning technology is adopted, and functional zones are continuously formed on a single membrane by controlling the flow velocity of a shell layer (PLGA / chitosan solution) and a core layer (PEO / GPC3 aptamer solution): a nucleic acid enrichment end adsorbs and captures nucleic acid through positive electricity of chitosan, and a protein capture end recognizes a liver cancer marker GPC3 protein through a fixed GPC3 specific nucleic acid aptamer G625. According to the design, the capture environment of nucleic acid and protein is physically isolated, the problems of mutual interference and cross contamination of functional sites in a traditional method are fundamentally solved, and parallel and in-situ capture and enrichment of genomics, transcriptomics and proteomics information in the same trace biological sample are realized. The method is easy and convenient to operate, GPC3 protein is specifically captured and recognized, the nucleic acid enrichment capacity and the cross contamination resistance capacity are high, and a new strategy is provided for early diagnosis and precise typing of liver cancer.