437 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 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.

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 add 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 invention relates to the technical field of biomedicine, in particular to a hepatic cell differentiation degree evaluation method based on multi-omics data, which comprises the steps of sample collection and preprocessing, transcriptomics, proteomics and metabonomics data analysis, multi-omics data integration and modeling and result output. By integrating multi-level biological information, a multi-dimensional scoring model is constructed, the liver cell differentiation state is quantitatively evaluated, and a standardized grading system is provided for clinic. The method can solve the limitation of single omics analysis, improves the evaluation accuracy and reliability, has universality, can be popularized to other malignant tumor research, and assists precise medical development.

Methods for assessing disease, such as multi-omics methods, are described herein. A multi-omics method can integrate proteomic data, transcriptomic data, genomic data, lipidomic data, or metabonomic data. The method screens for a disease or a disease state. Also described herein are methods of screening a disease or a disease state from a biological sample. Multi-omics databases and methods of using them are also described herein.

The invention belongs to the field of animal vaccines, and particularly relates to a multi-epitope fusion protein aiming at multi-serotype laisseria parasuis antigen and application. The applicant carries out proteomics analysis on four clinical isolated strains of serotypes 4, 5 and 13 of the parasuis leather serotype 4, 5 and 13 with high toxicity and good immunogenicity, and eight antigen proteins are screened out of the four clinical isolated strains of the serotypes 4, 5 and 13 of the parasuis leather serotype 4, 5 and 13. And further analyzing the screened eight antigen proteins to finally obtain the antigen multi-epitope fusion protein with immunogenicity capable of recognizing three serotypes. When the fusion protein is used as a vaccine, the protection rate of clinically separated piglets infected with a serum type 4 strain HB04, a serum type 5 strain SJZ05 and a serum type 13 strain GD20 reaches 100%.

The invention provides a cancer subtype classification method based on a KAN network and multi-omics data, and the method comprises the steps: obtaining the multi-omics data, including genomics data, transcriptomics data and proteomics data, and constructing a test data set; carrying out feature extraction and integration on the multi-omics data by utilizing a Colmogolov-Arnod network model, and carrying out training to obtain a trained cancer typing model; in the model evaluation stage, a Monte Carlo discarding method is adopted to carry out multiple times of forward propagation so as to estimate the uncertainty of a model prediction result and improve the robustness of the model; and carrying out cancer typing prediction, and carrying out high-precision cancer typing on multi-omics data by using the trained Colmogolov-Arnod network model. According to the method, the problem of heterogeneity of multi-omics data can be effectively solved, the accuracy and robustness of cancer typing are improved, and the effect of personalized treatment is improved.

The invention provides a gene detection and phenotype verification health detection method and system, and the method comprises the following steps: collecting multi-source data of a user, locking genetic variation highly related to disease and individual specificity through gene sequencing, and building an individualized genetic risk scoring system; wherein the multi-source data comprises gene sequencing and proteomics data of a user; performing gene phenotype coupling analysis, integrating multi-dimensional data of genes, proteins or phenotypes of the user, and constructing a health assessment system with high confidence; the intervention decision generation is used for generating a decision to intervene the user; through gene-phenotype coupling analysis, the disease risk prediction specificity of healthy people is improved, compared with a conventional health monitoring technology, risks are found in advance, the health behavior compliance rate of a user is increased, seamless connection with mainstream wearable equipment and an electronic medical record system is supported, and the utilization rate of data is increased.

The present invention relates to the discovery that lysosomal damage is an inducer of stress granule (SG) formation and that SG formation and mTOR inactivation are coordinated through a noncanonical function of mammalian Atg8 proteins (mAtg8s), primarily associated with autophagosomes but also modifying other stressed membranes in a process referred to as Atg8ylation. Proteomics indicated recruitment to damaged lysosomes of the core SG proteins NUFIP2 and G3BP1 along with the GABARAP subset of mAtg8s. GABARAPs interacted with NUFIP2 and G3BP1 and lipidation of mAtg8s was needed for their recruitment to damaged lysosomes whereupon NUFIP2 contributed to mTOR inactivation via the Ragulator-RagA / B complex. The shared function of NUFIP2 and G3BP1 in SG formation and mTOR inactivation was reflected in competition between their roles in SGs and mTOR regulation. Thus, cells employ Atg8ylation to control and coordinate SG and mTOR responses to lysosomal damage. Methods of treating autophagy modulated disease states and / or conditions are described, particularly cancer using inhibitors of NUFIP2 and / or G3BP1, alone or in combination with lysosomotropic agents.

A chemical approach is provided to profile uptake of compounds in cells, alone or in combination with other analytical techniques. The disclosure is particularly well suited for detecting cellular uptake of metabolites such as fatty acids. The disclosure can be carried out, applied in solution or on a surface, in bulk or on single cells, and is compatible with one or more additional techniques such as protein analysis. The disclosure is exemplified by probing fatty acid influx alone and in combination with proteomics analysis on a single-cell barcode chip to identify a combination therapy for inhibiting fatty acid metabolism and treating cancer.

The invention relates to a cell lysis solution, which is prepared from the following active components: 0.08 to 0.2 percent of DDM (Dichloromethane), 35 to 45 mM of CAA (Ceamic Acid Acetic Acid), 5 to 15 mM of TCEP (Trichloroethylene Phosphate), 85 to 100 mM of TEAB (Tetraethyl Ammonium Bromide), 0 to 0.25 percent of Protease MAX and 0 to 0.25 percent of RapiGest SF (SF). The invention further provides a single-cell protein pretreatment method based on the digital microfluidic system, the cell lysis solution is used for lysis, the number of detectable proteins and the number of detectable peptide fragments can be high, and the method can be well used for single-cell proteomics analysis.

The invention discloses an application of cinobufagin in preparation of a medicine for inhibiting bone metastasis of lung cancer. It is proved for the first time that 1) in-vitro cell experiments prove that cinobufagin can effectively inhibit proliferation and migration ability of lung cancer bone metastasis cells and promote cell apoptosis; 2) a mouse left ventricular injection bone metastasis model and a tibia injection model show that cinobufagin can significantly inhibit the development of lung cancer bone metastasis and osteolytic bone injury; (3) the specific regulation and control mechanism of cinobufagin for inhibiting bone metastasis of lung cancer is explored, and it is found through combined analysis of IP-MS proteomics and LiP-SMap proteomics that cinobufagin may target pyruvate carboxylase to play the function of cinobufagin; and 4) further verifying that cinobufagin can be combined with PC to inhibit bone metastasis of lung cancer through a drug affinity reactive target stability experiment and a heat transfer experiment. The research provides a new target spot and a treatment strategy for inhibiting bone metastasis of lung cancer.

The invention 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, and performing polymorphic context decoding to obtain a protein expression context matrix; analyzing interaction of behavior characteristics in the protein expression context matrix to obtain a multi-scale causal structure map; executing structure-function transformation rule extraction in the multi-scale causal structure atlas to obtain a function mapping unit set; performing evaluation based on the function mapping unit set so as to form a configuration decision diagram; simulating paths in the configuration decision diagram, and performing path adaptability scoring to obtain a path adaptability feedback table; and optimizing the path adaptability feedback table to obtain an optimal expression configuration set. According to the method, the precision, the stability and the controllability of protein information in the process of structural analysis, function recognition and regulation path construction can be improved.

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.

Techniques are disclosed herein that encompass image pre-processing and a semi-supervised clustering for optimization and analysis of immune-enriched single-cell proteomics data generated via multiplexed imaging technologies. This is achieved through an image pre-processing pipeline, which converts image data contained in one type of file (e.g., .mcd) into another type of file (e.g., .tiff) and removes artifact signals from the image data using various algorithms to generate improved image data. Thereafter, a semi-supervised clustering pipeline analyzes the improved image data using various techniques, including implementing a supervised algorithm to identify metaclusters such as general immune phenotypes (e.g., CD4−T-cells, Macrophages, Neutrophils, etc.) as well as non-immune phenotypes while implementing an unsupervised algorithm that enables the identification of specific subclusters and a more in-depth cellular status characterization.

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 present disclosure relates to proteomics, including the detection of immune cell target proteins as examples that the disclosed methods can be used for protein detection. Methods, compositions, and systems are described for identifying the presence of proteins, including detecting immune cell proteins from a sample using a combination of multiplex immuno-PCR with antibodies conjugated to oligonucleotides comprising an antibody-specific barcode flanked by PCR priming sites; detection of barcoded amplicons obtained from immuno-PCR with encoded padlock probes comprising codes specific for the barcodes / target protein; rolling circle amplification (RCA) of the circularized padlock probes; and determination of the sequence of the codes amplified by RCA by next generation sequencing or using fluorescently labeled hybridization probes.

The invention discloses an abdominal aortic aneurysm risk screening method based on HbA1c multiple risk markers, and aims to solve the problems that AAA screening in the prior art depends on a single marker, is lack of nonlinear analysis and is insufficient in application tool, a comprehensive risk prediction model is constructed by integrating HbA1c, proteomics data and non-genetic factors, a nonlinear risk relationship is revealed, and the risk of the abdominal aortic aneurysm is predicted to be the risk of the abdominal aortic aneurysm of the abdominal aortic aneurysm of the abdominal aortic aneurysm. And an application tool is developed, visual display of the morbidity probability and the risk level of n years is provided, a personalized screening strategy is supported, and the accuracy and practicability of AAA early screening are improved.

The invention discloses a construction method of a model for predicting an esophageal squamous cell carcinoma intervention effect. The construction method comprises the following steps: acquiring detection data of a predictive factor in a sample; the predictive factors at least comprise the content or the expression level of a depletion marker of a functional CD8 T cell in a sample and the content or the expression level of a C1QC + macrophage immune checkpoint molecule; and predicting the intervention effect of the esophageal squamous cell carcinoma according to the detection data of the predictive factor. Establishing a space proteomics map of a complex space tissue of a tumor microenvironment (TME) during treatment; key spatial features which are related to specific cell behaviors and influence treatment results are determined and layered; a powerful prediction model of drug response is developed by utilizing a machine learning technology, and the area under curve (AUC) can reach 97%.

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.

The invention relates to an individual future health state prediction method and device based on proteomics, and a medium. The method comprises the following steps: obtaining and preprocessing proteomics data; constructing a shared network: constructing an individual past and future common disease condition prediction neural network based on a twin network framework; backbone network construction: constructing a health specific outcome prediction neural network based on a multilayer perceptron method; and the health assessment network synthesizes the shared network and the backbone network architecture, carries out fusion and fine tuning on the extracted features in a potential space, and outputs the future risk assessment probability of various health specific outcomes through further training, updating and fine tuning of network parameters. Compared with the prior art, the method focuses on a proteomics data processing and modeling method, takes past and future health estimation as prior information, and realizes individualized health condition evaluation with multiple disease species and death as the outcome.

The invention discloses an application of CLASP2 protein in preparation of a medicine for promoting spinal cord injury repair. According to the invention, a bioinformatics and phosphorylated proteomics technology combined method is utilized to screen out that the functional protein of curcumin for promoting spinal cord injury repair is CLASP2 protein. The molecular biological technique verifies that curcumin regulates and controls microtubule stability by increasing phosphorylation of CLASP2 protein to promote functional repair after spinal cord transection injury. Therefore, the curcumin can regulate and control microtubules by promoting the phosphorylation of the CLASP2 to dynamically promote the motor function repair after the spinal cord injury of the mouse. The method has an important application value.

The invention provides a novel marker for IgG4 related diseases and application of the novel marker, and particularly relates to application of OX40 serving as a biomarker to preparation of products for diagnosing IgG4-RD, evaluating disease activity of the IgG4-RD and evaluating recurrence risk prognosis of the IgG4-RD. On the basis of Olink proteomics analysis, expression of PD1, OX40, CCL19 and MMP12 in IgG4-RD patients is remarkably up-regulated, and the disease activity of the IgG4-RD patients is remarkably up-regulated. A verification queue verifies that the plurality of biomarkers have relatively high diagnosis efficiency, sensitivity and specificity, are in positive correlation with the IgG4 level, the inflammation index and the disease activity, and are in positive correlation with specific visceral organ enumeration. The invention finds that serum OX40 can be used as a baseline for predicting disease recurrence and evaluating a prediction index of recurrence risk.

The invention discloses an active tuberculosis diagnosis method based on proteomics and metabonomics. The method comprises the following steps: collecting a serum sample of a suspected active tuberculosis patient; detecting specific proteins in the serum sample by using a proteomics technology; detecting specific amino acids in the serum sample by using a metabonomics technology; carrying out standardization treatment on the obtained protein, amino acid and bile acid data; establishing a diagnosis model by using a machine learning algorithm; inputting data of a to-be-diagnosed sample into the model, and calculating a probability value of the to-be-diagnosed sample suffering from active tuberculosis; according to a preset threshold value, whether the sample provider suffers from the active tuberculosis or not is judged. By comprehensively analyzing multiple omics data, the sensitivity and specificity of diagnosis are greatly improved; by utilizing a machine learning algorithm, the disease risk can be objectively and quantitatively evaluated, and human misjudgment is reduced; the detection process is simple and fast, the sample demand is small, and clinical popularization and application are facilitated.

The invention belongs to the technical field of biology, and relates to a proteomics-based adenoid hypertrophy grading diagnosis biomarker and application thereof. Comprising BET1, RPS6KA4, HSPA1A, DPP4, OSTC, MRPL21, CMC4, RPL37, PSPH, PTPRS, MFAP5, KIAA1143, EMC4, ZNF460, ABHD13, SH3BP5L, GPC3, DNAJC19, DTD1, H3-7, MKNK1, MFAP2, WRAP73, IGLV4-69, P4HA1, S100A9, QSOX2, RNF25, RMND5A and MRPS28. The invention further discloses a preparation method of the high-efficiency and high-efficiency biomedical kit. The screened biomarker can accurately diagnose the severity of adenoid hypertrophy in a non-large-area invasive manner, and especially can accurately distinguish severe adenoid hypertrophy from moderate adenoid hypertrophy.

The invention belongs to the technical field of in-vitro diagnostic reagents, and particularly relates to application of a detection reagent for GDF15 protein in extracellular vesicles in preparation of a prostate cancer diagnosis and / or staging kit. According to the invention, enrichment and proteomics detection are carried out on plasma EVs of patients with prostatic cancer (PCa) and benign prostatic hyperplasia (BPH), and through bioinformatics analysis, a potential EV protein marker GDF15 which can be used for early diagnosis of PCa is screened for the first time. The GDF15 protein is verified in a plurality of groups of new clinical queues by further adopting an immunological technology and a targeted quantitative mass spectrometry technology, and the distinguishing ability of the GDF15 protein to PCa and BPH and the distinguishing ability of the GDF15 protein to different PCa stages are determined. The method is expected to promote noninvasive precise diagnosis of clinical PCa, and has a good application prospect.

The invention relates to the technical field of biomedicine. The invention provides a phosphorylated protein marker for angiogenesis of glioma and application of the phosphorylated protein marker. Through a series of analysis such as iTRAQ quantitative phosphorylated proteomics, KEGG signal channel enrichment, a kinase spectrum, glioblastoma transcriptome high-throughput sequencing data and immunohistochemistry, it is determined that significantly different phosphorylated proteins and related signal channels can significantly affect neuroglioma angiogenesis; and the phosphorylated protein changes along with tumor progression, and is a phosphorylated protein marker and a potential therapeutic target for glioma angiogenesis.

The invention belongs to the technical field of biological analysis, and particularly relates to a co-extraction method for mRNA and protein in a trace sample and application. The co-extraction method mainly comprises the following steps: capturing messenger RNA by adopting Oligo dT magnetic beads so as to separate the messenger RNA from protein, and then selecting a proteome pretreatment working process based on SP3 magnetic beads. According to the technology, Oligo dT magnetic beads are innovatively selected to separate messenger RNA. The surface of the Oligo dT magnetic bead is modified with an Oligo dT sequence, so that the Oligo dT magnetic bead can selectively capture mRNA. The mRNA separation and purification technology has the characteristics of high efficiency, high purity and simplicity and convenience in operation. The problems that mRNA and protein in a trace sample are difficult to separate, the RNA amount is small, follow-up analysis cannot be met, and the proteomics pretreatment process is low in efficiency can be solved.