34 results about "Protein identification" patented technology

The application relates to a method for constructing a target proteome spectrum library and application in micro target proteome analysis, and the method comprises the following steps: performing multiple times of mass spectrum scanning on a target proteome in a data-dependent acquisition mode without dynamic exclusion through different scanning ranges, so as to obtain mass spectrum data of the target proteome, wherein the mass spectrum data of the target proteome comprises primary spectra of multiple scanning ranges of the target proteome and secondary spectra of parent ions in each scanning range, which meet a preset threshold; and performing library searching based on the mass spectrum data of the target proteome to construct the target proteome spectrum library. The application constructs a spectrum library through mass spectrum data in a data-dependent acquisition mode without dynamic exclusion in multiple scanning ranges, performs library searching analysis on DIA data of a micro proteome, improves the success rate of DIA data matching, and further improves the accuracy and sensitivity of DIA data analysis, and improves the accuracy and sensitivity of micro protein identification.

This invention discloses a magnetic nanomolecular sieve, its preparation method, and its application in the enrichment of low-abundance proteins. The preparation method of the magnetic nanomolecular sieve proposed in this invention is simple to operate, employing a one-pot method. It simultaneously imparts magnetism while maintaining the surface properties of the sieve, i.e., its protein adsorption capacity, thus preserving its magnetic properties. This provides a simpler, faster, and more efficient technical method for large-scale sample processing. The low-abundance protein enrichment based on magnetic molecular sieves proposed in this invention can be applied to almost all sample types, effectively solving the interference caused by high-abundance proteins on the identification of low-abundance proteins during mass spectrometry detection, and increasing the number of protein identifications by 100%-700%.

The invention discloses an acidophilic protein recognition method, device and equipment. The method comprises the following steps: constructing a data set which takes acidophilic protein as a positive sample and takes thermophilic, halophilic and basophilic protein as a negative sample, and preprocessing the data set; dividing the data set into a training set and a test set according to a preset proportion; performing feature coding on the protein sequence in the data set by using a pre-trained ESM C model, extracting deep semantic features, and generating high-dimensional embedded representation; learning high-dimensional embedding representation through an adversarial network DCGAN-GP to perform data enhancement, and obtaining a pseudo-negative class sample; combining the pseudo negative sample and the data set to obtain a combined set, and inputting the combined set into a lightweight shared sparse hybrid expert model for training to obtain a trained lightweight shared sparse hybrid expert model; and inputting a to-be-identified protein sequence into the trained lightweight shared sparse hybrid expert model to obtain an acidophilic protein prediction result. According to the method, efficient prediction of the acidophilic protein can be realized.

The application belongs to the cross field of virology and bioinformatics, and particularly relates to a virus identification model construction method and system based on capsid proteins. The method collects capsid protein sequence data and non-capsid protein sequence data, and trains a capsid protein identification model; obtains capsid protein sequences with known structures, structure information and hierarchical classification information, and identifies a few-sample category with a sample quantity lower than a preset threshold; generates supplementary data of the few-sample category through a protein sequence design model and screens the supplementary data from the collected capsid protein sequence data, predicts the three-dimensional structure of the sequence in the supplementary data, and together with the corresponding classification information, forms a capsid protein supplementary data set for training a capsid protein classification model. The application alleviates the problems of data imbalance caused by insufficient data of the few-sample category and high classification difficulty of remote homologous proteins caused by single feature extraction, and is helpful to realize efficient preliminary identification of viruses.

The invention belongs to a proteomics analysis technology in the technical field of biology, and particularly relates to a single-cell-level proteomics analysis system and analysis method for a mass spectrum system. The analysis system comprises a sample pretreatment optimization subsystem and a chromatography-mass spectrometry acquisition optimization subsystem, the purpose of the sample pretreatment optimization subsystem is to efficiently and stably prepare a peptide fragment mixture capable of being used on a mass spectrum machine from a trace cell sample. The sample loss is reduced by improving the protein extraction and enzymolysis efficiency of trace (nanogram-level) and single-cell-level protein samples. By optimizing mass spectrum data acquisition parameters, the identification depth and quantity of proteins are improved while the analysis time is shortened (the flux is improved). By adopting the analysis system disclosed by the invention, the accuracy and reproducibility of quantitative analysis of the micro sample proteome can be remarkably improved.

The present disclosure provides a peptide library comprising stable isotope-labeled (SIL) peptides, wherein the peptides comprise or consist of the sequences according to Table A, Table B, or Table C.

The application discloses a molecular sieve membrane and a preparation method and application thereof in low-abundance protein enrichment, and the preparation method comprises the following steps: 1) polishing a support body to be smooth, drying after ultrasonic treatment; 2) adding an alcohol solution to the dried support body, then adding a certain amount of a coupling agent and ammonia water, taking out the support body after stirring, and drying the support body for standby use; 3) adding an alkali source into deionized water, completely dissolving, then adding a template agent, a stabilizer and a surfactant, and dissolving; 4) adding a silicon source and an alkali metal source into step 3), dissolving, then adding the dried support body in step 2), crystallizing at room temperature, then hydrothermally crystallizing, taking out the support body after the hydrothermal crystallization, washing, drying and calcining, so that the molecular sieve membrane is obtained. The molecular sieve membrane is simple in operation, the surface of the synthesized molecular sieve membrane is continuous and dense, the surface of the molecular sieve membrane retains the superior protein adsorption performance of the molecular sieve material, and the molecular sieve membrane can effectively improve the protein identification number.

The application discloses a method for collecting active proteins of water leeches, a detection method, an analysis method and application thereof, and relates to the technical field of biological medicines. The application provides the water leech seedlings treated by hunger and the living host treated by emptying, so that the water leech seedlings adsorb and suck the host, and then the active proteins are injected into the body of the host; the sample of the host after being sucked is collected, and then is made into a freeze-dried powder after being frozen and dried, so that the active proteins of the water leeches are obtained; the proteins are extracted from the freeze-dried powder, and then are subjected to enzymolysis treatment to generate peptide segments; the liquid chromatography-mass spectrometry technology is used to detect the peptide segments; and the mass spectrometry data are subjected to database searching analysis based on the protein sequence database of the water leeches and the host, so that the active proteins from the water leeches are identified and the relative content is determined. The application utilizes the characteristic that the active proteins are injected into the body of the host in the sucking process of the water leeches, and realizes efficient enrichment of the target proteins. In combination with the proteomics technology, high-throughput and high-sensitivity protein identification is realized.

The invention discloses a Wilson's disease low-abundance urine protein biomarker detection method, and belongs to the technical field of Wilson's disease, and the method comprises the following steps: S1, collecting a urine sample of a Wilson's disease to-be-detected person, and pre-treating the urine sample; s2, analyzing the pretreated urine sample by adopting a DIA mass spectrometry method, and identifying and quantifying global protein components in the urine sample in an unbiased manner; s3, screening candidate marker proteins significantly related to the Wilson's disease in the global protein components by means of statistics and bioinformatics; and S4, verifying the candidate marker protein obtained by screening through targeted mass spectrometry to obtain biomarker protein data. A DIA mass spectrometry technology is combined with liquid chromatography-ion mobility separation, unbiased deep analysis is carried out on urine proteomes of Wilson disease patients and healthy controls, detection parameters are optimized to improve the number and reliability of protein identification, and potential Wilson disease specific low-abundance protein markers are screened out.

The invention discloses a method for extracting protein from human plasma for protein analysis, and relates to the technical field of plasma treatment, the key points of the technical scheme are as follows: the method effectively removes high-abundance protein, lipid and other interfering substances in the plasma through two steps of protein precipitation with acetone, degreasing and high-abundance protein removal; the method is suitable for subsequent protein identification and quantitative analysis. A stable and reliable method is provided for pretreatment of the plasma sample, a technical basis is provided for further use of protein in plasma for various medical analyses, and further development of the technology in the plasma detection field is facilitated.

The application provides a kind of based on hybrid deep learning architecture Halophilic protein prediction method and system, it is related to bioinformatics analysis technical field, the method comprises: obtaining the protein sequence to be predicted;Use protein language model to extract its global semantic embedding feature;At the same time, based on the specific reduction amino acid alphabet, the sequence is re-encoded, and the reduction dipeptide frequency in three interval modes is counted to generate a local composition feature vector;After splicing two kinds of features, input the hybrid deep learning model composed of one-dimensional convolutional neural network and light weight Transformer encoder in series for processing, and finally output the prediction result by full connection layer.The application significantly improves the accuracy, generalization ability and computing efficiency of halophilic protein identification by fusing multi-scale sequence features and hierarchical hybrid network architecture, and provides an effective tool for intelligent mining of large-scale extreme environment enzyme resources.

The application provides a serum proteomics and machine learning-based Kawasaki disease intravenous immunoglobulin (IVIG) resistance biomarker combination and screening method, and belongs to the technical field of medicines. The method comprises the following steps: (1) on the basis of establishing strict inclusion criteria and typing criteria, collecting whole blood samples of IVIG reaction type and non-reaction type Kawasaki disease children before treatment; (2) using DIA proteomics technology for systematic screening and differential protein identification; (3) weighted co-expression network analysis, screening of protein modules significantly related to IVIG non-reaction phenotype; (4) combined with LASSO-Logistic regression and SVM-RFE for multi-step feature screening, identifying five biological markers significantly related to IVIG resistance: PLA2G4A, SNX17, PURB, CERS3 and CASP1 (5) based on the marker expression level, using the pROC package for ROC analysis and calculating AUC; (6) analyzing the correlation between the marker and the clinical index related to Kawasaki disease; (7) after limma processing in the independent transcriptome set GSE18606, using glm to construct a multivariate binary logistic regression prediction model and perform ROC analysis. Through independent transcriptome dataset verification, the biomarker combination screened by the application can realize effective prediction of Kawasaki disease IVIG resistance, and shows good prediction performance and clinical application value.

Methods and systems are provided for accurate and efficient identification and quantification of proteins. In an aspect, disclosed herein is a method for identifying a protein in a sample of unknown proteins, comprising receiving information of a plurality of empirical measurements performed on the unknown proteins; comparing the information of empirical measurements against a database comprising a plurality of protein sequences, each protein sequence corresponding to a candidate protein among a plurality of candidate proteins; and for each of one or more of the plurality of candidate proteins, generating a probability that the candidate protein generates the information of empirical measurements, a probability that the plurality of empirical measurements is not observed given that the candidate protein is present in the sample, or a probability that the candidate protein is present in the sample; based on the comparison of the information of empirical measurements against the database.

The present disclosure relates to methods and compositions for protein identification via peptide barcodes.

The invention provides a halophilic protein prediction method and system based on a hybrid deep learning architecture, and relates to the technical field of information analysis, and the method comprises the steps: obtaining a to-be-predicted protein sequence; extracting global semantic embedding features by using a protein language model; meanwhile, recoding the sequence based on a specific reduced amino acid alphabet, and counting reduced dipeptide frequencies in three interval modes to generate a local composition feature vector; and after the two types of features are spliced, inputting the spliced features into a mixed deep learning model formed by connecting a one-dimensional convolutional neural network and a lightweight Transform encoder in series for processing, and finally outputting a prediction result by a full connection layer. According to the method, by fusing the multi-scale sequence features and the hierarchical hybrid network architecture, the accuracy, generalization ability and calculation efficiency of halophilic protein recognition are remarkably improved, and an effective tool is provided for intelligent mining of large-scale extreme environment enzyme resources.

This invention belongs to the field of proteomics extraction, specifically relating to a proteomics extraction pretreatment combination, and more specifically, to a proteomics extraction pretreatment combination for extracting trace amounts of protein (nanogram level). This invention provides a proteomics extraction pretreatment combination comprising: a first reagent: cyclodextrin or a cyclodextrin derivative; a second reagent combination: an organic solvent, an acid, and (2-hydroxypropyl)-α-cyclodextrin and / or (2-hydroxy-3-N,N,N-trimethylamino)propylchloro-β-cyclodextrin hydrate; and a third reagent: sodium 3-[(2-methyl-2-undecyl-1,3-dioxolane-4-yl)methoxy]-1-propanesulfonate. Through the synergistic effect between the reagents and the reagent combination, the proteome of samples that are difficult to extract using existing technologies can be extracted. In particular, it can be used for nanogram-level proteomics samples. The combination of this invention achieves a peptide recovery rate of over 60% while simultaneously improving the depth of protein identification.

The invention discloses an identification method of low-temperature compost energy metabolism core protein, and belongs to the technical field of protein screening. In order to solve the problems of single screening dimension, inaccurate key protein identification and unclear mechanism analysis in the prior art, the invention provides a low-temperature compost energy metabolism core protein identification method, which comprises the following steps of: acquiring protein expression data of a low-temperature microorganism sample by utilizing a macro proteomics sequencing technology, screening differential expression proteins based on a set threshold value, and identifying a low-temperature compost energy metabolism core protein. The method comprises the following steps: identifying proteins in a significant enrichment pathway through GO function annotation and KEGG pathway enrichment analysis, carrying out importance ranking on proteins in significant enrichment related pathways by using a random forest model, screening key proteins in combination with the connectivity and network centrality of a protein interaction network, and finally identifying the energy metabolism core proteins in the low-temperature compost. The identification method provided by the invention has the advantages of strong systematicness, high screening accuracy, wide applicability and the like, and can provide key technical support for efficiency improvement and mechanism analysis of low-temperature compost.

A method for determining or verifying the identity of a protein based on a melting curve obtained from a nano-differential scanning fluorimeter (nanoDSF). The method uses a machine learning model trained on a set of proteins of known identity and includes acquiring, through use of nanoDSF, a set of data points representative of a thermal denaturation curve (melting curve) of a test protein, generating a set of features derived from the melting curve, and providing the set of features as inputs to the trained machine learning model to obtain a corresponding output indicative of an identity of the test protein. Methods for training the machine learning model are provided, as well as systems including processor-executable instructions that, when executed by a processor, cause the system to perform the method for determining or verifying protein identity.

The invention discloses a sea cucumber producing area characteristic protein identification method based on an unmarked data independent acquisition mass spectrometry technology, and belongs to the technical field of food science and analytical chemistry. The identification method of the sea cucumber producing area characteristic protein based on the label-free data independent acquisition mass spectrometry technology comprises the following steps: sea cucumber sample collection and protein extraction; digesting and grading peptide fragments; constructing a sea cucumber specific data dependence acquisition spectrum library; carrying out data independent acquisition mass spectrometry detection; carrying out protein identification and quantification on the data based on the spectrum library, identifying differentially expressed proteins, and identifying the sea cucumber producing area based on the differential proteins; the identification depth of the proteins obtained by the method exceeds 6,278, differential expression proteins and key metabolic pathways of Liaoning and non-Liaoning sea cucumbers can be accurately identified, and the method has the advantages of high coverage depth, good reproducibility and low cost, and is suitable for large-scale sea cucumber geographical traceability and quality authentication.

This invention discloses a key protein identification method based on two-stream supergraphs and multi-level gating in the field of multi-source bioinformatics. The method includes the following steps: S1, acquiring the target PPI network and multi-source biological heterogeneous attribute data, extracting network interaction edge data of the target protein, and constructing a basic PPI network view; S2, based on the PPI network view constructed in step 1, performing high-order topological information extraction based on local closed-loop subgraphs, thereby overcoming topological noise interference caused by inherent false positive edges in the basic interactive network, and deeply mining the synergistic interaction patterns of macromolecular complexes in spatial conformation. The algorithm of this invention exhibits good performance advantages in key protein identification, demonstrating that the dynamic fusion effect of high-order topology and multi-dimensional attributes is superior to single feature or homogeneous splicing strategies, providing a new approach and algorithm for solving the problem of accurate screening of key targets in complex biological networks.

The invention discloses an exosome protein recognition method based on multi-kernel learning and a fuzzy hypergraph model. The method comprises the following steps: 1, constructing and preprocessing a data set; 2, extracting and screening the optimal characteristics of the PSSM-DWT, the PSSM-AB and the PsePSSM; 3, fusing features by adopting HSIC multi-kernel learning to generate an optimal kernel matrix; 4, constructing a fuzzy hypergraph Laplacian support vector machine (H-FLapSVM) model, using kernel entropy component analysis reconstruction errors as fuzzy membership to suppress noise, and introducing a hypergraph Laplacian matrix to capture a sample high-order relation to complete training and prediction; and 5, performance evaluation. According to the method, multi-dimensional feature information is fused, the anti-interference capability of the model and the relation representation comprehensiveness are enhanced, and the exosome protein recognition precision is remarkably improved.

The invention belongs to the crossing field of virology and biological information technology, and particularly relates to a capsid protein-based virus recognition model construction method and system. The method comprises the following steps: collecting capsid protein sequence data and non-capsid protein sequence data, and training a capsid protein recognition model; acquiring a capsid protein sequence with a known structure, structure information and hierarchical classification information, and identifying a few-sample category of which the sample quantity is lower than a preset threshold value; the method comprises the following steps: generating a protein sequence design model, screening supplementary data of few sample categories from collected capsid protein sequence data, predicting a three-dimensional structure of a sequence in the supplementary data, and forming a capsid protein supplementary data set together with corresponding classification information for training a capsid protein classification model. According to the method, the problems of data imbalance caused by insufficient data of few sample categories, high difficulty in classification of distant homologous proteins caused by single feature extraction and the like are solved, and high-efficiency preliminary identification of viruses is facilitated.

The invention provides a top-down mass spectrum deconvolution method and system based on deep learning. The method comprises the following steps: acquiring protein sample mass spectrum data for model training; extracting multi-dimensional physical characteristics representing signal peak characteristics from candidate experimental envelopes of protein sample mass spectrum data and corresponding theoretical envelope models; constructing an isotope envelope similarity evaluation model based on a multi-scale convolutional neural network, and training the isotope envelope similarity evaluation model by using a label generated based on a theoretical fragment ion matching result until the isotope envelope similarity evaluation model meets a preset training termination condition, obtaining a trained isotope envelope similarity evaluation model; and screening real isotope envelope signals in the mass spectrum data to be processed by using the trained isotope envelope similarity evaluation model, and generating a single isotope mass list for protein identification. According to the invention, the accuracy of top-down mass spectrum deconvolution can be improved.

The application provides a key protein identification method and device based on a dynamic graph neural network, the method comprising: obtaining first original protein data, and constructing a dynamic protein interaction network with a time attribute; performing random walk sampling on the dynamic protein interaction network, and constructing a training corpus; using a trained graph convolutional neural network model to extract structural features of protein nodes; inputting the structural feature data into a pre-trained long short-term memory network model, and outputting time features of the protein nodes; and identifying key proteins through pattern classification according to the structural features and the time features. The application models a protein interaction graph as a dynamic graph, and learns structural features and time features of protein nodes on the dynamic graph by combining a graph convolutional neural network model and a long short-term memory network model, so that key proteins can be identified more efficiently.

Methods and systems are provided for accurate and efficient identification and quantification of proteins. In an aspect, disclosed herein is a method for identifying a protein in a sample of unknown proteins, comprising receiving information of a plurality of empirical measurements performed on the unknown proteins; comparing the information of empirical measurements against a database comprising a plurality of protein sequences, each protein sequence corresponding to a candidate protein among a plurality of candidate proteins; and for each of one or more of the plurality of candidate proteins, generating a probability that the candidate protein generates the information of empirical measurements, a probability that the plurality of empirical measurements is not observed given that the candidate protein is present in the sample, or a probability that the candidate protein is present in the sample; based on the comparison of the information of empirical measurements against the database.

The application discloses a kind of Top-Down mass spectrum protein identification methods based on deep learning, comprising: the Top-Down mass spectrum data of the complete protein form to be identified is preprocessed and encoded to obtain spectrum vector representation;By peak encoder, its encoding obtains spectrum latent feature representation;The precursor monoisotopic mass and the distribution of multiple charge state are jointly embedded to obtain prior constraint vector;With spectrum peak energy distribution, monoisotopic mass is adaptively segmented to obtain sub-section mass budget;Sequence decoder performs beam search constrained by mass budget in each subsection and splices into skeleton sequence;By diffusion model, low confidence area is iteratively refined in multiple steps, and the denoising process is jointly guided by global mass consistency, local fragment matching degree and modification site compatibility, and outputs complete protein form containing post-translational modification site annotation and confidence score thereof.The application does not need to be cut and database dependent, can identify long sequence and post-translational modification, significantly improve recognition accuracy and generalization.

The present disclosure relates to methods and compositions for protein identification via peptide barcodes.

A method and apparatus for monitoring and / or controlling the extent of denaturation and / or bond cleavages of proteins on any surface (e.g., biological tissues, biofilms, etc.). In one embodiment, a low power laser (e.g., a 5 mW, 362 nm diode laser) is directed through a biological sample to a photodetector. The sample is heated by a set of radiant heaters to between about 220° C. and about 250° C. in a time period of between 10 seconds to 60 seconds. The baseline transmissivity of the sample is monitored continuously throughout treatment of the biological sample via continuous monitoring of the signal voltage detected at the photodetector. Upon detection of increase in relative transmissivity in the biological sample, the heating treatment is concluded and the biological sample is removed for in situ protein identification as part of an imaging MALDI-MS measurement.

This application discloses a method for processing protein samples for mass spectrometry detection. The method involves processing the protein sample into peptides using only ultrasound to obtain a protein sample suitable for mass spectrometry detection, without the use of enzymatic digestion. By precisely controlling the power, time, and mode of ultrasound, the high shear force generated by ultrasonic cavitation directly acts on the peptide bonds of the protein, achieving random and non-specific breakage. This method significantly reduces the protein sample pretreatment time from hours in existing enzymatic methods to minutes, eliminates the need for expensive enzymes, significantly reduces detection costs, simplifies the operation process, and can generate peptides of varying lengths covering the entire protein sequence, providing complementary protein sequence coverage information to enzymatic methods. This offers a novel and efficient solution for protein identification and structural analysis.