40 results about "Protein Databases" patented technology

The invention discloses a universal HLA antigen presentation prediction method based on a protein language model and a multi-modal neural network, which comprises the following steps: firstly, extracting verified HLA binding peptide fragment sequences from an immune epitope database, generating equivalent non-epitope peptide fragment sequences in combination with a protein database, and constructing an HLA-I class and HLA-II class balanced data set; then, extracting sequence embedding characteristics and contact graph structure information of the peptide fragment sequence by utilizing a protein language model; the method comprises the following steps: processing a protein map constructed by a contact map through a map neural network to obtain global structure features, and meanwhile, carrying out regional convolution and residual convolution processing on sequence embedding by adopting a one-dimensional convolutional neural network (1DCNN) to extract local context sequence features; the method effectively fuses sequence semantics and space structure information, improves the accuracy and generalization ability of HLA antigen presentation prediction, and is suitable for immune recognition modeling tasks under various HLA subtypes.

The invention relates to a protein implicit binding site prediction method based on deep learning. The method comprises four stages of data acquisition, feature engineering, model training and verification application. The method comprises the following steps: firstly, acquiring initial data from a protein database, performing data screening and sequence redundancy elimination, performing standardization processing on a PDB structure file, and generating a sample; then, constructing a composite feature space containing basic protein features and evolutionary conservative features; thirdly, feature dimension reduction is achieved through a hierarchical feature conversion and integrated feature selection method, and a model is constructed; finally, verification and application show that the method obtains 99.44% prediction accuracy on 954 non-redundant protein structures, ROC-AUC and PR-AUC both reach 0.9998, and the overlapping rate with a BioLiP database is 44.4%. The method can accurately identify potential drug binding sites, especially shows good generalization ability for drug targets difficult to prepare, and has important application prospects.

A protein complex model global quality evaluation method based on a deep fusion network belongs to the field of bioinformatics and computer application, and comprises the following steps: firstly, setting specific thresholds for maximum sequence redundancy, resolution and residue length in a protein database RCSB PDB to screen out a natural protein structure; for each protein structure, generating a disturbance structure model by using an HDck and xTrimoMultimer docking algorithm and combining a chain orientation disturbance strategy; then, extracting evolutionary features, physical features and geometric features based on each protein structure model, and constructing a deep fusion network fusing a deep convolutional neural network, an axial attention mechanism network and a graph attention network so as to fully capture local and global topological information of a protein compound; and finally, a global quality score is obtained through prediction of a decoding module. According to the method, the global topological information of the protein complex model can be effectively captured, so that the quality of the protein complex model is evaluated.

The application belongs to the field of bioengineering. The application provides a molecular modification design method for improving the hydrophobicity of the catalytic center of bacillus subtilis protease, which comprises the following steps: searching for a resolved bacillus subtilis protease crystal structure in a protein database PDB, generating an enzyme-substrate complex by docking a hexapeptide substrate through a Glide mode of software, calculating the polar charge of each amino acid residue of polar atoms N and O and H combined thereof within a 10 angstrom range of the catalytic center amino acid of the bacillus subtilis protease by using a PPC force field, calculating the absolute value sum of the polar charge, selecting a potential mutation site of the bacillus subtilis protease, virtually mutating the potential mutation site into a large steric hindrance nonpolar amino acid, calculating the absolute value sum of the polar charge of polar atoms N, O and H within a 10 angstrom range of the catalytic center amino acid of the single-point mutant by using the PPC force field, selecting a virtual bacillus subtilis protease single-point mutant, expressing the mutant and iteratively mutating, re-expressing, obtaining a modified bacillus subtilis protease mutant, and verifying the hydrophobicity. The molecular modification design method can quickly and effectively reduce water molecules in the catalytic center of the bacillus subtilis protease and improve the hydrophobicity thereof. It has been verified that the enzyme modification method is effective and reliable.

The invention belongs to the technical field of biology, and particularly relates to a universal method for screening protein cancer biomarkers and application thereof. The universal method for screening the protein cancer biomarkers comprises the following steps: (1) preparing a specific enrichment material to carry out peptide fragment enrichment on an actual biological sample, and carrying out primary data processing through LC-MS (Liquid Chromatography-Mass Spectrometer); and (2) further retrieving the data obtained by the primary processing through a protein database, and screening target proteins with research potential for subsequent qualitative and quantitative research. The method disclosed by the invention can be used for simply, conveniently and quickly screening potential protein type biomarkers, and the research efficiency of the biomarkers is improved.

The invention discloses an oligomeric protein de novo design method and system, terminal equipment and a computer readable storage medium. The method comprises the steps that functional characteristics of target protein are acquired, and a first design rule is determined based on the functional characteristics; based on the first design rule, extracting a first protein sequence and a second protein sequence from a natural protein database, and determining a second design rule; generating a candidate mutation sequence library according to the first design rule and the second design rule; performing structure prediction and stability calculation on the sequences in the candidate mutation sequence library, and screening out optimized sequences meeting preset requirements; performing functional characteristic simulation verification on the optimized sequence, and if the verification is passed, outputting a final target protein sequence; otherwise, the verification result is fed back to the protein sequence optimization model for iterative optimization design. The problem that the efficiency is low when the oligomeric protein is designed is solved, and the effect of efficiently designing the oligomeric protein from the beginning is achieved.

The invention discloses a preparation method of polypeptide with antioxidant activity, and relates to the technical field of biology. The preparation method comprises the step of preparing polypeptide with an amino acid sequence shown as SEQ ID NO.3 through a solid-phase synthesis method. According to the invention, a plurality of antioxidant active peptides are obtained by using separation and purification and mass spectrum identification technologies and by means of a saury protein database; multiple rounds of screening are carried out by utilizing the DPPH free radical scavenging rate, reducing power, water solubility, biotoxicity and other properties of the active peptide; the interaction between the antioxidant active peptide and the receptor MPO is explored by combining molecular docking, and the in-vitro antioxidant activity of the polypeptide is verified through solid-phase synthesis and cell experiments. Results show that the active peptide obtained through screening has in-vitro antioxidant activity, has good water solubility and is free of biotoxicity.

The invention discloses a discovering method of a heterogeneous entanglement element and application of the heterogeneous entanglement element in topological protein synthesis. Through automatic screening and manual verification of a protein structure database, a plurality of protein heterogeneous entanglement elements are systematically found, the efficiency of mediating protein isohydrocarbon formation is identified, and a richer tool library is provided for protein mechanical coupling. The protein heterogeneous entanglement element not only can efficiently form single-structural-domain Soxhlet hydrocarbon, but also can tolerate fusion of functional proteins, does not affect the folding structure and activity of the protein, and can be used for designing a mechanical coupling fluorescence resonance energy transfer fluorescent protein pair and a bispecific antibody analogue to realize four-level structure regulation and control and multifunctional integration.

This invention proposes a method for screening bisphenol A-specific peptides using molecular simulation, belonging to the field of food safety technology. The method includes the following steps: 1) Through protein stacking and molecular docking, the molecular structure and interactions of bisphenol A-protein cocrystal compounds in a protein structure database are comprehensively analyzed to obtain the receptor protein and the original parent chain; 2) Based on the original parent chain obtained in step 1), the parent peptide is obtained by truncation of the parent peptide segment by analyzing the main active amino acids in the molecular docking results; 3) By performing virtual amino acid mutations on the parent peptide obtained in step 2), a peptide library specifically binding to bisphenol A is constructed, and molecular dynamics is used for preliminary screening of the peptides; 4) The peptide chains screened in step 3) are used as specific recognition elements, and peptides labeled with fluorescein isothiocyanate are used to form specific recognition probes. The specific recognition probes are mixed with bisphenol A, and the peptide sequences with the strongest specific binding ability to bisphenol A are screened based on the different fluorescence differences. This invention is mainly applied to the screening of specific recognition elements for toxic and harmful small molecules, active ingredients, and functional factors. This method avoids the complex preparation steps of antibodies and the cumbersome screening process of aptamers, achieving high-throughput design of specific recognition elements.

The invention discloses a self-supervised learning pre-training method and a self-supervised learning pre-training system based on protein size prompt. The self-supervised learning pre-training method comprises the following steps: firstly, collecting unlabeled protein data from a protein database; encoding the protein size information into a prompt vector by using a size prompt adapter, and embedding the prompt vector into a protein encoder; then, based on the graph or point cloud representation of the protein, executing a mask prediction task to complete pre-training; and finally, performing fine adjustment on the pre-training model in a downstream task, and optimizing the specific task performance of the pre-training model. According to the method, the problem of inconsistent data distribution between the pre-training and the downstream task is remarkably reduced through protein size prompt, so that the universality of the pre-training model and the performance of the downstream task are improved. The method has high data efficiency, model universality and expansibility, and is suitable for various tasks such as protein function prediction and binding site detection.

The invention discloses a construction method of a jellyfish toxin metalloproteinase substrate peptide library, and relates to the technical field of biology. The construction method comprises the following steps: inducing mice with different dermatitis degrees by using a metalloproteinase inhibitor and jellyfish toxin; skin tissues with different dermatitis degrees are collected for proteome identification, and substrate membrane proteins of jellyfish toxin metalloproteinase are analyzed and screened through bioinformatics; screening by utilizing a protein database to obtain a potential substrate sequence in the substrate membrane protein; and screening sequences from the potential substrate sequences according to application conditions to construct a peptide library, and synthesizing the jellyfish toxin metalloproteinase substrate peptide library. The sequence contained in the jellyfish toxin metalloproteinase substrate peptide library constructed by the method is clear, the effect that the prepared polypeptide is hydrolyzed by toxin is remarkable, and the application prospect of the peptide library in jellyfish stings research is further guaranteed.

According to the cyclopeptide capable of specifically recognizing sucralose and the computer-aided optimization design method thereof, through computer simulation, molecular docking, molecular dynamics, deep learning and other technologies, the combination free energy of molecular docking score and molecular dynamics simulation is used as a judgment basis, and the cyclic peptide capable of specifically recognizing sucralose is obtained without a wet test. And the cyclopeptide with high affinity to target small molecules such as sucralose can be rapidly and efficiently designed. Comprising the following steps: extracting a high-quality protein structure from a protein database, splitting the high-quality protein structure into oligopeptide fragments and carrying out geometric fingerprint calculation; through molecular docking with a target small molecule, an optimal peptide fragment is selected and combined for recombination, and the space structure is optimized; and reconstructing a cyclic peptide sequence through information of the affinity layer, the adjacent layer and the shell layer obtained through calculation to obtain the high-affinity cyclic peptide for targeted recognition of the target small molecules. The design cost is greatly reduced, the design success efficiency is improved, and the method has a wide prospect when being used for identifying and detecting sucralose in small molecules such as environmental sewage.

The invention discloses a method for exploring the influence of electromagnetic waves on a protein structure directly based on molecular dynamics simulation. The method comprises the following steps: acquiring target protein from a protein database; constructing a simulation system; adding a protein force field and a water molecule model; solvent molecules and ions are added to neutralize system charges, and energy minimization treatment is carried out; pre-balancing is carried out under the NVT ensemble and the NPT ensemble; performing molecular dynamics simulation on the simulation system, and storing an atomic coordinate trajectory; and performing post-processing on the track file, extracting distance time sequence data between atoms or residues, screening out atom pairs or residue pairs meeting conditions, performing Fourier transform analysis, and judging whether the possibility of resonance with the electromagnetic wave with the specific frequency exists or not. According to the method, the interaction between the protein and the electromagnetic field can be revealed under the atomic resolution without consumption of experimental materials, and an efficient and reliable molecular-level theoretical basis is provided for evaluating the biological effect of environmental electromagnetic radiation.

This invention relates to a peptide sequence generation model and method utilizing intermediate sequence semantic masking (MSA) and diffusion masking mechanisms, specifically a peptide sequence AI model training method incorporating evolutionary information. It innovatively introduces intermediate sequences to address the problem that peptide sequences are too short to directly extract evolutionary information. First, an MSA is constructed for the peptide dataset. For sequences where an effective MSA cannot be constructed, their sequences are compared with a protein database to select intermediate sequences with high homology and appropriate length. Then, MSA data of these intermediate sequences is constructed to guide the AI ​​model in capturing evolutionary features. Model training employs a diffusion language masking mechanism and an MSA Transformer. All regions are randomly masked in the peptide MSA, and peptide mapping regions are masked in the intermediate MSA, enabling embedding modeling and generation of peptides. This method can capture sparse peptide evolutionary information, achieving semantic modeling and efficient generation of peptides, and is applicable to peptide sequence optimization and synthesis in fields such as drug design and protein engineering.

The present invention provides a method and system for predicting protein toxicity based on sequence information. The method comprises obtaining multiple protein sequences from a protein database; performing feature calculation on all protein sequences to obtain protein feature vectors for six categories; concatenating the six feature vectors by column to obtain a first feature vector; performing dimensionality reduction screening on the first feature vector to obtain a second feature vector of the target dimension; using the second feature vector to train a graph attention-based neural network model, and using the trained neural network model as a protein toxicity prediction model; inputting a new protein sequence into the protein toxicity prediction model to obtain a toxicity prediction result output by the protein toxicity prediction model. The present invention is adaptable to different types of protein sequences and accurately predicts protein toxicity by learning common sequence patterns.

The application provides a method for identifying a breakable cross-linked peptide segment, and the method comprises the following steps: S1, extracting characteristic double peaks with a mass difference corresponding to a fixed mass difference of a cross-linking agent in a spectrum, taking the mass corresponding to the characteristic double peaks as the mass of an alpha peptide segment, obtaining a candidate mass pair composed of the mass of the alpha peptide segment and the mass of a beta peptide segment based on the parent ion mass corresponding to the breakable cross-linked peptide segment, and the mass of the beta peptide segment is complementary to the mass of the alpha peptide segment; wherein the parent ion mass is obtained by calibrating a primary spectrum of the breakable cross-linked peptide segment; S2, according to the candidate mass pair obtained in step S1, screening a plurality of candidate peptide segments respectively equal to the mass of the alpha peptide segment and the mass of the beta peptide segment from a protein database according to the mass of the alpha peptide segment and the mass of the beta peptide segment in the candidate mass pair, respectively forming paired candidate cross-linked peptide segments by combining all candidate peptide segments of the alpha peptide segment with all candidate peptide segments of the beta peptide segment to form a candidate cross-linked peptide segment set, matching and scoring the cross-linked peptide segments in the candidate cross-linked peptide segment set according to a preset fragment ion, and selecting the cross-linked peptide segment with the highest score.

The present invention discloses a Cathelicidin family polypeptide derived from aquatic mammals. The polypeptide is obtained by mining the proteome database of aquatic mammals. The amino acid sequence of the polypeptide is as shown in SEQ ID NO: 1, SEQ ID NO: 2, or an amino acid sequence having more than 90% homology with SEQ ID NO: 1 or 2. The present invention intercepts the mature peptide segments of the Cathelicidin family by sequence alignment and mining of the protein database of aquatic mammals, and obtains the polypeptides BM-CATH and LV-CATH. The antibacterial polypeptides BM-CATH and LV-CATH designed and synthesized by the present invention have a broad antibacterial spectrum, a fast bactericidal speed, low hemolytic activity against sheep red blood cells, and low cytotoxicity to mammalian cells. They can be applied to the treatment of common bacterial infections and drug-resistant bacterial infectious diseases, and are excellent alternative drugs or adjuvant drugs for existing anti-infection treatments.

A greedy approach to identifying peptides with multiple post-translational modification is provided. A method includes extracting tags from input data and reducing information indicative of a protein database. The extracting includes converting peaks in tandem mass spectra of the input data into a weighted directed graph, resulting in extracted tags. The tags represent sequential amino acids. The reducing includes determining respective coverages of proteins in the protein database using the extracted tags. Further, the method includes locating a selected tag in an indexed database configured for protein candidate retrieval and scoring ones of the proteins that comprise the selected tag. The selected tag is selected from the extracted tags. Further, the method includes using a greedy approach process that characterizes post-translational modification patterns of the selected tag based on the scoring. The method also includes, based on a result of the greedy approach process, implementing a quality control process.

The present invention discloses a mass spectrometry method for large-scale discovery of D-type proteins, comprising: obtaining conformation-resolved proteomics data of a biological sample; extracting features from a data set generated by searching a protein database for the amino acid sequences in the conformation-resolved proteomics data to establish a feature set for protein sample isomeric modification retrieval; for the feature data of the same amino acid sequence, deleting data with a retention time difference less than 2 minutes and / or a collision cross-sectional area difference less than 1.5% to screen out proteins or polypeptides with stereoisomers, and obtaining a preliminary screening result by calculating the isomerization ratio and quantity of each protein; using means such as an isomeric signal amplification algorithm and isomerase to perform isomeric verification on the preliminary screening result to generate final D-type protein data. The present invention can accurately identify protein stereoisomeric modifications, improve the reliability and accuracy of large-scale identification of D-type proteins; quickly process large-scale proteomics data; and has universality.

The invention discloses a protein design method and device based on symmetric self-game online reinforcement learning in the technical field of bioinformatics and artificial intelligence. The protein design method based on symmetric self-game online reinforcement learning comprises the following steps: inputting main chain structure information of a target protein, constructing a training set based on a protein structure database, and representing a protein structure as graph data; constructing an isotropic graph neural network EGNN as an infrastructure of a sequence generator, and initializing a first strategy model, a second strategy model and a reference model; and symmetric self-game online training is carried out. According to the protein design method based on symmetric self-game online reinforcement learning, the limitation of existing static supervised learning and single-agent reinforcement learning is overcome, and collaborative optimization and sustainable evolution of a protein sequence under multi-objective constraint are realized.

The present invention discloses a protein structure prediction method, comprising: extracting a target sequence from a protein file to be tested; matching the target sequence against a protein database of known structures to find a matching sequence; obtaining a matching structure of the matching sequence based on the matching sequence; constructing an initial three-dimensional structural model of the target sequence based on the matching sequence and its matching structure; combining unmatched sequence segments of the target sequence with a portion of adjacent matched sequence segments to form a sub-target sequence; and searching for matching subsequences and their structures of the sub-target sequence in a protein database of known structures; and filling in missing portions of the initial three-dimensional structural model based on the found matching subsequences and their structures to obtain the three-dimensional structure of the protein file to be tested. The protein structure prediction method of the present invention can more accurately predict the structure of a protein.

The invention relates to the technical field of bioinformatics, in particular to a construction method of a dehalogenation microorganism functional genome database and an analysis method of dehalogenation microorganisms. The construction method comprises the following steps: obtaining a dehalogenation reaction related gene; based on a Uniprot protein database, obtaining a seed sequence and a homologous sequence of a family to which a dehalogenation reaction related gene belongs to obtain a core database; comparing the core database with a homologous protein database, and combining the sequence of the dehalogenation functional gene family obtained by comparison with the core database to obtain a preliminary database; and comparing the preliminary database to a Refseq database of microorganisms in NCBI, and carrying out classification, homologous sequence merging and redundancy elimination to obtain a complete database. The accurate dehalogenation function gene database is constructed based on the analysis and search results of a plurality of databases, can be used for conveniently and accurately analyzing the dehalogenation microorganism function community in the environment, and has important application value.

The invention discloses an L-carnitine synthetase gene and application thereof, and belongs to the field of bioinformatics. According to the invention, a three-dimensional structure of a fourth-step enzyme gene synthesized by known L-carnitine of fungi (or animals) is found in a PDB protein database, and then the three-dimensional structure of the protein is targeted to an arabidopsis AT3G24430 gene and a white crocus Cla01G1824.1 gene in Foldseek, so that the protein is obtained. The AT3G24430 gene and the Cala01G1824.1 gene are confirmed and verified to be key enzyme genes for related steps of biosynthesis of L-carnitine in arabidopsis thaliana and white crocus sativus for the first time through experiments, a new target gene resource is provided for biological research on synthesis of L-carnitine, and the blank of research on synthetic pathways of L-carnitine in the plant field is filled up; a new cognition is provided for a synthetic route of the L-carnitine in eukaryotes.

Methods for predicting drug-target binding using synthetically enhanced data involve generating a plurality of phantom ligands for a plurality of proteins in a protein structure database; generating a plurality of drug-target interaction (DTI) signatures for proteins and ligands in a (DTI) database using the plurality of phantom ligands; using the DTI features to generate a machine learning model; and predicting the likelihood of interaction of the combination of query proteins and query ligands using the machine learning model.

A method of generating a microbial protein database is disclosed. The method includes: (a) aligning genomic sequences of metagenomic data to reference genomes to obtain a database of microbial species that are potentially present in the at least one microbiome sample, and their relative abundance; (b) refining the database; (c) obtaining protein sequences of said microbes of the refined database and align to the metagenomic data; and (d) selecting those proteins having a % sequence alignment coverage greater than a second predetermined % to obtain a microbial protein database.

The present application relates to the technical field of bioinformatics, and particularly relates to a construction method of a dehalogenating microorganism functional genome database and an analysis method of dehalogenating microorganisms. The construction method comprises the following steps: obtaining dehalogenation-related genes; obtaining seed sequences and homologous sequences of a family to which the dehalogenation-related genes belong based on a Uniprot protein database to obtain a core database; comparing the core database with a homologous protein database, merging sequences of dehalogenation functional gene families obtained through comparison with the core database to obtain a preliminary database; and comparing the preliminary database to a Refseq database of microorganisms in NCBI, and obtaining a complete database through classification, merging of homologous sequences and de-redundancy. The present application constructs an accurate dehalogenation functional gene database based on analysis and search results of multiple databases, which can be used to conveniently and accurately analyze dehalogenating microorganism functional communities in the environment, and has important application value.

The application discloses a sea cucumber-derived active peptide with immunocompetence and a preparation method and application thereof, and belongs to the technical field of biology. An amino acid sequence of the sea cucumber-derived active peptide is shown as SEQ ID NO. 1. The amino acid sequence of the sea cucumber-derived active peptide is obtained by means of a sea cucumber protein database with the aid of separation and purification technology and mass spectrometry identification technology; the interaction between the immunocompetent peptide and a membrane recognition receptor (TLR-2) is explored through multiple rounds of screening and molecular docking technology; and the in-vitro immunocompetence of the polypeptide is verified through solid-phase synthesis and a RAW264.7 cell model. The results show that the novel immunocompetent peptide screened in the application has in-vitro immunocompetence, no biological toxicity, can activate the immune response of macrophages in the body, but will not excessively activate, so that the body is in a healthy and stable state, and the novel immunocompetent peptide has important significance for deep processing of marine protein resources.

The application discloses application of protein Fobut in regulation of pathogenicity of banana fusarium wilt fungus. The application obtains a deletion mutant and a back-supplementation mutant of Fobut by constructing a knock-out plasmid and a back-supplementation plasmid of a gene (Fobut) coding protein Fobut of banana fusarium wilt fungus and transforming corresponding protoplasts respectively. It is found through observation of the phenotype of the deletion mutant and the back-supplementation mutant and pathogenicity analysis that the knock-out gene Fobut can obviously reduce the pathogenicity of banana fusarium wilt fungus, and the pathogenicity is restored after back-supplementation, which indicates that the protein Fobut is related to the pathogenicity of banana fusarium wilt fungus and can be used as a target for prevention and treatment of banana fusarium wilt. The application enriches a protein database related to the pathogenicity of banana fusarium wilt fungus and is beneficial to prevention and treatment of banana fusarium wilt.

The invention discloses a toxoplasma gondii CTL epitope peptide identification method and application. The toxoplasma gondii CTL epitope peptide identification method comprises the following steps: constructing a toxoplasma gondii infection model of an HLA-A * 0201 transgenic mouse; capturing a natural peptide fragment combined with the HLA-A * 0201 molecule from the spleen lymphocyte surface of the infected model mouse through a weak acid elution method; analyzing the captured peptide fragment by adopting a liquid chromatography-tandem mass spectrometry technology, and screening a toxoplasma gondii-sourced specific peptide fragment based on a toxoplasma gondii protein database; through an in-vitro protein renaturation experiment, the binding capacity of the screened candidate peptide fragment and the HLA-A * 0201 molecule is verified; a TAP defect type T2 lymphoblastic cell model is adopted, and the binding stability of the candidate peptide fragment and the HLA-A * 0201 molecule is verified through a fluorescence index. According to the method, through a progressive screening strategy that after natural peptide group screening, a NetMHCpan-4.1 algorithm is adopted for filtering and eliminating host homology, in-vitro pMHC-I renaturation verification and T2 cell binding stability detection are carried out, the clinical transformation value of epitope screening is remarkably improved, and high-confidence candidate molecules are provided for toxoplasma gondii multi-epitope vaccine design.

The invention discloses a method for ultra-deep detection of red blood cell protein. The method comprises the following steps: purifying red blood cells, carrying out hypotonic splitting decomposition and precipitation on a purified red blood cell sample, and extracting a cell membrane component of a precipitate and a cytoplasm component of a supernatant; carrying out proteolysis treatment on the cell membrane and cytoplasm component sample to obtain a peptide fragment sample, desalting and washing with a desalting column to obtain an eluent containing peptide fragments, and drying; carrying out trans-liquid chromatography fractionation under an alkaline condition, collecting about 100 peptide fragment fractions, and drying; detecting each fraction by mass spectrometry; and comparing mass spectrum results by using a protein database to identify peptide fragments and proteins to obtain qualitative and quantitative information of the proteins. According to the invention, all proteins in red blood cells are comprehensively and systematically detected to detect more proteins compared with the prior art, the detection sequence coverage of each protein is greatly improved, and more protein and amino acid change information is provided for physiological functions and disease mechanisms of the red blood cells; and more molecular clues are provided for fundamental research and clinical diagnosis and treatment of red blood cells.