Macro-proteome mining method and application of macro-proteome mining method in obtaining hydrolysis characteristics of intestinal microbial proteins

A technology of proteolysis and proteome, which is applied in the metaproteome mining method and its application in obtaining the proteolysis characteristics of intestinal microorganisms, can solve the problems that the changes of proteolysis characteristics of intestinal microorganisms have not been studied, and reduce false positives. Effect of positive identification, lower confidence, good accuracy

Pending Publication Date: 2021-05-11
THE FIFTH AFFILIATED HOSPITAL SUN YAT SEN UNIV +1
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  • Abstract
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  • Claims
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Problems solved by technology

[0004] However, in complex disease states such as IBD, the characteristic changes of intestinal microbial proteolysis have not been studied, so there is an urgent need for a method that can grasp the characteristics of intestinal microbial proteolysis in complex disease states

Method used

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  • Macro-proteome mining method and application of macro-proteome mining method in obtaining hydrolysis characteristics of intestinal microbial proteins
  • Macro-proteome mining method and application of macro-proteome mining method in obtaining hydrolysis characteristics of intestinal microbial proteins

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0033] Embodiment 1 Different software executes search performance

[0034] Using the MLI dataset and a large metaprotein database, we compared different commercial software (ProteomeDiscoverer, PEAK, ProteinPilot, and Byonic) and open source software (MaxQuant, MSFragger, and pFind) on several 36-core servers (installed with 192G memory) Properties of hemitryptic peptides. Proteome Discoverer, Byonic, MaxQuant, pFind, and ProteinPilot did not complete searches in a month, while MSFragger crashed with an out-of-memory error. Only PEAK completed the analysis within one month, so further high-throughput analysis was performed using a 156-core HPC cluster, which completed the database search within 2 weeks.

Embodiment 2

[0035] Example 2 Database Search

[0036] The database search process generally includes two main steps: (1) de-novo sequencing, and use a large macroprotein database (large database) and PEAKS software to perform the first search to obtain at least one peptide identified Protein and generate a corresponding small protein database (reduced database); (2) Use the reduced database and various software to perform a second search to improve the accuracy of identifying the semitrypsin polypeptide.

[0037] In order to cope with the increased search space and time in the identification of metaproteomic semitryptic peptides, the search was first performed using PEAKS DB on a cluster configured with an Intel(R) Xeon(R) 156-core processor and 1.5TB 2666MHz memory, the software De novo sequencing was first performed, followed by a database search using the following parameters: mass bias of 10 ppm for precursor ions and 0.02 Da for fragment ions; aminomethylation of cysteine ​​was set a...

Embodiment 3

[0042] Example 3 Semitrypsin Polypeptide Identification and Classification and Functional Analysis

[0043] 1. Identification principle of semitrypsin polypeptide

[0044] Peptides whose first amino acid is not R or K before the identified sequence are semi-tryptic N-terminal peptides (excluding the N-terminal of the protein). The lack of R or K in the last amino acid of the identified sequence is a half-tryptic C-terminal peptide (excluding the C-terminal of the protein). In-source CID fragments were distinguished from proteolytically derived hemitryptic polypeptides based on elution time. Most in-source fragments showed different retention times compared to their theoretical retention times (predicted using SSRCalc). The microbial hemitrypsin polypeptides were distinguished from peptides of human origin and peptides of food origin by the corresponding accession numbers in the FASTA sequence entry.

[0045] 2. Combining semi-trypsin and complete trypsin data to quantify th...

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Abstract

The invention belongs to the technical field of biology, and discloses a semi-trypsin polypeptide-centered metaproteome data mining method, which comprises the steps of two-step library searching, de novo sequencing, open retrieval and matching of various library searching software, and large-scale semi-trypsin polypeptide-centered metaproteome information mining is carried out aiming at high-resolution mass spectrum data. These strategies can reduce the false positive rate resulting from database incompleteness and post-translational modification. When the method is used for analyzing the escherichia coli proteome, 93.4% of peptide fragments identified from a huge macro protein database are consistent with peptide fragments identified from a traditional escherichia coli reference database.

Description

technical field [0001] The present invention relates to the technical field of biological information analysis, and more specifically, relates to a metaproteome mining method and its application in obtaining proteolysis characteristics of intestinal microorganisms. Background technique [0002] Gut microbes live in a dynamic environment, facing proteotoxic and metabolic stress from drugs, diet, microbial competition, and endogenous chemical components of the host. Bacteria have evolved different regulatory strategies to adapt to changing environments, including changes in gene expression, cell differentiation, and motility. Among these regulatory strategies, proteolysis plays a crucial role. Proteolytic regulation is the key factor affecting all Biologically important processes, bacteria use energy-dependent proteases to degrade misfolded proteins or activate regulatory proteins to respond rapidly to the dynamic intestinal environment. Microorganisms regulate a wide range o...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16B20/00G16B50/00
CPCG16B20/00G16B50/00
Inventor 严志祥单鸿贺飞翔张婷薛可文
Owner THE FIFTH AFFILIATED HOSPITAL SUN YAT SEN UNIV
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