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Method for predicting space epitope of protein antigen according to antibody species classification

A technology for spatial epitope and classification prediction, which is applied in the fields of electrical digital data processing, special data processing applications, instruments, etc., and can solve problems involving less antibodies.

Inactive Publication Date: 2012-06-27
TONGJI UNIV
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AI Technical Summary

Problems solved by technology

The existing spatial epitope prediction models mostly start from the perspective of the antigen, and rarely involve the antibody to be bound

Method used

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  • Method for predicting space epitope of protein antigen according to antibody species classification
  • Method for predicting space epitope of protein antigen according to antibody species classification
  • Method for predicting space epitope of protein antigen according to antibody species classification

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Embodiment Construction

[0041] In order to understand the technical content of the present invention more clearly, the following combination figure 1 The present invention is described in detail. It should be understood that the examples are only used to illustrate the present invention, not to limit the present invention.

[0042] 1. Data Collection

[0043] The sources of antibody species in the PDB database include: MUSMUSCULUS, HOMO SAPIENS, RATTUSRATTUS, CRICETULUSMIGRATORIUS, etc. Here, the human source (HOMO SAPIENS) and the mouse source (MUS MUSCULUS) with the largest amount of data are taken as examples.

[0044] Use antibody, antigen, immu*, etc. as keywords to search the PDB database to collect antigen-antibody complex data. Remove less accurate structures (e.g., the accuracy threshold is set to ). Use Naccess V2.1.1 software to calculate the solvent-accessible area of ​​protein antigen amino acids and determine the spatial epitope. Measure the similarity between spatial epitopes of...

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Abstract

The invention relates to a method for predicting space epitope of protein antigen according to antibody species classification. The method comprises the steps of 1) collecting data: classifying and collecting antigen-antibody structure data according to the antibody species source information (such as mouse source, human source and the like) to obtain classified data sets, and collecting descriptive features of protein interactive binding sites; 2) building model: computing the descriptive features for the collected classified data sets, and building antigen space epitope prediction model for antigens which bind antibodies of different species sources according to the interspecies difference; and 3) predicting epitope: according to the species source of antibody to be bound, selecting the antigen space epitope prediction model to predict the potential space epitope for antigen with unknown epitope. The method of the invention predicts the space epitope of protein antigen according to antibody species classification, comprehensively utilizes the amino acid physicochemical feature and three-dimensional space local structure feature in the protein, has ingenious design, and notably improves prediction accuracy. Thus, the method is suitable for being applied on a large scale.

Description

technical field [0001] The invention relates to the technical field of antigen space epitopes, in particular to a method for predicting protein antigen space epitopes according to antibody species classification. The method selects a model according to the combined antibody species source information, and comprehensively uses various amino acid physicochemical properties and three-dimensional structure space information to predict protein antigen space epitopes. Background technique [0002] According to relevant statistics, with the rapid development of the global vaccine industry, the current international vaccine market has exceeded 20 billion US dollars. It is estimated that in the next five years, the global vaccine market will grow at an annual growth rate of 14%, while the average annual growth rate of my country's vaccine market will also exceed 15%. According to a report recently published by the market research firm Datamonitor, it is predicted that by 2010, only ...

Claims

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Application Information

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IPC IPC(8): G06F19/18
Inventor 曹志伟孙静
Owner TONGJI UNIV
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