Subspace fusion-based protein-vitamin binding location point predicting method

Abstract

The invention provides a subspace fusion-based protein-vitamin binding location point predicting method. The method comprises the steps of feature extracting and feature combining: extracting evolution information, secondary-level structural information the binding tendency information of a protein by respectively using a PSI-BLAST, a PSIPRED and a protein-vitamin binding location point tendency table, and converting amino acid residues in a protein sequence into a vector presentation mode by a sliding window and serial combination; using a multi-feature selection algorithm to perform feature selection on an original feature space for multiple times; forming a feature subspace by feature subsets obtained from feature selection every time and establishing multiple feature subspaces; training one SVM (support vector machine) classifier for each obtained feature subspace; fusing multiple SVM classifiers which are trained by a weighted average classifier fusing mode; performing protein-vitamin binding location prediction on the protein to be predicted based on a fused SVM predictor. The prediction method is fast in prediction sped and high in prediction precision.

Description

technical field [0001] The invention relates to the field of bioinformatics protein-vitamin interaction, in particular to a method for predicting protein-vitamin binding sites based on subspace fusion. Background technique [0002] The interaction between proteins and vitamins plays a vital role in metabolism and is ubiquitous and indispensable in life activities. Determining the binding sites between proteins and vitamins through biological experiments takes a lot of time and money, and is inefficient. With the rapid development of sequencing technology and the continuous advancement of human structural genome, proteomics has accumulated a large number of protein sequences that have not been labeled with vitamin binding sites. Therefore, there is an urgent need to develop a fast and accurate intelligent prediction method for protein-vitamin binding sites directly from the protein sequence by applying the relevant knowledge of bioinformatics, and it is of great significance...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the above-mentioned shortcomings of mutually exclusive features in a single multi-dimensional feature space, which lead to a large gap between pr

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