BP neural network based protein secondary structure prediction method

Abstract

The invention belongs to the field of protein secondary structure prediction methods, relates to a BP neural network training and prediction method used for protein secondary structure prediction and solves the problem of bad prediction effect of the protein secondary structure. The BP neural network training and prediction method comprises the steps of firstly selecting a group of training sample sets with [alpha]-helix, [beta]-sheet and coiling structures accounting for normal proportions from PDB, coding an amino acid sequence of a protein and regarding the coded amino acid sequence of the protein as a network input, and regarding a secondary structure of the corresponding amino acid as a network output; optimizing based on a gradient method, introducing a learning rule which is attached with a momentum item and a self-adaptive learning rate to avoid an oscillation phenomenon and prevent from being trapped in a local minimum value; adopting a six-bit input coding way and a sliding window technology in an input layer, setting a hidden layer structure based on an experience formula and the size of a sliding window; and outputting and predicting classification of the protein secondary structure by an output layer based on a DSSP algorithm.

Description

technical field [0001] The invention relates to the field of biological information, in particular to a method for predicting protein secondary structures. Background technique [0002] The high-level structure of a protein determines its biological function, and the secondary structure of a protein is determined by its amino acid sequence characteristics, which is the basis for predicting the high-level structure. The protein secondary structure mainly uses amino acid sequence information to predict the secondary structure of the protein through experimental analysis or statistical methods. When the prediction accuracy reaches more than 80%, the spatial structure of the protein can be described more accurately. With the development of DNA analysis and sequencing technology, a large amount of protein sequence information has been obtained through the derivation and analysis of DNA, but the protein structure obtained through traditional experimental and statistical methods is...

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Problems solved by technology

[0004] The present invention aims at the problem that the prediction accuracy of protein secondary structure is low and the defects of BP neural network, and the learning process of the network is improved, and provides a BP neural network training and prediction method for protein secondary structure prediction

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