Prediction method and system of protein local structure characteristics

Abstract

The invention relates to the field of bioinformatics, and discloses a prediction method and system of protein local structure characteristics, which uses the depth learning technology to improve the prediction accuracy, provides key reference information for the prediction of the tertiary structure of a protein, and solves the problem of high cost and low efficiency caused by determining the tertiary structure of the protein by a biological experimental method. The method uniformly constructs characteristic sequences of various protein sequences in a sample set as the input of a training model; the training model adopts a deep neural network model based on stack sparse self-coding with a number of hidden layers of 3, a dropout method is applied to the hidden layers of the whole network, and some neurons in the hidden layers are not allowed to work to reduce the over-fitting of the model; and weight parameters of the training model are optimized through a training set, so that the valueof a constructed loss function is minimized, therefore, the solvent accessibility of each residue or residue contact number prediction in the protein sequences can be carried out correspondingly according to the trained network model.

Description

technical field [0001] The invention relates to the field of bioinformatics, in particular to a method and system for predicting local structural features of proteins. Background technique [0002] Protein is the material basis of all life activities and participates in major physiological activities in the body. Enzymes, hormones, antibodies and other active substances in the human body are composed of proteins. Therefore, understanding the function of proteins is of great significance to understanding the mechanism of protein action in vivo. However, the functions of proteins are closely related to the spatial structure of protein molecules. Different proteins have different physical and chemical properties and physiological functions precisely because they have different spatial structures. Therefore, understanding the spatial structure of proteins is beneficial to the understanding of protein functions and mechanisms of action. [0003] With the rapid development of ...

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Problems solved by technology

[0007] The purpose of the present invention is to disclose a method and system for predicting local structural features of proteins, so as to improve the accuracy of prediction by utilizing the advantages of deep learning technology, and then provide key reference information for the prediction of the tertiary structure of proteins, thereby solving the problems caused by biological experiment methods. Costly and Inefficient Problems of Determining Protein Tertiary Structure

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