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Protein secondary structure prediction method based on deep neural networks

A deep neural network and secondary structure technology, applied in the field of protein secondary structure prediction based on deep neural network, can solve the problems of low accuracy, unable to meet daily application requirements, and few methods for predicting 8 states of proteins, etc. The effect of improving feature extraction capabilities

Active Publication Date: 2019-05-28
LUDONG UNIVERSITY
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Problems solved by technology

[0005] The technical problem solved by the present invention is: there are relatively few existing methods for predicting the eight states of proteins, and the accuracy of prediction is low, which cannot meet the needs of daily applications

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  • Protein secondary structure prediction method based on deep neural networks
  • Protein secondary structure prediction method based on deep neural networks
  • Protein secondary structure prediction method based on deep neural networks

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Embodiment Construction

[0019] The technical solution of the present invention will be further described below in conjunction with specific embodiments.

[0020] Such as figure 1 As shown, the specific implementation of the present invention is to provide a protein secondary structure prediction method based on deep neural network, including the following steps:

[0021] Step 100: Extract protein sequence features. Among the input features of the network, the number of features of amino acid sequence information is 21, and the number of features of amino acid structure information is also 21. Each amino acid has 42 features that are used to predict its corresponding secondary level structure. Each protein sequence contains 700 amino acids at most, so each protein can be represented by a 700×42 matrix. If there are less than 700 amino acids in a protein, 0 is used to fill in the features of the following amino acid sequence. A protein sequence can be expressed as:

[0022]

[0023] Where L is the length of...

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Abstract

The invention relates to a protein secondary structure prediction method based on deep neural networks. According to the method, the mutual dependence characteristics of protein sequences can be fusedthrough a plurality of different levels of convolutional neural networks. Meanwhile, single amino acid characteristics are extracted at the same time. The characteristics are input into a recurrent neural network to be further fused, and mapping relations with eight categories of protein are established through a full connection layer. Finally, an RMSProp optimizer is used for training the deep neural network based on the cross entropy error between labels and logits, so that the prediction accuracy of the protein secondary structure is effectively improved.

Description

Technical field [0001] The invention relates to a protein secondary structure prediction method based on a deep neural network, which includes technologies such as convolutional neural network, cyclic neural network and protein structure prediction. Background technique [0002] Proteins have four levels of structure. The secondary structure refers to the first level of folding of the protein, which is a general structure formed by folding part of the protein chain. The structure of protein chain depends entirely on its amino acid sequence, but so far we have not fully understood the folding rules of protein sequence. The structure of a protein plays an important role in the analysis of its function and pharmacology, so how to predict the structure of a protein based on the amino acid sequence is one of the challenges facing bioinformatics. Precise protein structure and function prediction is partly based on the accuracy of protein secondary structure prediction. [0003] Protein...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16B15/20G06N3/04G06N3/08
CPCY02A90/10
Inventor 周树森邹海林柳婵娟臧睦君刘通
Owner LUDONG UNIVERSITY
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