Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (1): 285-291.doi: 10.11896/jsjkx.201100117

• Artificial Intelligence • Previous Articles     Next Articles

Protein Solubility Prediction Based on Sequence Feature Fusion

NIU Fu-sheng, GUO Yan-bu, LI Wei-hua, LIU Wen-yang   

  1. School of Information Science and Engineering,Yunnan University,Kunming 650500,China
  • Received:2020-11-16 Revised:2021-06-29 Online:2022-01-15 Published:2022-01-18
  • About author:NIU Fu-sheng,born in 1993,postgra-duate.His main research interests include deep learning and bioinformatics.
    LI Wei-hua,born in 1977,Ph.D,asso-ciate professor.Her main research inte-rests include data mining and bioinformatics.
  • Supported by:
    National Natural Science Foundation of China(32060151),Scientific Research Fundation of the Education Department of Yunnan Province,China(2019J0006),Innovative Research Team of Yunnan Province,China(2018HC019) and Yunnan University of Postgraduate Research and Innovation Foundation Project,China(2020Z73).

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Abstract: Protein solubility plays an important role in the research of drug design.Traditional biological experiments of detecting protein solubility are time-consuming and laborious.Identifying protein solubility based on computational methods has become an important research hot spot in bioinformatics.Aiming at the problem of insufficient representation of protein features by traditio-nal solubility prediction models,this paper designs a neural network model PSPNet based on protein sequence information and applies it to protein solubility prediction.PSPNet uses amino acid residue sequence embedding information and amino acid sequence evolution information to represent protein sequences.Then convolutional neural network is used to extract the local key information of amino acid sequence embedding features.Secondly,bidirectional LSTM network is used to extract the features of remote dependencies of protein sequences.Finally,the attention mechanism is used to fuse this feature and amino acid evolution information,and the fusion feature containing multiple sequence information is used in protein solubility prediction.The experimental results show that PASNet obtains the remarkable performance of protein solubility prediction compared with the benchmark me-thods and also has a good scalability.

Key words: Attention mechanism, Deep learning, Multi-feature fusion, Protein solubility

CLC Number: 

  • TP391
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