Protein Solubility Prediction Based on Sequence Feature Fusion

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.

Keywords

• protein solubility|multi-feature fusion|deep learning|attention mechanism