Prediction of protein solubility based on sequence physicochemical patterns and distributed representation information with DeepSoluE

• Published in: BMC biology Vol. 21; no. 1; pp. 12 - 11
• Main Authors: Wang, Chao, Zou, Quan
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 24.01.2023; BioMed Central; BMC
• Subjects: Amino Acid Sequence; Amino acids; Analysis; Bioinformatics; Chemical properties; Computational Biology - methods; Datasets; E coli; Escherichia coli - genetics; Escherichia coli - metabolism; Feature embedding; Feature selection; Gene expression; Genetic algorithms; Inclusion bodies; Industrial applications; Interpretation; Long short-term memory; Machine learning; Methodology; Optimization; Performance evaluation; Prediction models; Protein expression; Protein Processing, Post-Translational; Protein solubility; Proteins; Proteins - metabolism; Representations; Solubility; Support vector machines; China; Feature embedding; Interpretation; Protein solubility; Machine learning
• ISSN: 1741-7007; 1741-7007
• DOI: 10.1186/s12915-023-01510-8

Protein solubility is a precondition for efficient heterologous protein expression at the basis of most industrial applications and for functional interpretation in basic research. However, recurrent formation of inclusion bodies is still an inevitable roadblock in protein science and industry, where only nearly a quarter of proteins can be successfully expressed in soluble form. Despite numerous solubility prediction models having been developed over time, their performance remains unsatisfactory in the context of the current strong increase in available protein sequences. Hence, it is imperative to develop novel and highly accurate predictors that enable the prioritization of highly soluble proteins to reduce the cost of actual experimental work. In this study, we developed a novel tool, DeepSoluE, which predicts protein solubility using a long-short-term memory (LSTM) network with hybrid features composed of physicochemical patterns and distributed representation of amino acids. Comparison results showed that the proposed model achieved more accurate and balanced performance than existing tools. Furthermore, we explored specific features that have a dominant impact on the model performance as well as their interaction effects. DeepSoluE is suitable for the prediction of protein solubility in E. coli; it serves as a bioinformatics tool for prescreening of potentially soluble targets to reduce the cost of wet-experimental studies. The publicly available webserver is freely accessible at http://lab.malab.cn/~wangchao/softs/DeepSoluE/ .