Effective Gene Expression Prediction and Optimization from Protein Sequences
Tuoyu Liu, Yiyang Zhang, Yanjun Li, Guoshun Xu, Han Gao, Pengtao Wang, Tao Tu, Huiying Luo, Ningfeng Wu, Bin Yao, Bo Liu, Feifei Guan, Huoqing Huang, Jian Tian
Abstract
High soluble protein expression in heterologous hosts is crucial for various research and applications. Despite considerable research on the impact of codon usage on expression levels, the relationship between protein sequence and expression is often overlooked. In this study, a novel connection between protein expression and sequence is uncovered, leading to the development of SRAB (Strength of Relative Amino Acid Bias) based on AEI (Amino Acid Expression Index). The AEI served as an objective measure of this correlation, with higher AEI values enhancing soluble expression. Subsequently, the pre-trained protein model MP-TRANS (MindSpore Protein Transformer) is developed and fine-tuned using transfer learning techniques to create 88 prediction models (MPB-EXP) for predicting heterologous expression levels across 88 species. This approach achieved an average accuracy of 0.78, surpassing conventional machine learning methods. Additionally, a mutant generation model, MPB-MUT, is devised and utilized to enhance expression levels in specific hosts. Experimental validation demonstrated that the top 3 mutants of xylanase (previously not expressed in Escherichia coli) successfully achieved high-level soluble expression in E. coli. These findings highlight the efficacy of the developed model in predicting and optimizing gene expression based on protein sequences.