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Updated: Jun 3, 2025
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An Integrated Approach for Microprotein Identification and Sequence Analysis
Published on: July 12, 2022
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Effective Gene Expression Prediction and Optimization from Protein Sequences
Tuoyu Liu1,2,3, Yiyang Zhang2, Yanjun Li2
1State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|January 9, 2025
Summary
This study reveals a link between protein sequence and expression, developing SRAB and AEI metrics. A novel AI model accurately predicts protein expression across 88 species, enabling optimized gene expression and mutant design.
Area of Science:
- Biotechnology
- Protein Engineering
- Computational Biology
Background:
- High soluble protein expression in heterologous systems is vital for research and applications.
- The influence of protein sequence on expression levels is often underestimated compared to codon usage.
Purpose of the Study:
- To uncover the relationship between protein sequence and soluble expression in heterologous hosts.
- To develop predictive models for optimizing protein expression based on sequence characteristics.
Main Methods:
- Development of SRAB (Strength of Relative Amino Acid Bias) and AEI (Amino Acid Expression Index) to quantify sequence-expression correlation.
- Fine-tuning the MP-TRANS (MindSpore Protein Transformer) model to create 88 MPB-EXP models for predicting expression across 88 species.
- Utilizing an MPB-MUT model for generating expression-enhancing mutants.
Main Results:
- The AEI metric showed a positive correlation with soluble protein expression.
- The MPB-EXP models achieved an average prediction accuracy of 0.78, outperforming traditional machine learning methods.
- Experimental validation confirmed successful high-level soluble expression of previously unexpressed xylanase mutants in E. coli.
Conclusions:
- Protein sequence is a significant determinant of heterologous protein expression.
- The developed AI models provide a powerful tool for predicting and optimizing protein expression.
- This approach facilitates the design of proteins with enhanced expression in specific hosts.
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