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An Integrated Approach for Microprotein Identification and Sequence Analysis
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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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
amino acid expression indexmutant generationpredicting protein expressionsoluble expressiontransfer learning

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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.