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EPSOL: sequence-based protein solubility prediction using multidimensional embedding.

Xiang Wu1, Liang Yu1

  • 1School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China.

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|June 19, 2021
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Summary
This summary is machine-generated.

A new deep learning tool, EPSOL, accurately predicts protein solubility in E. coli expression systems. This predictor enhances protein production yield and reduces costs by forecasting solubility before experiments.

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Protein Engineering

Background:

  • Recombinant protein production in E. coli is crucial for biotechnology.
  • Protein solubility significantly impacts yield and production costs.
  • Accurate prediction of protein solubility is needed to optimize recombinant protein expression.

Purpose of the Study:

  • To develop a novel, highly accurate deep learning model for predicting protein solubility in E. coli.
  • To create a tool that forecasts protein solubility before experimental work, minimizing costs and improving yield.

Main Methods:

  • Developed EPSOL, a deep learning architecture for protein solubility prediction.
  • Utilized multidimensional embedding for comprehensive protein feature representation.
  • Evaluated EPSOL against existing sequence-based solubility predictors.

Main Results:

  • EPSOL achieved 0.79 accuracy and 0.58 Matthew's correlation coefficient.
  • Outperformed all existing sequence-based solubility predictors.
  • Demonstrated reliable prediction of solubility for new recombinant proteins.

Conclusions:

  • EPSOL offers a reliable method for predicting protein solubility in E. coli.
  • Enables large-scale screening of sequence variants for improved manufacturability.
  • Facilitates optimization of recombinant protein production, reducing costs and increasing yield.