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Related Experiment Video

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A New Screening Method for the Directed Evolution of Thermostable Bacteriolytic Enzymes
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Evaluating Protein Engineering Thermostability Prediction Tools Using an Independently Generated Dataset.

Peishan Huang1, Simon K S Chu1, Henrique N Frizzo2

  • 1Biophysics Graduate Group, University of California, Davis 95616, California, United States.

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|April 8, 2020
PubMed
Summary
This summary is machine-generated.

Protein engineering aims to improve thermal stability for industrial biocatalysts. This study expands datasets to evaluate thermal stability (T_M) and free energy change (ΔΔG) for 51 mutants, finding current computational tools lack predictive power for stability changes.

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Area of Science:

  • Protein Engineering
  • Biocatalysis
  • Computational Biology

Background:

  • Enhancing protein thermal stability is crucial for industrial biocatalysts.
  • Existing datasets and computational tools for guiding protein engineering are continuously being developed.
  • Previous work reported measures of T50 and kinetic constants for beta-glucosidase mutants.

Purpose of the Study:

  • To expand the dataset of beta-glucosidase mutants with measures of melting temperature (T_M) and change in Gibbs free energy (ΔΔG).
  • To evaluate the correlation between T50 and T_M as measures of thermal stability.
  • To assess the performance of nine computational tools in predicting protein stability and soluble protein production.

Main Methods:

  • Generated 51 beta-glucosidase mutants.
  • Measured T50, T_M, and ΔΔG for each mutant.
  • Evaluated the predictive accuracy of nine computational tools for T50, T_M, and ΔΔG.
  • Assessed the ability of computational tools to predict the production of isolatable soluble protein.

Main Results:

  • T50 and T_M showed moderate correlation (Pearson r=0.58, Spearman's rho=0.47), indicating they capture different physical properties.
  • None of the nine computational tools accurately predicted observed changes in T50, T_M, or ΔΔG.
  • Rosetta ΔΔG, FoldX, DeepDDG, PoPMuSiC, and SDM successfully predicted the production of isolatable soluble protein.

Conclusions:

  • Current computational tools are insufficient for accurately predicting modest changes in protein thermal stability.
  • Specific algorithms show promise for prescreening protein designs to ensure fold stability and soluble production.
  • Further development of predictive algorithms is needed for accurate thermal stability prediction in protein engineering.