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Updated: Aug 19, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Increasing protein stability by inferring substitution effects from high-throughput experiments.

Rasmus Krogh Norrild1,2, Kristoffer Enøe Johansson1, Charlotte O'Shea1

  • 1Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, 2200 Copenhagen N, Denmark.

Cell Reports Methods
|December 1, 2022
PubMed
Summary
This summary is machine-generated.

Global multi-mutant analysis (GMMA) identifies beneficial mutations for protein stabilization, even for highly stable proteins. This computational method significantly enhances protein stability through targeted amino acid substitutions.

Keywords:
GMMAdeep mutational scanningfunctional screeningprotein designprotein stability

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Area of Science:

  • Protein engineering
  • Computational biology
  • Biophysics

Background:

  • Conventional protein engineering methods face limitations when improving the stability of already highly stable proteins.
  • Computational redesign offers potential for enhancing protein stability but requires experimental validation.

Purpose of the Study:

  • To apply and demonstrate the efficacy of global multi-mutant analysis (GMMA) for identifying stabilizing amino acid substitutions in a computationally redesigned protein.
  • To assess the limits of conventional selection systems and showcase GMMA's capability for highly stable proteins.

Main Methods:

  • Utilized a computational model, global multi-mutant analysis (GMMA), on a randomly mutated gene library with high mutation frequency.
  • Employed an in vivo genetic sensor for protein folding to screen a mutant library of a highly stable, computationally redesigned model protein.
  • Determined the stability effect of 374 out of 912 possible single amino acid substitutions.

Main Results:

  • GMMA successfully assigned stability effects to a large number of single amino acid substitutions.
  • Combining the top 9 identified substitutions increased the protein's unfolding energy by 47 to 69 kJ/mol in one step.
  • Structural analysis of stabilized variants revealed minor perturbations in specific helical regions, aligning them more closely with the redesign template.

Conclusions:

  • GMMA is a powerful computational tool for protein stabilization, overcoming limitations of traditional methods.
  • The method is broadly applicable to any protein engineering screen requiring functional or stability assessments.
  • This study demonstrates a significant single-step improvement in protein stability using GMMA-guided engineering.