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Global Analysis of Multi-Mutants to Improve Protein Function.

Kristoffer E Johansson1, Kresten Lindorff-Larsen1, Jakob R Winther1

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A new Global Multi-Mutant Analysis (GMMA) method identifies beneficial amino acid substitutions for protein engineering. This approach leverages multiply-substituted variants to enhance protein stability and function, as demonstrated with green fluorescent protein (GFP).

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Area of Science:

  • Protein Engineering
  • Computational Biology
  • Biophysics

Background:

  • Identifying beneficial amino acid substitutions for protein stability and function is crucial in protein engineering.
  • High-throughput experiments generate large datasets of protein variants, offering opportunities for advanced analysis.

Purpose of the Study:

  • To introduce and validate the Global Multi-Mutant Analysis (GMMA) method for identifying beneficial amino acid substitutions.
  • To apply GMMA to a large dataset of green fluorescent protein (GFP) variants to assess its efficacy.

Main Methods:

  • Global Multi-Mutant Analysis (GMMA) was developed to analyze multiply-substituted variants.
  • GMMA was applied to a dataset of over 54,000 GFP variants with known fluorescence output.
  • Experimental validation was performed on top-ranking identified substitutions.

Main Results:

  • The GMMA method provided a good fit to the experimental data and is analytically transparent.
  • Six top-ranking amino acid substitutions were shown to progressively enhance GFP fluorescence.
  • GMMA successfully recovered previously reported beneficial substitutions for GFP folding and function.

Conclusions:

  • Large libraries of multiply-substituted variants are a valuable resource for protein engineering.
  • GMMA offers a powerful and transparent approach to extract actionable insights from such libraries.
  • The method has broad applicability for optimizing protein stability and function.