Increasing protein stability by inferring substitution effects from high-throughput experiments

2022/11/01 by Rasmus K. Norrild, Kristoffer E. Johansson, Charlotte O’Shea +3 · 1 voice · 3 citations
Biochemistry, Genetics and Molecular Biology · Environmental Science · #RNA and protein synthesis mechanisms #CRISPR and Genetic Engineering #Bacteriophages and microbial interactions

paper · doi:10.1016/j.crmeth.2022.100333

Abstract

We apply a computational model, global multi-mutant analysis (GMMA), to inform on effects of most amino acid substitutions from a randomly mutated gene library. Using a high mutation frequency, the method can determine mutations that increase the stability of even very stable proteins for which conventional selection systems have reached their limit. As a demonstration of this, we screened a mutant library of a highly stable and computationally redesigned model protein using an in vivo genetic sensor for folding and assigned a stability effect to 374 of 912 possible single amino acid substitutions. Combining the top 9 substitutions increased the unfolding energy 47 to 69 kJ/mol in a single engineering step. Crystal structures of stabilized variants showed small perturbations in helices 1 and 2, which rendered them closer in structure to the redesign template. This case study illustrates the capability of the method, which is applicable to any screen for protein function.

Citations

• <i>Phaser</i>crystallographic software

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Discussions

• AI design of a protein with thermal stability of 150°C doi.org/10.1016/j.cr... (AI: Actually Interpretable Assay-Informed Additivity Inference) [bsky, 26 points, 2 comments]

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