Large-scale in silico mutagenesis experiments reveal optimization of genetic code and codon usage for protein mutational
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RESEARCH ARTICLE
Open Access
Large-scale in silico mutagenesis experiments reveal optimization of genetic code and codon usage for protein mutational robustness Martin Schwersensky1† , Marianne Rooman1,2*† and Fabrizio Pucci1,2*†
Abstract Background: How, and the extent to which, evolution acts on DNA and protein sequences to ensure mutational robustness and evolvability is a long-standing open question in the field of molecular evolution. We addressed this issue through the first structurome-scale computational investigation, in which we estimated the change in folding free energy upon all possible single-site mutations introduced in more than 20,000 protein structures, as well as through available experimental stability and fitness data. Results: At the amino acid level, we found the protein surface to be more robust against random mutations than the core, this difference being stronger for small proteins. The destabilizing and neutral mutations are more numerous in the core and on the surface, respectively, whereas the stabilizing mutations are about 4% in both regions. At the genetic code level, we observed smallest destabilization for mutations that are due to substitutions of base III in the codon, followed by base I, bases I+III, base II, and other multiple base substitutions. This ranking highly anticorrelates with the codon-anticodon mispairing frequency in the translation process. This suggests that the standard genetic code is optimized to limit the impact of random mutations, but even more so to limit translation errors. At the codon level, both the codon usage and the usage bias appear to optimize mutational robustness and translation accuracy, especially for surface residues. Conclusion: Our results highlight the non-universality of mutational robustness and its multiscale dependence on protein features, the structure of the genetic code, and the codon usage. Our analyses and approach are strongly supported by available experimental mutagenesis data. Keywords: Protein evolution, Stability prediction, Mutational robustness, Translation accuracy, Genetic code, Codon usage, Codon usage bias
*Correspondence: [email protected]; [email protected] † Martin Schwersensky, Marianne Rooman and Fabrizio Pucci contributed equally to this work. 1 Computational Biology and Bioinformatics, Université Libre de Bruxelles, CP 165/61, Roosevelt Ave. 50, 1050 Brussels, Belgium 2 Interuniversity Institute of Bioinformatics in Brussels, Boulevard du Triomphe, 1050 Brussels, Belgium © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the ma
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