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Quantifying the Mutational Robustness of Protein-Coding Genes

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Abstract

We use large-scale mutagenesis data and computer simulations to quantify the mutational robustness of protein-coding genes by taking into account constraints arising from protein function and the genetic code. Analyses of the distribution of amino acid substitutions from 18 mutagenesis studies revealed an average of 45% of neutral variants; while mutagenesis data of 12 proteins artificially designed under no other constraints but stability, reach an average of 60%. Simulations using a lattice protein model allow us to contrast these estimates to the expected mutational robustness of protein families by generating unbiased samples of foldable sequences, which we find to have 30% of neutral variants. In agreement with mutagenesis data of designed proteins, the model shows that maximally robust protein families might access up to twice the amount of neutral variants observed in the unbiased samples (i.e. 60%). A biophysical model of protein-ligand binding suggests that constraints associated to molecular function have only a moderate impact on robustness of approximately 5 to 10% of neutral variants; and that the direction of this effect depends on the relation between functional performance and thermodynamic stability. Although the genetic code constraints the access of a gene’s nucleotide sequence to only 30% of the full distribution of amino acid mutations, it provides an extra 15 to 20% of neutral variants to the estimations above, such that the expected, observed, and maximal robustness of protein-coding genes are approximately 50, 65, and 75%, respectively. We discuss our results in the light of three main hypothesis put forward to explain the existence of mutationally robust genes.

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Acknowledgements

The author thanks to Dr. Patricio Orio at the Centro Interdisciplinario de Neurociencias de Valparíso (CINV), for access to computational resources; and to the Chilean National Agency for Research and Development (ANID), for support through the project REDES190089.

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  1. Instituto de Sistemas Complejos de Valparaíso (ISCV), Artillería 470, Cerro Artillería, Valparaíso, Chile

    Evandro Ferrada

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Correspondence to Evandro Ferrada.

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Ferrada, E. Quantifying the Mutational Robustness of Protein-Coding Genes. J Mol Evol 89, 357–369 (2021). https://doi.org/10.1007/s00239-021-10009-1

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