Selection pressures on evolution of ribonuclease H explored with rigorous free–energy–based design

Author:

Hayes Ryan L.12ORCID,Nixon Charlotte F.3ORCID,Marqusee Susan345ORCID,Brooks Charles L.26ORCID

Affiliation:

1. Department of Chemical and Biomolecular Engineering, University of California, Irvine, CA 92697

2. Department of Chemistry, University of Michigan, Ann Arbor, MI 48109

3. Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720

4. California Institute for Quantitative Biosciences, University of California, Berkeley, CA 94720

5. Department of Chemistry, University of California, Berkeley, CA 94720

6. Biophysics Program, University of Michigan, Ann Arbor, MI 48109

Abstract

Understanding natural protein evolution and designing novel proteins are motivating interest in development of high-throughput methods to explore large sequence spaces. In this work, we demonstrate the application of multisite λ dynamics (MSλD), a rigorous free energy simulation method, and chemical denaturation experiments to quantify evolutionary selection pressure from sequence–stability relationships and to address questions of design. This study examines a mesophilic phylogenetic clade of ribonuclease H (RNase H), furthering its extensive characterization in earlier studies, focusing on E. coli RNase H (ecRNH) and a more stable consensus sequence (AncCcons) differing at 15 positions. The stabilities of 32,768 chimeras between these two sequences were computed using the MSλD framework. The most stable and least stable chimeras were predicted and tested along with several other sequences, revealing a designed chimera with approximately the same stability increase as AncCcons, but requiring only half the mutations. Comparing the computed stabilities with experiment for 12 sequences reveals a Pearson correlation of 0.86 and root mean squared error of 1.18 kcal/mol, an unprecedented level of accuracy well beyond less rigorous computational design methods. We then quantified selection pressure using a simple evolutionary model in which sequences are selected according to the Boltzmann factor of their stability. Selection temperatures from 110 to 168 K are estimated in three ways by comparing experimental and computational results to evolutionary models. These estimates indicate selection pressure is high, which has implications for evolutionary dynamics and for the accuracy required for design, and suggests accurate high-throughput computational methods like MSλD may enable more effective protein design.

Funder

HHS | National Institutes of Health

National Science Foundation

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

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