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

• 成果类型: Article
• 署名作者: Hayes, Ryan L.; Nixon, Charlotte F.; Marqusee, Susan; Brooks III, Charles L.
• 署名单位: University of California System; University of California Irvine; University of Michigan System; University of Michigan; University of California System; University of California Berkeley; University of California System; University of California Berkeley; University of California System; University of California Berkeley; University of Michigan System; University of Michigan
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-8671
• DOI: 10.1073/pnas.2312029121
• 发表日期: 2024-01-16
• 关键词: particle mesh ewald; direct-coupling analysis; novo protein design; binding free-energy; molecular-dynamics; reliable prediction; sequence statistics; STABILITY; accurate; mutations

摘要

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 lambda dynamics (MS lambda D), 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 MS lambda D 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 MS lambda D may enable more effective protein design.