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The Influence of Protein Stability on Sequence Evolution: Applications to Phylogenetic Inference

  • Ugo Bastolla
  • Miguel Arenas
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1851)

Abstract

Phylogenetic inference from protein data is traditionally based on empirical substitution models of evolution that assume that protein sites evolve independently of each other and under the same substitution process. However, it is well known that the structural properties of a protein site in the native state affect its evolution, in particular the sequence entropy and the substitution rate. Starting from the seminal proposal by Halpern and Bruno, where structural properties are incorporated in the evolutionary model through site-specific amino acid frequencies, several models have been developed to tackle the influence of protein structure on sequence evolution. Here we describe stability-constrained substitution (SCS) models that explicitly consider the stability of the native state against both unfolded and misfolded states. One of them, the mean-field model, provides an independent sites approximation that can be readily incorporated in maximum likelihood methods of phylogenetic inference, including ancestral sequence reconstruction. Next, we describe its validation with simulated and real proteins and its limitations and advantages with respect to empirical models that lack site specificity. We finally provide guidelines and recommendations to analyze protein data accounting for stability constraints, including computer simulations and inferences of protein evolution based on maximum likelihood. Some practical examples are included to illustrate these procedures.

Key words

Stability-constrained substitution models Mean-field substitution model Protein folding stability Protein evolution Ancestral protein reconstruction 

Notes

Acknowledgments

M.A. was supported by the grant “Ramón y Cajal” RYC-2015-18241 from the Spanish Government. U.B. is supported by the grant BIO2016-79043 from the Spanish Ministry of Economy.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Centre for Molecular Biology Severo Ochoa(CSIC-UAM)MadridSpain
  2. 2.Department of Biochemistry, Genetics and ImmunologyUniversity of VigoVigoSpain

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