Constructing sequence-dependent protein models using coevolutionary information

Ryan R. Cheng, Mohit Raghunathan, Jeffrey K. Noel, José N. Onuchic

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Recent developments in global statistical methodologies have advanced the analysis of large collections of protein sequences for coevolutionary information. Coevolution between amino acids in a protein arises from compensatory mutations that are needed to maintain the stability or function of a protein over the course of evolution. This gives rise to quantifiable correlations between amino acid sites within the multiple sequence alignment of a protein family. Here, we use the maximum entropy-based approach called mean field Direct Coupling Analysis (mfDCA) to infer a Potts model Hamiltonian governing the correlated mutations in a protein family. We use the inferred pairwise statistical couplings to generate the sequence-dependent heterogeneous interaction energies of a structure-based model (SBM) where only native contacts are considered. Considering the ribosomal S6 protein and its circular permutants as well as the SH3 protein, we demonstrate that these models quantitatively agree with experimental data on folding mechanisms. This work serves as a new framework for generating coevolutionary data-enriched models that can potentially be used to engineer key functional motions and novel interactions in protein systems.

Original languageEnglish
Pages (from-to)111-122
Number of pages12
JournalProtein Science
Volume25
Issue number1
DOIs
StatePublished - Jan 1 2016

Bibliographical note

Publisher Copyright:
© 2015 The Protein Society.

Keywords

  • coarse-grained protein models
  • coevolutionary information
  • computational biophysics
  • statistical inference

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology

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