Format

Send to

Choose Destination
Curr Opin Struct Biol. 2017 Apr;43:55-62. doi: 10.1016/j.sbi.2016.11.004. Epub 2016 Nov 18.

Potts Hamiltonian models of protein co-variation, free energy landscapes, and evolutionary fitness.

Author information

1
Center for Biophysics and Computational Biology, Department of Chemistry, and Institute for Computational Molecular Science, Temple University, Philadelphia, PA 19122, United States. Electronic address: ronlevy@temple.edu.
2
Center for Biophysics and Computational Biology, Department of Chemistry, and Institute for Computational Molecular Science, Temple University, Philadelphia, PA 19122, United States.
3
Center for Biophysics and Computational Biology, Department of Chemistry, and Institute for Computational Molecular Science, Temple University, Philadelphia, PA 19122, United States; Department of Physics and Astronomy, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, United States.

Abstract

Potts Hamiltonian models of protein sequence co-variation are statistical models constructed from the pair correlations observed in a multiple sequence alignment (MSA) of a protein family. These models are powerful because they capture higher order correlations induced by mutations evolving under constraints and help quantify the connections between protein sequence, structure, and function maintained through evolution. We review recent work with Potts models to predict protein structure and sequence-dependent conformational free energy landscapes, to survey protein fitness landscapes and to explore the effects of epistasis on fitness. We also comment on the numerical methods used to infer these models for each application.

PMID:
27870991
PMCID:
PMC5869684
DOI:
10.1016/j.sbi.2016.11.004
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center