Computational characterization of the sequence landscape in simple protein alphabets

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Abstract

We characterize the "sequence landscapes" in several simple, lieteropolymer models of proteins by examining their mutation properties. Using an efficient flat-histogram Monte Carlo search method, our approach involves determining the distribution in energy of all sequences of a given length when threaded through a common backbone. These calculations are performed for a number of Protein Data Bank structures using two variants of the 20-letter contact potential developed by Miyazawa and Jernigan [Miyazawa S, Jernigan WL. Macromolecules 1985;18:534], and the 2-monomer HP model of Lau and Dill [Lau KF, Dill KA. Macromolecules 1989;22:3986]. Our results indicate significant differences among the energy functions in terms of the "smoothness" of their landscapes. In particular, one of the Miyazawa-Jernigan contact potentials reveals unusual cooperative behavior among its species' interactions, resulting in what is essentially a set of phase transitions in sequence space. Our calculations suggest that model-specific features can have a profound effect on protein design algorithms, and our methods offer a number of ways by which sequence landscapes can be quantified.

Original languageEnglish (US)
Pages (from-to)232-243
Number of pages12
JournalProteins: Structure, Function and Genetics
Volume62
Issue number1
DOIs
StatePublished - Jan 1 2006

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry
  • Molecular Biology

Keywords

  • Flat-histogram
  • Landscapes
  • Monte Carlo
  • Phase transitions
  • Proteins
  • Statistical Mechanics
  • Thermodynamics

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