Folding funnels: The key to robust protein structure prediction

Corey Hardin, Michael P. Eastwood, Michael Prentiss, Zaida Ann Luthey-Schulten, Peter G. Wolynes

Research output: Contribution to journalArticlepeer-review

Abstract

Natural proteins fold because their free energy landscapes are funneled to their native states. The degree to which a model energy function for protein structure prediction can avoid the multiple minima problem and reliably yield at least low-resolution predictions is also dependent on the topography of the energy landscape. We show that the degree of funneling can be quantitatively expressed in terms of a few averaged properties of the landscape. This allows us to optimize simplified energy functions for protein structure prediction even in the absence of homology information. Here we outline the optimization procedure in the context of associative memory energy functions originally introduced for tertiary structure recognition and demonstrate that even partially funneled landscapes lead to qualitatively correct, low-resolution predictions.

Original languageEnglish (US)
Pages (from-to)138-146
Number of pages9
JournalJournal of Computational Chemistry
Volume23
Issue number1
DOIs
StatePublished - Jan 15 2002

Keywords

  • Energy landscape
  • Folding funnels
  • Optimization
  • Protein folding
  • Structure prediction

ASJC Scopus subject areas

  • Chemistry(all)
  • Computational Mathematics

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