Associative memory Hamiltonians for structure prediction without homology: α/β proteins

Corey Hardin, Michael P. Eastwood, Michael C. Prentiss, Zadia Luthey-Schulten, Peter G. Wolynes

Research output: Contribution to journalArticlepeer-review

Abstract

We describe a method for predicting the structure of α/β class proteins in the absence of information from homologous structures. The method is based on an associative memory model for short to intermediate range in sequence contacts and a contact potential for long range in sequence contacts. The coefficients in the energy function are chosen to maximize the ratio of the folding temperature to the glass transition temperature. We use the resulting optimized model to predict the structure of three α/β protein domains ranging in length from 81 to 115 residues. The resulting predictions align with low rms deviations to large portions of the native state. We have also calculated the free energy as a function of similarity to the native state for one of these three domains, and we show that, as expected from the optimization criteria, the free energy surface resembles a rough funnel to the native state. Finally, we briefly demonstrate the effect of roughness in the energy landscape on the dynamics.

Original languageEnglish (US)
Pages (from-to)1679-1684
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume100
Issue number4
DOIs
StatePublished - Feb 18 2003

ASJC Scopus subject areas

  • General

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