Evaluating protein structure-prediction schemes using energy landscape theory

Authors:
M. P. Eastwood;C. Hardin;Z. Luthey-Schulten;P. G. Wolynes
Affiliations:
Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, California;Department of Chemistry, University of Illinois, Urbana, Illinois;Department of Chemistry, University of Illinois, Urbana, Illinois;Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, California
Venue:
IBM Journal of Research and Development
Year:
2001

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Abstract

Protein structure prediction is beginning to be, at least partially, successful. Evaluating predictions, however, has many elements of subjectivity, making it difficult to determine the nature and extent of improvements that are most needed. We describe how the funnellike nature of energy functions used for protein structure prediction determines their quality and can be quantified using landscape theory and multiple histogram sampling methods. Prediction algorithms exhibit a "caldera"-like landscape rather than a perfectly funneled one. Estimates are made of the expected number of effectively distinct structures produced by a prediction algorithm.