Evaluating protein structure-prediction schemes using energy landscape theory

M. P. Eastwood, C. Hardin, Z. Luthey-Schulten, P. G. Wolynes

Research output: Contribution to journalArticlepeer-review


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 funnel-like 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.

Original languageEnglish (US)
Pages (from-to)475-497
Number of pages23
JournalIBM Journal of Research and Development
Issue number3-4
StatePublished - 2001
Externally publishedYes

ASJC Scopus subject areas

  • Computer Science(all)

Fingerprint Dive into the research topics of 'Evaluating protein structure-prediction schemes using energy landscape theory'. Together they form a unique fingerprint.

Cite this