Protein Structure Prediction: The Next Generation

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dc.creatorPrentiss, Michael C.
dc.creatorHardin, Corey
dc.creatorEastwood, Michael P.
dc.creatorZong, Chenghong
dc.creatorWolynes, Peter G.
dc.date2006-06-12
dc.date.accessioned2026-07-07T07:18:55Z
dc.date.available2026-07-07T07:18:55Z
dc.descriptionOver the last 10-15 years a general understanding of the chemical reaction of protein folding has emerged from statistical mechanics. The lessons learned from protein folding kinetics based on energy landscape ideas have benefited protein structure prediction, in particular the development of coarse grained models. We survey results from blind structure prediction. We explore how second generation prediction energy functions can be developed by introducing information from an ensemble of previously simulated structures. This procedure relies on the assumption of a funnelled energy landscape keeping with the principle of minimal frustration. First generation simulated structures provide an improved input for associative memory energy functions in comparison to the experimental protein structures chosen on the basis of sequence alignment.
dc.identifierhttps://arxiv.org/abs/q-bio/0606012
dc.identifierhttp://arxiv.org/abs/q-bio/0606012
dc.identifierJournal of Chemical Theory and Computation, Volume 2 Issue 3 (May 09, 2006)
dc.identifierdoi:10.1021/ct0600058
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/114475
dc.subjectBiomolecules
dc.titleProtein Structure Prediction: The Next Generation
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