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PubMed · 17143895

A knowledge-based move set for protein folding.

William W Chen·Jae Shick Yang·Eugene I Shakhnovich
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Abstract

The free energy landscape of protein folding is rugged, occasionally characterized by compact, intermediate states of low free energy. In computational folding, this landscape leads to trapped, compact states with incorrect secondary structure. We devised a residue-specific, protein backbone move set for efficient sampling of protein-like conformations in computational folding simulations. The move set is based on the selection of a small set of backbone dihedral angles, derived from clustering dihedral angles sampled from experimental structures. We show in both simulated annealing and replica exchange Monte Carlo (REMC) simulations that the knowledge-based move set, when compared with a conventional move set, shows statistically significant improved ability at overcoming kinetic barriers, reaching deeper energy minima, and achieving correspondingly lower RMSDs to native structures. The new move set is also more efficient, being able to reach low energy states considerably faster. Use of this move set in determining the energy minimum state and for calculating thermodynamic quantities is discussed.

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GlycineKineticsKnowledge BasesLeucineModels, MolecularMonte Carlo MethodProtein ConformationProtein FoldingProteinsThermodynamicsJournal Article

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William W Chen, Jae Shick Yang, Eugene I Shakhnovich. 2007-02-15. A knowledge-based move set for protein folding.. https://doi.org/10.1002/prot.21237

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