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dc.contributor.authorWróblewska, Liliana
dc.contributor.authorJagielska, Anna
dc.contributor.authorSkolnick, Jeffrey
dc.date.accessioned2011-10-28T19:33:30Z
dc.date.available2011-10-28T19:33:30Z
dc.date.issued2008-04
dc.identifier.citationWroblewska, L, Jagielska A, Skolnick J. 2008. Development of a physics-based force field for the scoring and refinement of protein models. Biophysical journal. 94(8):3227-40.en
dc.identifier.issn0006-3495
dc.identifier.urihttp://hdl.handle.net/1853/41938
dc.description© 2008 by the Biophysical Societyen_US
dc.description.abstractThe minimal requirements of a physics-based potential that can refine protein structures are the existence of a correlation between the energy with native similarity and the scoring of the native structure as the lowest in energy. To develop such a force field, the relative weights of the Amber ff03 all-atom potential supplemented by an explicit hydrogen-bond potential were adjusted by global optimization of energetic and structural criteria for a large set of protein decoys generated for a set of 58 nonhomologous proteins. The average correlation coefficient of the energy with TM-score significantly improved from 0.25 for the original ff03 potential to 0.65 for the optimized force field. The fraction of proteins for which the native structure had lowest energy increased from 0.22 to 0.90. Moreover, use of an explicit hydrogen-bond potential improves scoring performance of the force field. Promising preliminary results were obtained in applying the optimized potentials to refine protein decoys using only an energy criterion to choose the best decoy among sampled structures. For a set of seven proteins, 63% of the decoys improve, 18% get worse, and 19% are not changed.en
dc.language.isoen_USen
dc.publisherGeorgia Institute of Technologyen
dc.subjectRefinement of protein structuresen
dc.subjectSurface area energyen
dc.subjectForce field optimizationen
dc.subjectProtein decoysen
dc.titleDevelopment of a physics-based force field for the scoring and refinement of protein modelsen
dc.typeArticleen
dc.contributor.corporatenameGeorgia Institute of Technology. Center for the Study of Systems Biology
dc.contributor.corporatenameGeorgia Institute of Technology. School of Biology
dc.publisher.originalBiophysical Society


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