Combining statistical potentials with dynamics-based entropies improves selection from protein decoys and docking poses. J Phys Chem B 2012 Jun 14;116(23):6725-31
Date
04/12/2012Pubmed ID
22490366DOI
10.1021/jp2120143Scopus ID
2-s2.0-84862290983 (requires institutional sign-in at Scopus site) 23 CitationsAbstract
Protein structure prediction and protein-protein docking are important and widely used tools, but methods to confidently evaluate the quality of a predicted structure or binding pose have had limited success. Typically, either knowledge-based or physics-based energy functions are employed to evaluate a set of predicted structures (termed "decoys" in structure prediction and "poses" in docking), with the lowest energy structure being assumed to be the one closest to the native state. While successful for many cases, failures are still common. Thus, improvements to structure evaluation methods are essential for future improvements. In this work, we combine multibody statistical potentials with dynamics models, evaluating fluctuation-based entropies that include contributions from the entire structure. This leads to enhanced selection of native-like structures for CASP9 decoys, refined ClusPro docking poses, as well as large sets of docking poses from the Benchmark 3.0 and Dockground data sets. The data used include both bound and unbound docking, and positive results are found for each type. Not only does this method yield improved average results, but for high quality docking poses, we often pick the best pose.
Author List
Zimmermann MT, Leelananda SP, Kloczkowski A, Jernigan RLAuthor
Michael T. Zimmermann PhD Director, Associate Professor in the Data Science Institute department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
EntropyMolecular Dynamics Simulation
Protein Conformation
Proteins
