A Bayesian statistical approach of improving knowledge-based scoring functions for protein-ligand interactions
Copyright © 2014 Wiley Periodicals, Inc.
| Veröffentlicht in: | Journal of computational chemistry. - 1984. - 35(2014), 12 vom: 05. Mai, Seite 932-43 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2014
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| Zugriff auf das übergeordnete Werk: | Journal of computational chemistry |
| Schlagworte: | Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. knowledge-based scoring function ligand interactions molecular docking protein sparse data Ligands |
| Zusammenfassung: | Copyright © 2014 Wiley Periodicals, Inc. Knowledge-based scoring functions are widely used for assessing putative complexes in protein-ligand and protein-protein docking and for structure prediction. Even with large training sets, knowledge-based scoring functions face the inevitable problem of sparse data. Here, we have developed a novel approach for handling the sparse data problem that is based on estimating the inaccuracies in knowledge-based scoring functions. This inaccuracy estimation is used to automatically weight the knowledge-based scoring function with an alternative, force-field-based potential (FFP) that does not rely on training data and can, therefore, provide an improved approximation of the interactions between rare chemical groups. The current version of STScore, a protein-ligand scoring function using our method, achieves a binding mode prediction success rate of 91% on the set of 100 complexes by Wang et al., and a binding affinity correlation of 0.514 with the experimentally determined affinities in PDBbind. The method presented here may be used with other FFPs and other knowledge-based scoring functions and can also be applied to protein-protein docking and protein structure prediction |
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| Beschreibung: | Date Completed 13.11.2014 Date Revised 04.04.2014 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1096-987X |
| DOI: | 10.1002/jcc.23579 |