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231224s2014 xx |||||o 00| ||eng c |
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|a 10.1002/jcc.23579
|2 doi
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|a pubmed24n0787.xml
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Grinter, Sam Z
|e verfasserin
|4 aut
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| 245 |
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|a A Bayesian statistical approach of improving knowledge-based scoring functions for protein-ligand interactions
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|c 2014
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Completed 13.11.2014
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|a Date Revised 04.04.2014
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Copyright © 2014 Wiley Periodicals, Inc.
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|a 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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| 650 |
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4 |
|a Journal Article
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| 650 |
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|a Research Support, N.I.H., Extramural
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| 650 |
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|a Research Support, Non-U.S. Gov't
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| 650 |
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4 |
|a Research Support, U.S. Gov't, Non-P.H.S.
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| 650 |
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4 |
|a knowledge-based scoring function
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| 650 |
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4 |
|a ligand interactions
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| 650 |
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4 |
|a molecular docking
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| 650 |
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4 |
|a protein
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| 650 |
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4 |
|a sparse data
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| 650 |
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|a Ligands
|2 NLM
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| 650 |
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7 |
|a Proteins
|2 NLM
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| 700 |
1 |
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|a Zou, Xiaoqin
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 35(2014), 12 vom: 05. Mai, Seite 932-43
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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| 773 |
1 |
8 |
|g volume:35
|g year:2014
|g number:12
|g day:05
|g month:05
|g pages:932-43
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| 856 |
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|u http://dx.doi.org/10.1002/jcc.23579
|3 Volltext
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