NDLI: GemAffinity: A Scoring Function for Predicting Binding Affinity and Virtual Screening

Content Provider	IEEE Xplore Digital Library
Author	Kai-Cheng Hsu Yen-Fu Chen Jinn-Moon Yang
Copyright Year	2009
Abstract	Prediction of protein-ligand binding affinities is an important issue in molecular recognition and virtual screening. We have developed a scoring function, namely GemAffinity, to predict binding affinities by analyzing 88 descriptors derived from 891 protein-ligand structures selected from the Protein Data Bank (PDB). Based on these 88 descriptors, we derived GemAffinity using a stepwise regression method to identify five descriptors, including van der Waals contact; metal-ligand interactions; water effects; ligand deformation penalties; and highly conserved residues interacting to a bound ligand with hydrogen bonds. GemAffinity was evaluated on an independent set, and the correlation between predicted and experimental values is 0.572. GemAffinity is the best among 13 methods on this set. Our GemAffinity was then applied to virtual screening for thymidine kinase (TK), human carbonic anhydrase II (HCAII), estrogen receptor of antagonists (ER) and agonists (ERA). Experimental results indicate that GemAffinity is able to reduce the disadvantages (i.e. preferring highly polar or high molecular weight compounds) of energy-based scoring functions. In addition, GemAffinity easily combined with other scoring functions to enrich screening accuracies. We believe that GemAffinity is useful to predict binding affinity and virtual screening.
Starting Page	309
Ending Page	314
File Size	704703
Page Count	6
File Format	PDF
ISBN	9780769538853
DOI	10.1109/BIBM.2009.24
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Drugs Atomic measurements protein-ligand interactions Protein engineering structure-based drug design virtual screening Hydrogen Humans Water conservation scoring functions Biology Lead compounds binding affinity prediction Bioinformatics Testing
Content Type	Text
Resource Type	Article

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