Full metadata record
DC FieldValueLanguage
dc.contributor.authorHsu, Kai-Chengen_US
dc.contributor.authorChen, Yen-Fuen_US
dc.contributor.authorYang, Jinn-Moonen_US
dc.date.accessioned2014-12-08T15:21:02Z-
dc.date.available2014-12-08T15:21:02Z-
dc.date.issued2009en_US
dc.identifier.isbn978-0-7695-3885-3en_US
dc.identifier.urihttp://hdl.handle.net/11536/14952-
dc.identifier.urihttp://dx.doi.org/10.1109/BIBM.2009.24en_US
dc.description.abstractPrediction 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 (TIC), 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.en_US
dc.language.isoen_USen_US
dc.subjectcomponenten_US
dc.subjectbinding affinity predictionen_US
dc.subjectscoring functionsen_US
dc.subjectprotein-ligand interactionsen_US
dc.subjectstructure-based drug designen_US
dc.subjectvirtual screeningen_US
dc.titleGemAffinity: A Scoring Function for Predicting Binding Affinity and Virtual Screeningen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/BIBM.2009.24en_US
dc.identifier.journal2009 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINEen_US
dc.citation.spage309en_US
dc.citation.epage314en_US
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.identifier.wosnumberWOS:000275900200056-
Appears in Collections:Conferences Paper


Files in This Item:

  1. 000275900200056.pdf

If it is a zip file, please download the file and unzip it, then open index.html in a browser to view the full text content.