GemAffinity: A scoring function for predicting binding affinity and virtual screening

Kai Cheng Hsu, Yen Fu Chen, Jinn Moon Yang

研究成果: 書貢獻/報告類型會議貢獻

摘要

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.
原文英語
主出版物標題2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009
頁面309-314
頁數6
DOIs
出版狀態已發佈 - 2009
對外發佈
事件2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009 - Washington, D.C., 美国
持續時間: 十一月 1 2009十一月 4 2009

其他

其他2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009
國家/地區美国
城市Washington, D.C.
期間11/1/0911/4/09

ASJC Scopus subject areas

  • 人工智慧
  • 軟體
  • 生物醫學工程
  • 健康資訊學

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