IFPTarget: A Customized Virtual Target Identification Method Based on Protein-Ligand Interaction Fingerprinting Analyses.

J Chem Inf Model

Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Sichuan 610041, China.

Published: July 2017

AI Article Synopsis

  • The text discusses the challenges in identifying protein targets for small molecules in drug discovery, highlighting the limitations of current universal scoring methods that may overlook important binding features of specific targets.
  • Introduced is IFPTarget, a new approach that utilizes interaction fingerprinting for more accurate target-specific analysis and a ranking system (Cvalue) to identify and prioritize potential protein targets effectively.
  • Evaluation shows that IFPTarget improves binding pose predictions and successfully uncovers both known and new targets, exemplified by its identification of the metallo-β-lactamase VIM-2 as a target for quercetin.

Article Abstract

Small-molecule target identification is an important and challenging task for chemical biology and drug discovery. Structure-based virtual target identification has been widely used, which infers and prioritizes potential protein targets for the molecule of interest (MOI) principally via a scoring function. However, current "universal" scoring functions may not always accurately identify targets to which the MOI binds from the retrieved target database, in part due to a lack of consideration of the important binding features for an individual target. Here, we present IFPTarget, a customized virtual target identification method, which uses an interaction fingerprinting (IFP) method for target-specific interaction analyses and a comprehensive index (Cvalue) for target ranking. Evaluation results indicate that the IFP method enables substantially improved binding pose prediction, and Cvalue has an excellent performance in target ranking for the test set. When applied to screen against our established target library that contains 11,863 protein structures covering 2842 unique targets, IFPTarget could retrieve known targets within the top-ranked list and identified new potential targets for chemically diverse drugs. IFPTarget prediction led to the identification of the metallo-β-lactamase VIM-2 as a target for quercetin as validated by enzymatic inhibition assays. This study provides a new in silico target identification tool and will aid future efforts to develop new target-customized methods for target identification.

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http://dx.doi.org/10.1021/acs.jcim.7b00225DOI Listing

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