Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling.

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TitleSite-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling.
Publication TypeJournal Article
Year of Publication2014
AuthorsYu, W, Lakkaraju, SKaushik, E Raman, P, Mackerell, AD
JournalJ Comput Aided Mol Des
Date Published2014 May
KeywordsCluster Analysis, Drug Design, Ligands, Models, Chemical

Database screening using receptor-based pharmacophores is a computer-aided drug design technique that uses the structure of the target molecule (i.e. protein) to identify novel ligands that may bind to the target. Typically receptor-based pharmacophore modeling methods only consider a single or limited number of receptor conformations and map out the favorable binding patterns in vacuum or with a limited representation of the aqueous solvent environment, such that they may suffer from neglect of protein flexibility and desolvation effects. Site-Identification by Ligand Competitive Saturation (SILCS) is an approach that takes into account these, as well as other, properties to determine 3-dimensional maps of the functional group-binding patterns on a target receptor (i.e. FragMaps). In this study, a method to use the FragMaps to automatically generate receptor-based pharmacophore models is presented. It converts the FragMaps into SILCS pharmacophore features including aromatic, aliphatic, hydrogen-bond donor and acceptor chemical functionalities. The method generates multiple pharmacophore hypotheses that are then quantitatively ranked using SILCS grid free energies. The pharmacophore model generation protocol is validated using three different protein targets, including using the resulting models in virtual screening. Improved performance and efficiency of the SILCS derived pharmacophore models as compared to published docking studies, as well as a recently developed receptor-based pharmacophore modeling method is shown, indicating the potential utility of the approach in rational drug design.

Alternate JournalJ. Comput. Aided Mol. Des.
PubMed ID24610239
PubMed Central IDPMC4048638
Grant ListR01 CA107331 / CA / NCI NIH HHS / United States
CA107331 / CA / NCI NIH HHS / United States