Pharmacophore modeling using site-identification by ligand competitive saturation (SILCS) with multiple probe molecules.

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TitlePharmacophore modeling using site-identification by ligand competitive saturation (SILCS) with multiple probe molecules.
Publication TypeJournal Article
Year of Publication2015
AuthorsYu, W, Lakkaraju, SKaushik, E Raman, P, Fang, L, Mackerell, AD
JournalJ Chem Inf Model
Volume55
Issue2
Pagination407-20
Date Published2015 Feb 23
ISSN1549-960X
KeywordsAlgorithms, Drug Design, High-Throughput Screening Assays, Hydrogen Bonding, Ligands, Models, Chemical, Models, Molecular, Molecular Docking Simulation, Molecular Probes, Proteins, Receptors, Drug, Reproducibility of Results, User-Computer Interface, Water
Abstract

Receptor-based pharmacophore modeling is an efficient computer-aided drug design technique that uses the structure of the target protein to identify novel leads. However, most methods consider protein flexibility and desolvation effects in a very approximate way, which may limit their use in practice. The Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling protocol (SILCS-Pharm) was introduced recently to address these issues, as SILCS naturally takes both protein flexibility and desolvation effects into account by using full molecular dynamics simulations to determine 3D maps of the functional group-affinity patterns on a target receptor. In the present work, the SILCS-Pharm protocol is extended to use a wider range of probe molecules including benzene, propane, methanol, formamide, acetaldehyde, methylammonium, acetate and water. This approach removes the previous ambiguity brought by using water as both the hydrogen-bond donor and acceptor probe molecule. The new SILCS-Pharm protocol is shown to yield improved screening results, as compared to the previous approach based on three target proteins. Further validation of the new protocol using five additional protein targets showed improved screening compared to those using common docking methods, further indicating improvements brought by the explicit inclusion of additional feature types associated with the wider collection of probe molecules in the SILCS simulations. The advantage of using complementary features and volume constraints, based on exclusion maps of the protein defined from the SILCS simulations, is presented. In addition, reranking using SILCS-based ligand grid free energies is shown to enhance the diversity of identified ligands for the majority of targets. These results suggest that the SILCS-Pharm protocol will be of utility in rational drug design.

DOI10.1021/ci500691p
Alternate JournalJ Chem Inf Model
PubMed ID25622696
PubMed Central IDPMC4339487
Grant ListR01 CA107331 / CA / NCI NIH HHS / United States
CA107331, / CA / NCI NIH HHS / United States