Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling
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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.
KeywordsComputer Virtual screening Drug design Lead discovery Enrichment
This work was supported by NIH grant CA107331, Maryland Industrial Partnerships Award 5212 and the Samuel Waxman Cancer Research Foundation. The authors acknowledge computer time and resources from the Computer Aided Drug Design (CADD) Center at the University of Maryland, Baltimore.
Conflict of interest
ADM is co-founder and Chief Scientific Officer of SilcsBio LLC.
- 1.Yu W, Guvench O, MacKerell AD, Jr. (2013) Computational approaches for the design of protein–protein interaction inhibitors. In: Zinzalla G (ed) Understanding and exploiting protein–protein interactions as drug targets. Future Science Book Series. Future Science Ltd, London, UK, pp 90–102Google Scholar
- 2.Zhong S, Oashi T, Yu W, Shapiro P, MacKerell AD, Jr. (2012) Prospects of modulating protein–protein interactions. In: Gohlke H (ed) Protein–ligand interactions. Wiley KGaA, Weinheim, Germany, pp 295–329Google Scholar
- 25.Cao X, Yap J, Newell-Rogers M, Peddaboina C, Jiang W, Papaconstantinou H, Jupitor D, Rai A, Jung K-Y, Tubin R, Yu W, Vanommeslaeghe K, Wilder P, MacKerell A, Fletcher S, Smythe R (2013) The novel BH3 alpha-helix mimetic JY-1-106 induces apoptosis in a subset of cancer cells (lung cancer, colon cancer and mesothelioma) by disrupting Bcl-xL and Mcl-1 protein–protein interactions with Bak. Mol Cancer 12(1):42CrossRefGoogle Scholar
- 26.Molecular operating environment (MOE), 2012.10 (2012). Chemical Computing Group Inc., MontrealGoogle Scholar
- 27.Discovery studio modeling environment (2013). Accelrys Software Inc., San DiegoGoogle Scholar
- 32.Bolin JT, Filman DJ, Matthews DA, Hamlin RC, Kraut J (1982) Crystal structures of Escherichia coli and Lactobacillus casei dihydrofolate reductase refined at 1.7 A resolution. I. General features and binding of methotrexate. J Biol Chem 257(22):13650–13662Google Scholar
- 34.Brooks BR, Brooks CL III, Mackerell AD Jr, Nilsson L, Petrella RJ, Roux B, Won Y, Archontis G, Bartels C, Boresch S, Caflisch A, Caves L, Cui Q, Dinner AR, Feig M, Fischer S, Gao J, Hodoscek M, Im W, Kuczera K, Lazaridis T, Ma J, Ovchinnikov V, Paci E, Pastor RW, Post CB, Pu JZ, Schaefer M, Tidor B, Venable RM, Woodcock HL, Wu X, Yang W, York DM, Karplus M (2009) CHARMM: the biomolecular simulation program. J Comput Chem 30(10):1545–1614CrossRefGoogle Scholar
- 35.Mackerell AD Jr, Bashford D, Bellott M, Dunbrack RL, Evanseck JD, Field MJ, Fischer S, Gao J, Guo H, Ha S, Joseph-McCarthy D, Kuchnir L, Kuczera K, Lau FTK, Mattos C, Michnick S, Ngo T, Nguyen DT, Prodhom B, Reiher WE, Roux B, Schlenkrich M, Smith JC, Stote R, Straub J, Watanabe M, Wiórkiewicz-Kuczera J, Yin D, Karplus M (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 102(18):3586–3616CrossRefGoogle Scholar
- 36.Mackerell AD Jr, Feig M, Brooks CL III (2004) Extending the treatment of backbone energetics in protein force fields: limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations. J Comput Chem 25(11):1400–1415CrossRefGoogle Scholar
- 40.Cerchietti LC, Ghetu AF, Zhu X, Da Silva GF, Zhong S, Matthews M, Bunting KL, Polo JM, Fares C, Arrowsmith CH, Yang SN, Garcia M, Coop A, Mackerell AD Jr, Prive GG, Melnick A (2010) A small-molecule inhibitor of BCL6 kills DLBCL cells in vitro and in vivo. Cancer Cell 17(4):400–411CrossRefGoogle Scholar
- 42.Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39(4):561–577Google Scholar
- 45.Matter H, Defossa E, Heinelt U, Blohm P-M, Schneider D, Muller A, Herok S, Schreuder H, Liesum A, Brachvogel V, Lonze P, Walser A, Al-Obeidi F, Wildgoose P (2002) Design and quantitative structure-activity relationship of 3-Amidinobenzyl-1H-indole-2-carboxamides as potent, nonchiral, and selective inhibitors of blood coagulation factor Xa. J Med Chem 45(13):2749–2769CrossRefGoogle Scholar