Skip to main content
SpringerLink
Log in

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

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Scheme 1
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  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 90–102

  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 295–329

  3. Leach AR, Shoichet BK, Peishoff CE (2006) Prediction of protein–ligand interactions. Docking and scoring: successes and gaps. J Med Chem 49(20):5851–5855

    Article  CAS  Google Scholar 

  4. Leach AR, Gillet VJ, Lewis RA, Taylor R (2009) Three-dimensional pharmacophore methods in drug discovery. J Med Chem 53(2):539–558

    Article  Google Scholar 

  5. Ewing TA, Makino S, Skillman AG, Kuntz I (2001) DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Des 15(5):411–428

    Article  CAS  Google Scholar 

  6. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791

    Article  CAS  Google Scholar 

  7. Sanders MPA, McGuire R, Roumen L, de Esch IJP, de Vlieg J, Klomp JPG, de Graaf C (2012) From the protein’s perspective: the benefits and challenges of protein structure-based pharmacophore modeling. MedChemComm 3(1):28–38

    Article  CAS  Google Scholar 

  8. Goodford PJ (1985) A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 28(7):849–857

    Article  CAS  Google Scholar 

  9. Joseph-McCarthy D, Alvarez JC (2003) Automated generation of MCSS-derived pharmacophoric DOCK site points for searching multiconformation databases. Proteins 51(2):189–202

    Article  CAS  Google Scholar 

  10. Cross S, Baroni M, Goracci L, Cruciani G (2012) GRID-based three-dimensional pharmacophores I: FLAPpharm, a novel approach for pharmacophore elucidation. J Chem Inf Model 52(10):2587–2598

    Article  CAS  Google Scholar 

  11. Hu B, Lill MA (2012) Protein pharmacophore selection using hydration-site analysis. J Chem Inf Model 52(4):1046–1060

    Article  CAS  Google Scholar 

  12. Teague SJ (2003) Implications of protein flexibility for drug discovery. Nat Rev Drug Discov 2(7):527–541

    Article  CAS  Google Scholar 

  13. Cozzini P, Kellogg GE, Spyrakis F, Abraham DJ, Costantino G, Emerson A, Fanelli F, Gohlke H, Kuhn LA, Morris GM, Orozco M, Pertinhez TA, Rizzi M, Sotriffer CA (2008) Target flexibility: an emerging consideration in drug discovery and design. J Med Chem 51(20):6237–6255

    Article  CAS  Google Scholar 

  14. Fuentes G, Dastidar SG, Madhumalar A, Verma CS (2011) Role of protein flexibility in the discovery of new drugs. Drug Dev Res 72(1):26–35

    Article  CAS  Google Scholar 

  15. Lam AR, Bhattacharya S, Patel K, Hall SE, Mao A, Vaidehi N (2011) Importance of receptor flexibility in binding of cyclam compounds to the chemokine receptor CXCR4. J Chem Inf Model 51(1):139–147

    Article  CAS  Google Scholar 

  16. de Beer SBA, Vermeulen NPE, Oostenbrink C (2010) The role of water molecules in computational drug design. Curr Top Med Chem 10(1):55–66

    Article  Google Scholar 

  17. Wang L, Berne BJ, Friesner RA (2011) Ligand binding to protein-binding pockets with wet and dry regions. Proc Natl Acad Sci U S A 108(4):1326–1330

    Article  CAS  Google Scholar 

  18. Wong SE, Lightstone FC (2011) Accounting for water molecules in drug design. Expert Opin Drug Discov 6(1):65–74

    Article  CAS  Google Scholar 

  19. Yang Y, Lightstone FC, Wong SE (2013) Approaches to efficiently estimate solvation and explicit water energetics in ligand binding: the use of WaterMap. Expert Opin Drug Discov 8(3):277–287

    Article  CAS  Google Scholar 

  20. Guvench O, MacKerell AD Jr (2009) Computational fragment-based binding site identification by ligand competitive saturation. PLoS Comput Biol 5(7):e1000435

    Article  Google Scholar 

  21. Raman EP, Yu W, Guvench O, MacKerell AD Jr (2011) Reproducing crystal binding modes of ligand functional groups using site-identification by ligand competitive saturation (SILCS) simulations. J Chem Inf Model 51(4):877–896

    Article  CAS  Google Scholar 

  22. Raman EP, Yu W, Lakkaraju SK, MacKerell AD (2013) Inclusion of multiple fragment types in the site identification by ligand competitive saturation (SILCS) approach. J Chem Inf Model 53(12):3384–3398

    Article  CAS  Google Scholar 

  23. Huang N, Shoichet BK, Irwin JJ (2006) Benchmarking sets for molecular docking. J Med Chem 49(23):6789–6801

    Article  CAS  Google Scholar 

  24. Foster TJ, MacKerell AD, Guvench O (2012) Balancing target flexibility and target denaturation in computational fragment-based inhibitor discovery. J Comput Chem 33(23):1880–1891

    Article  CAS  Google 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):42

    Article  CAS  Google Scholar 

  26. Molecular operating environment (MOE), 2012.10 (2012). Chemical Computing Group Inc., Montreal

  27. Discovery studio modeling environment (2013). Accelrys Software Inc., San Diego

  28. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323

    Article  Google Scholar 

  29. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF chimera—a visualization system for exploratory research and analysis. J Comput Chem 25(13):1605–1612

    Article  CAS  Google Scholar 

  30. De Loof H, Nilsson L, Rigler R (1992) Molecular dynamics simulation of galanin in aqueous and nonaqueous solution. J Am Chem Soc 114(11):4028–4035

    Article  Google Scholar 

  31. Bernstein FC, Koetzle TF, Williams GJB, Meyer EF Jr, Brice MD, Rodgers JR, Kennard O, Shimanouchi T, Tasumi M (1977) The protein data bank: a computer-based archival file for macromolecular structures. J Mol Biol 112(3):535–542

    Article  CAS  Google 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):13650–13662

    CAS  Google Scholar 

  33. Word JM, Lovell SC, Richardson JS, Richardson DC (1999) Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation. J Mol Biol 285(4):1735–1747

    Article  CAS  Google 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):1545–1614

    Article  CAS  Google 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):3586–3616

    Article  CAS  Google 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):1400–1415

    Article  CAS  Google Scholar 

  37. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79(2):926–935

    Article  CAS  Google Scholar 

  38. Halgren TA (1996) Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem 17(5–6):490–519

    Article  CAS  Google Scholar 

  39. Zhong S, Chen X, Zhu X, Dziegielewska B, Bachman KE, Ellenberger T, Ballin JD, Wilson GM, Tomkinson AE, Mackerell AD Jr (2008) Identification and validation of human DNA ligase inhibitors using computer-aided drug design. J Med Chem 51(15):4553–4562

    Article  CAS  Google 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):400–411

    Article  CAS  Google Scholar 

  41. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19(14):1639–1662

    Article  CAS  Google Scholar 

  42. Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39(4):561–577

    CAS  Google Scholar 

  43. Wang S, Milne GWA, Yan X, Posey IJ, Nicklaus MC, Graham L, Rice WG (1996) Discovery of novel, non-peptide HIV-1 protease inhibitors by pharmacophore searching. J Med Chem 39(10):2047–2054

    Article  CAS  Google Scholar 

  44. Maignan S, Guilloteau J-P, Pouzieux S, Choi-Sledeski YM, Becker MR, Klein SI, Ewing WR, Pauls HW, Spada AP, Mikol V (2000) Crystal structures of human factor Xa complexed with potent inhibitors. J Med Chem 43(17):3226–3232

    Article  CAS  Google 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):2749–2769

    Article  CAS  Google Scholar 

  46. Blaney JM, Hansch C, Silipo C, Vittoria A (1984) Structure-activity relationships of dihydrofolated reductase inhibitors. Chem Rev 84(4):333–407

    Article  CAS  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

  1. Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD, 21201, USA

    Wenbo Yu, Sirish Kaushik Lakkaraju, E. Prabhu Raman & Alexander D. MacKerell Jr.

Authors
  1. Wenbo Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sirish Kaushik Lakkaraju
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. E. Prabhu Raman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alexander D. MacKerell Jr.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexander D. MacKerell Jr..

Electronic supplementary material

Below is the link to the electronic supplementary material.

10822_2014_9728_MOESM1_ESM.pdf

Table including parameters used to develop SILCS pharmacophore models, a figure and description of the DHFR FragMaps generated in the present study and ROC plots of VS results using docking and pharmacophore modeling methods. (PDF 1720 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yu, W., Lakkaraju, S.K., Raman, E.P. et al. Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling. J Comput Aided Mol Des 28, 491–507 (2014). https://doi.org/10.1007/s10822-014-9728-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-014-9728-0

Keywords

Search

Navigation