Abstract
In recent years pharmacophore modeling has become increasingly popular due to the development of software solutions and improvement in algorithms that allowed researchers to focus on interactions between protein and ligands instead of technical details of the software. At the same time, progress in computer hardware made molecular dynamics (MD) simulations on regular PC hardware possible. MD simulations are usually used, within the virtual screening process, to take into account the flexibility of the target and studying it in more realistic way. In order to do so, it is customary to use simulations before the virtual screening process and then use them for collecting some specific conformation of the target used. Furthermore, some researchers have demonstrated that the use of multiple crystal structures of the same protein can be valuable to better explore the role of the ligand within the binding pocket and then evaluate the most important interactions that are created during the host-guest recognition process. Findings derived from the MD analysis, especially focused on interactions, can be in fact exploited as features for pharmacophore generation or constraints to be used in the molecular docking as integrated steps of the whole virtual screening process. In this chapter, we will present the recent advances in the field pharmacophore modeling combined with the use of MD, a field well explored by our research group in the last 2 years.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
DesJarlais RL, Seibel GL, Kuntz ID et al (1990) Structure-based design of nonpeptide inhibitors specific for the human immunodeficiency virus 1 protease. Proc Natl Acad Sci U S A 87(17):6644–6648. https://doi.org/10.1073/pnas.87.17.6644
Acharya KR, Sturrock ED, Riordan JF et al (2003) Ace revisited: a new target for structure-based drug design. Nat Rev Drug Discov 2(11):891–902. https://doi.org/10.1038/nrd1227
Spyrakis F, Benedetti P, Decherchi S et al (2015) A pipeline to enhance ligand virtual screening: integrating molecular dynamics and fingerprints for ligand and proteins. J Chem Inf Model 55(10):2256–2274. https://doi.org/10.1021/acs.jcim.5b00169
Kapetanovic IM (2008) Computer aided drug discovery and development: in silico-chemico-biological approach. Chem Biol Interact 171(2):165–176. https://doi.org/10.1016/j.cbi.2006.12.006
Cerqueira NMFSA, Gesto D, Oliveira EF et al (2015) Receptor-based virtual screening protocol for drug discovery. Arch Biochem Biophys 582:56–67. https://doi.org/10.1016/j.abb.2015.05.011
Chang CA, Ai R, Gutierrez M et al (2012) Homology modeling of cannabinoid receptors: discovery of cannabinoid analogues for therapeutic use. In: Baron R (ed) Computational drug discovery and design. Methods in molecular biology (methods and protocols). Springer, New York. https://doi.org/10.1007/978-1-61,779-465-0_35
Ou-Yang S-S, Lu J-Y, Kong X-Q et al (2012) Computational drug discovery. Acta Pharmacol Sin 33(9):1131–1140. https://doi.org/10.1038/aps.2012.109
Dias R, de Azevedo WF (2008) Molecular docking algorithms. Curr Drug Targets 9(12):1040–1047. https://doi.org/10.2174/138945008786949432
Wu F, Xu T, He G et al (2012) Discovery of novel focal adhesion kinase inhibitors using a hybrid protocol of virtual screening approach based on multicomplex-based pharmacophore and molecular docking. Int J Mol Sci 13(12):15668–15678. https://doi.org/10.3390/ijms131215668
Agrawal R, Jain P, Dikshit SN et al (2013) Ligand-based pharmacophore detection, screening of potential pharmacophore and docking studies, to get effective glycogen synthase kinase inhibitors. Med Chem Res 22(11):5504–5535. https://doi.org/10.1007/s00044-013-0547-y
Dror O, Schneidman-Duhovny D, Inbar Y et al (2009) Novel approach for efficient pharmacophore-based virtual screening: method and applications. J Chem Inf Model 49(10):2333–2343. https://doi.org/10.1021/ci900263d
Langer T (2011) Pharmacophores for medicinal chemists: a personal view. Future Med Chem 3(8):901–904. https://doi.org/10.4155/fmc.11.34
Wolber G, Sippl W (2015) Pharmacophore identification and pseudo-receptor modeling. In: Wermuth CG et al (eds) The practice of medicinal chemistry, 4th edn. Academic Press, London, pp 489–510. https://doi.org/10.1016/B978-0-12-417,205-0.00021-3
Yang S-Y (2010) Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today 15(11-12):444–450. https://doi.org/10.1016/j.drudis.2010.03.013
Langer T, Wolber G (2004) Pharmacophore definition and 3D searches. Drug Discov Today Technol 1(3):203–207. https://doi.org/10.1016/j.ddtec.2004
Tutone M, Perricone U, Almerico AM (2017) Conf-VLKA: a structure-based revisitation of the virtual lock-and-key approach. J Mol Graph Model 71:50–57. https://doi.org/10.1016/j.jmgm.2016.11.006
Jorgensen WL (2004) The many roles of computation in drug discovery. Science 303(5665):1813–1818. https://doi.org/10.1126/science.1096361
Teague SJ (2003) Implications of protein flexibility for drug discovery. Nat Rev Drug Discov 2(7):527–541. https://doi.org/10.1038/nrd1129
B-Rao C, Subramanian J, Sharma SD (2009) Managing protein flexibility in docking and its applications. Drug Discov Today 14(7-8):394–400. https://doi.org/10.1016/j.drudis.2009.01.003
Gallicchio E, Levy RM (2011) Advances in all atom sampling methods for modeling protein-ligand binding affinities. Curr Opin Struct Biol 21(2):161–166. https://doi.org/10.1016/j.sbi.2011.01.010
Chen YC (2015) Beware of docking! Trends Pharmacol Sci 36(2):78–95. https://doi.org/10.1016/j.tips.2014.12.001
Shin WH, Kim JK, Kim DS et al (2013) GalaxyDock2: protein-ligand docking using beta-complex and global optimization. J Comput Chem 34(30):2647–2656. https://doi.org/10.1002/jcc.23438
Sherman W, Day T, Jacobson MP et al (2006) Novel procedure for modeling ligand/receptor induced fit effects. J Med Chem 49(2):534–553. https://doi.org/10.1021/jm050540c
Koska J, Spassov VZ, Maynard AJ et al (2008) Fully automated molecular mechanics based induced fit protein-ligand docking method. J Chem Inf Model 48(10):1965–1973. https://doi.org/10.1021/ci800081s
Bolia A, Gerek ZN, Ozkan SB (2014) BP-dock: a flexible docking scheme for exploring protein-ligand interactions based on unbound structures. J Chem Inf Model 54(3):913–925. https://doi.org/10.1021/ci4004927
Ivetac A, McCammon JA (2011) Molecular recognition in the case of flexible targets. Curr Pharm Des 17(17):1663–1671. https://doi.org/10.2174/138161211796355056
Forman-Kay JD (1999) The “dynamics” in the thermodynamics of binding. Nat Struct Biol 6:1086–1087. https://doi.org/10.1038/70008
Nichols SE, Baron R, McCammon JA (2012) On the use of molecular dynamics receptor conformations for virtual screening. In: Baron R (ed) Computational drug discovery and design. Methods in molecular biology (methods and protocols), vol 819. Springer, New York, NY. https://doi.org/10.1007/978-1-61,779-465-0_7
Totrov M, Abagyan R (2008) Flexible ligand docking to multiple receptor conformations: a practical alternative. Curr Opin Struct Biol 18(2):178–184. https://doi.org/10.1016/j.sbi.2008.01.004
Verkhivker GM, Bouzida D, Gehlhaar DK et al (2002) Complexity and simplicity of ligand-macromolecule interactions: the energy landscape perspective. Curr Opin Struct Biol 12(2):197–203. https://doi.org/10.1016/S0959-440X(02)00310-X
Abagyan R, Rueda M, Bottegoni G (2010) Recipes for the selection of experimental protein conformations for virtual screening. J Chem Inf Model 50(1):186–193. https://doi.org/10.1021/ci9003943
Isvoran A, Badel A, Craescu CT et al (2011) Exploring NMR ensembles of calcium binding proteins: perspectives to design inhibitors of protein-protein interactions. BMC Struct Biol 11:24. https://doi.org/10.1186/1472-6807-11-24
Miteva MA, Robert CH, Maréchal JD et al (2011) Receptor Flexibility in ligand docking and virtual screening. In: Miteva MA (ed) In silico lead discovery. Bentham Science Publishers, Emirate of Sharjah
Osguthorpe DJ, Sherman W, Hagler AT (2012) Generation of receptor structural ensembles for virtual screening using binding site shape analysis and clustering. Chem Biol Drug Des 80(2):182–193. https://doi.org/10.1111/j.1747-0285.2012.01396.x
Asses Y, Venkatraman V, Leroux V et al (2012) Exploring c-Met kinase flexibility by sampling and clustering its conformational space. Proteins 80(4):1227–1238. https://doi.org/10.1002/prot.24021
Degliesposti G, Portioli C, Parenti MD et al (2011) BEAR, a novel virtual screening methodology for drug discovery. J Biomol Screen 16(1):129–133. https://doi.org/10.1177/1087057110388276
Hou T, Wang J, Li Y et al (2011) Assessing the performance of the MM/PBSA and MM/GBSA methods: 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J Chem Inf Comput Sci 51(1):69–82. https://doi.org/10.1021/ci100275a
Proctor EA, Yin S, Tropsha A et al (2012) Discrete molecular dynamics distinguishes nativelike binding poses from decoys in difficult targets. Biophys J 102(1):144–151. https://doi.org/10.1016/j.bpj.2011.11.4008
Karplus M, McCammon JA (2002) Molecular dynamics simulations of biomolecules. Nat Struct Biol 9:646–652. https://doi.org/10.1038/nsb0902-646
Deng J, Lee KW, Sanchez T et al (2005) Dynamic receptor-based pharmacophore model development and its application in designing novel HIV-1 integrase inhibitors. J Med Chem 48(5):1496–1505. https://doi.org/10.1021/jm049410e
Ogrizek M, Turk S, Lesnik S et al (2015) Molecular dynamics to enhance structure-based virtual screening on cathepsin B. J Comput Aided Mol Des 29(8):707–712. https://doi.org/10.1007/s10822-015-9847-2
Tutone M, Chinnici A, Almerico AM et al (2016) Design, synthesis and preliminary evaluation of dopamine-amino acid conjugates as potential D1 dopaminergic modulators. Eur J Med Chem 124:435–444. https://doi.org/10.1016/j.ejmech.2016.08.051
Barril X, Morley SD (2005) Unveiling the full potential of flexible receptor docking using multiple crystallographic structures. J Med Chem 48(13):4432–4443. https://doi.org/10.1021/jm048972v
Bolstad ESD, Anderson AC (2009) In pursuit of virtual lead optimization: pruning ensembles of receptor structures for increased efficiency and accuracy during docking. Proteins 75(1):62–74. https://doi.org/10.1002/prot.22214
Amaro RE, Baron R, McCammon JA (2008) An improved relaxed complex scheme for receptor flexibility in computer-aided drug design. J Comput Aided Mol Des 22(9):693–705. https://doi.org/10.1007/s10822-007-9159-2
Martiny VY, Carbonell P, Lagorce D et al (2013) In silico mechanistic profiling to probe small molecule binding to Sulfotransferases. PLoS One 8(9):e73587. https://doi.org/10.1371/journal.pone.0073587
Rueda M, Bottegoni G, Abagyan R (2009) Consistent improvement of cross-docking results using binding site ensembles generated with elastic network normal modes. J Chem Inf Model 49(3):716–725. https://doi.org/10.1021/ci8003732
Leis S, Zacharias M (2011) Efficient inclusion of receptor flexibility in grid-based protein-ligand docking. J Comput Chem 32:3433–3439. https://doi.org/10.1002/jcc.21923
Korb O, Olsson TSG, Bowden SJ et al (2012) Potential and limitations of ensemble docking. J Chem Inf Model 52(5):1262–1274. https://doi.org/10.1021/ci2005934
Sgobba M, Caporuscio F, Anighoro A et al (2012) Application of a post-docking procedure based on MM-PBSA and MM-GBSA on single and multiple protein conformations. Eur J Med Chem 58:431–440. https://doi.org/10.1016/j.ejmech.2012.10.024
Berman HM, Westbrook J, Feng Z et al (2000) The protein data bank. Nucleic Acids Res 28(1):235–242. https://doi.org/10.1093/nar/28.1.235
Liebeschuetz J, Hennemann J, Olsson T et al (2012) The good, the bad and the twisted: a survey of ligand geometry in protein crystal structures. J Comput Aided Mol Des 26(2):169–183. https://doi.org/10.1007/s10822-011-9538-6
Reynolds CH (2014) Protein-ligand cocrystal structures: we can do better. ACS Med Chem Lett 5(7):727–729. https://doi.org/10.1021/ml500220a
Mirjalili V, Feig M (2013) Protein structure refinement through structure selection and averaging from molecular dynamics ensembles. J Chem Theory Comput 9(2):1294–1303. https://doi.org/10.1021/ct300962x
Whitesides GM, Krishnamurthy VM (2005) Designing ligands to bind proteins. Q Rev Biophys 38(4):385–395. https://doi.org/10.1017/S0033583506004240
Deng J, Sanchez T, Neamati N et al (2006) Dynamic pharmacophore model optimization: identification of novel HIV-1 integrase inhibitors. J Med Chem 49(5):1684–1692. https://doi.org/10.1021/jm0510629
Bowman AL, Makriyannis A (2011) Approximating protein flexibility through dynamic pharmacophore models: application to fatty acid amide hydrolase (FAAH). J Chem Inf Model 51(12):3247–3253. https://doi.org/10.1021/ci200371z
Carlson HA, Masukawa KM, Rubins K et al (2000) Developing a dynamic pharmacophore model for HIV-1 integrase. J Med Chem 43(11):2100–2114. https://doi.org/10.1021/jm990322h
Choudhury C, Priyakumar UD, Sastry GN (2015) Dynamics based pharmacophore models for screening potential inhibitors of mycobacterial cyclopropane synthase. J Chem Inf Model 55(4):848–860. https://doi.org/10.1021/ci500737b
Mallik B, Morìkis D (2005) Development of a quasi-dynamic pharmacophore model for anti-complement peptide analogues. J Am Chem Soc 127(31):10967–10976. https://doi.org/10.1021/ja051004c
Saez NJ, Mobli M, Bieri M et al (2011) A dynamic pharmacophore drives the interaction between Psalmotoxin-1 and the putative drug target acid-sensing ion channel 1a. Mol Pharmacol 80(5):796–808. https://doi.org/10.1124/mol.111.072207
Thangapandian S, John S, Lee Y et al (2011) Dynamic structure-based pharmacophore model development: a new and effective addition in the histone deacetylase 8 (HDAC8) inhibitor discovery. Int J Mol Sci 12(12):9440–9462. https://doi.org/10.3390/ijms12129440
Wieder M, Perricone U, Boresch S et al (2016) Evaluating the stability of pharmacophore features using molecular dynamics simulations. Biochem Biophys Res Commun 470(3):685–689. https://doi.org/10.1016/j.bbrc.2016.01.081
Wieder M, Garon A, Perricone U et al (2017) Common hits approach: combining pharmacophore modeling and molecular dynamics simulations. J Chem Inf Model 57(2):365–385. https://doi.org/10.1021/acs.jcim.6b00674
Perricone U, Wieder M, Seidel T et al (2017) A molecular dynamics-shared pharmacophore approach to boost early-enrichment virtual screening: a case study on peroxisome proliferator-activated receptor α. ChemMedChem 12(16):1399–1407. https://doi.org/10.1002/cmdc.201600526
Cereto-Massague A, Ojeda MJ, Joosten RP et al (2013) The good, the bad and the dubious: VHELIBS, a validation helper for ligands and binding sites. J Cheminform 5:1–9. https://doi.org/10.1186/1758-2946-5-36
Madhavi Sastry G, Adzhigirey M, Day T et al (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27(3):221–234. https://doi.org/10.1007/s10822-013-9644-8
Eswar N, Webb B, Marti-Renom MA et al (2007) Comparative protein structure modeling using Modeller. Curr Protoc Bioinformatics 2:Unit 2.9. https://doi.org/10.1002/0471250953.bi0506s15
Guo Z, Mohanty U, Noehre J et al (2010) Probing the alpha-helical structural stability of stapled p53 peptides: molecular dynamics simulations and analysis. Chem Biol Drug Des 75(4):348–359. https://doi.org/10.1111/j.1747-0285.2010.00951.x
Shivakumar D, Williams J, Wu YJ et al (2010) Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field. J Chem Theory Comput 6(5):1509–1519. https://doi.org/10.1021/ct900587b
Wolber G, Langer T (2005) LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model 45(1):160–169. https://doi.org/10.1021/ci049885e
Wolber G, Seidel T, Bendix F et al (2008) Molecule-pharmacophore superpositioning and pattern matching in computational drug design. Drug Discov Today 13(1-2):23–29. https://doi.org/10.1016/j.drudis.2007.09.007
Halgren TA, Murphy RB, Friesner RA et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47(7):1750–1759. https://doi.org/10.1021/jm030644s
Friesner RA, Banks JL, Murphy RB et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47(7):1739–1749. https://doi.org/10.1021/jm0306430
Olsson MHM, Søndergaard CR, Rostkowski M et al (2011) PROPKA3: consistent treatment of internal and surface residues in empirical p K a predictions. J Chem Theory Comput 7(2):525–537. https://doi.org/10.1021/ct100578z
Søndergaard CR, Olsson MHM, Rostkowski M et al (2011) Improved treatment of ligands and coupling effects in empirical calculation and rationalization of p K a values. J Chem Theory Comput 7(7):2284–2295. https://doi.org/10.1021/ct200133y
Mark P, Nilsson L (2001) Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K. J Phys Chem A 105(43):9954–9960. https://doi.org/10.1021/jp003020w
Meza JC (2010) Steepest descent. Wiley Interdiscip Rev Comput Stat 2(6):719–722. https://doi.org/10.1002/wics.117
Andrew G, Gao J (2007) Scalable training of L1 -regularized log-linear models, In: Proceedings of the 24th international conference on Machine learning - ICML ‘07, pp. 33–40
Malouf R (2002) A comparison of algorithms for maximum entropy parameter estimation, In: Proceeding of the 6th conference on Natural language learning - COLING-02, pp. 1–7
Mysinger MM, Carchia M, Irwin JJ et al (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55(14):6582–6594. https://doi.org/10.1021/jm300687e
Cereto-Massagué A, Guasch L, Valls C et al (2012) DecoyFinder: an easy-to-use python GUI application for building target-specific decoy sets. Bioinformatics 28(12):1661–1662. https://doi.org/10.1093/bioinformatics/bts249
Berthold MR, Cebron N, Dill F et al (2009) KNIME - the Konstanz information miner. SIGKDD Explor 11(1):26–31. https://doi.org/10.1145/1656274.1656280
Zhao W, Hevener KE, White SW et al (2009) A statistical framework to evaluate virtual screening. BMC Bioinformatics 10:225. https://doi.org/10.1186/1471-2105-10-225
Truchon JF, Bayly CI (2007) Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem. J Chem Inf Model 47(2):488–508. https://doi.org/10.1021/ci600426e
Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27(8):861–874. https://doi.org/10.1016/j.patrec.2005.10.010
Buch I, Giorgino T, De Fabritiis G (2011) Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations. Proc Natl Acad Sci U S A 108(25):10184–10110,189. doi:https://doi.org/10.1073/pnas.1103547108
Legge FS, Budi A, Treutlein H et al (2006) Protein flexibility: multiple molecular dynamics simulations of insulin chain B. Biophys Chem 119(2):146–157. https://doi.org/10.1016/j.bpc.2005.08.002
Perez JJ, Tomas MS, Rubio-Martinez J (2016) Assessment of the sampling performance of multiple-copy dynamics versus a unique trajectory. J Chem Inf Model 56(10):1950–1962. https://doi.org/10.1021/acs.jcim.6b00347
Wieder M, Perricone U, Seidel T et al (2016) Comparing pharmacophore models derived from crystal structures and from molecular dynamics simulations. Monatsh Chem 147(3):553–563. https://doi.org/10.1007/s00706-016-1674-1
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Perricone, U., Wieder, M., Seidel, T., Langer, T., Padova, A. (2018). The Use of Dynamic Pharmacophore in Computer-Aided Hit Discovery: A Case Study. In: Mavromoustakos, T., Kellici, T. (eds) Rational Drug Design. Methods in Molecular Biology, vol 1824. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8630-9_19
Download citation
DOI: https://doi.org/10.1007/978-1-4939-8630-9_19
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-8629-3
Online ISBN: 978-1-4939-8630-9
eBook Packages: Springer Protocols