Abstract
This chapter focuses on computational techniques for identifying and optimizing lead molecules, with a special emphasis on natural compounds. A number of case studies have been specifically discussed, such as the case of the naphthyridine scaffold, discovered through a structure-based virtual screening (SBVS) and proposed as the starting point for further lead optimization process, to enhance its telomeric RNA selectivity. Another example is the case of Liphagal, a tetracyclic meroterpenoid extracted from Aka coralliphaga, known as PI3Kα inhibitor, provide an evidence for the design of new active congeners against PI3Kα using molecular dynamics (MD) simulations. These are only two of the numerous examples of the computational techniques’ powerful in drug design and drug discovery fields. Finally, the design of drugs that can simultaneously interact with multiple targets as a promising approach for treating complicated diseases has been reported. An example of polypharmacological agents are the compounds extracted from mushrooms identified by means of molecular docking experiments. This chapter may be a useful manual of molecular modeling techniques used in the lead-optimization and lead identification processes.
Acknowledgements
This work was partially supported by Prof. Francesco Ortuso. The authors also gratefully acknowledge the helpful comments and suggestions of the book editor and reviewers, which have improved the presentation.
References
[1] Newman DJ, Cragg GM. Natural products as sources of new drugs from 1981 to 2014. J Nat Prod. 2016;79:629–61.10.1021/acs.jnatprod.5b01055Search in Google Scholar PubMed
[2] Langer T, Hoffmann RD. Virtual screening: an effective tool for lead structure discovery? Curr Pharm Des. 2001;7:509–27.10.2174/1381612013397861Search in Google Scholar PubMed
[3] Artese A, Alcaro S, Moraca F, Reina R, Ventura M, Costantino G, et al. State-of-the-art and dissemination of computational tools for drug-design purposes: a survey among Italian academics and industrial institutions. Future Med Chem. 2013;5:907–27.10.4155/fmc.13.59Search in Google Scholar PubMed
[4] Maruca A, Moraca F, Rocca R, Molisani F, Alcaro F, Gidaro MC, et al. Chemoinformatic database building and in silico hit-identification of potential multi-targeting bioactive compounds extracted from mushroom species. Molecules. 2017;22:1571.10.3390/molecules22091571Search in Google Scholar
[5] Lionta E, Spyrou G, Vassilatis DK, Cournia Z. Structure-based virtual screening for drug discovery: principles, applications and recent advances. Curr Top Med Chem. 2014;14:1923–38.10.2174/1568026614666140929124445Search in Google Scholar PubMed
[6] Ripphausen P, Nisius B, Bajorath J. State-of-the-art in ligand-based virtual screening. Drug Discov Today. 2011;16:372–6.10.1016/j.drudis.2011.02.011Search in Google Scholar PubMed
[7] Chen YZ, Zhi DG. Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins. 2001;43:217–26.10.1002/1097-0134(20010501)43:2<217::AID-PROT1032>3.0.CO;2-GSearch in Google Scholar PubMed
[8] Lauro G, Masullo M, Piacente S, Riccio R, Bifulco G. Inverse Virtual Screening allows the discovery of the biological activity of natural compounds. Bioorg Med Chem. 2012;20:3596–602.10.1016/j.bmc.2012.03.072Search in Google Scholar PubMed
[9] Vyas V, Jain A, Jain A, Gupta A. Virtual screening: a fast tool for drug design. SciPharm. 2008;76:333–60.10.3797/scipharm.0803-03Search in Google Scholar
[10] Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001;46:3–26.10.1016/S0169-409X(00)00129-0Search in Google Scholar PubMed
[11] Cheng F, Li W, Liu G, Tang Y. In silico ADMET prediction: recent advances, current challenges and future trends. Curr Top Med Chem. 2013;13:1273–89.10.2174/15680266113139990033Search in Google Scholar PubMed
[12] Zuegg J, Cooper MA. Drug-likeness and increased hydrophobicity of commercially available compound libraries for drug screening. Curr Top Med Chem. 2012;12:1500–13.10.2174/156802612802652466Search in Google Scholar PubMed
[13] Blagg J. Structure–activity relationships for in vitro and in vivo toxicity. Annu Rep Med Chem. 2006;41:353–68.10.1016/S0065-7743(06)41024-1Search in Google Scholar
[14] Baell JB, Holloway GA. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem. 2010;53:2719–40.10.1021/jm901137jSearch in Google Scholar PubMed
[15] Metz JT, Huth JR, Hajduk PJ. Enhancement of chemical rules for predicting compound reactivity towards protein thiol groups. J Comput Aided Mol Des. 2007;21:139–44.10.1007/s10822-007-9109-zSearch in Google Scholar PubMed
[16] Athanasiadis E, Cournia Z, Spyrou G. ChemBioServer: a web-based pipeline for filtering, clustering and visualization of chemical compounds used in drug discovery. Bioinformatics. 2012;28:3002–3.10.1093/bioinformatics/bts551Search in Google Scholar PubMed
[17] Lagorce D, Sperandio O, Galons H, Miteva MA, Villoutreix BO. FAF-Drugs2: free ADME/tox filtering tool to assist drug discovery and chemical biology projects. BMC Bioinf. 2008;9:396.10.1186/1471-2105-9-396Search in Google Scholar
[18] Lagorce D, Maupetit J, Baell J, Sperandio O, Tuffléry P, Miteva MA, et al. The FAF-Drugs2 server: a multistep engine to prepare electronic chemical compound collections. Bioinformatics. 2011;27:2018–20.10.1093/bioinformatics/btr333Search in Google Scholar
[19] Kalliokoski T, Salo HS, Lahtela-Kakkonen M, Poso A. The effect of ligand-based tautomer and protomer prediction on structure-based virtual screening. J Chem Inf Model. 2009;49:2742–8.10.1021/ci900364wSearch in Google Scholar PubMed
[20] Sadowski J, Rudolph C, Gasteiger J. The generation of 3D-models of host-guest. Anal Chim Acta. 1992;265:233–41.10.1016/0003-2670(92)85029-6Search in Google Scholar
[21] Milletti F, Vulpetti A. Tautomer preference in PDB complexes and its impact on structure-based drug discovery. J Chem Inf Model. 2010;50:1062–74.10.1021/ci900501cSearch in Google Scholar PubMed
[22] Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem. 2009;30:2785–91.10.1002/jcc.21256Search in Google Scholar PubMed
[23] LigPrep. Version 3.9. New York (USA): Schrödinger LLC, 2016.Search in Google Scholar
[24] Canada, H3A 2R7: Montreal, QC. Molecular Operating Environment (MOE), St West, Suite #910, 2013.Search in Google Scholar
[25] MAPS. Version 3.4. Paris, France: Scienomics SARL, 2014.10.1016/S1773-035X(14)72320-2Search in Google Scholar
[26] http://wiki.uoft.bkslab.org/index.php/Preparing_the_ligand.Search in Google Scholar
[27] http://www.accelrys.com.Search in Google Scholar
[28] Hyperchem. Gainesville, FL: HyperCube.Search in Google Scholar
[29] Grinter SZ, Yan C, Huang SY, Jiang L, Zou X. Automated large-scale file preparation, docking, and scoring: evaluation of ITScore and STScore using the 2012 community structure-activity resource benchmark. J Chem Inf Model. 2013;53:1905–14.10.1021/ci400045vSearch in Google Scholar PubMed
[30] Wermuth CG, Ganellin CR, Lindberg P, Mitscher LA. Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1998).10.1016/S0065-7743(08)61101-XSearch in Google Scholar
[31] Wolber G, Langer T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model. 2005;45:160–9.10.1021/ci049885eSearch in Google Scholar PubMed
[32] Kirchmair J, Markt P, Distinto S, Wolber G, Langer T. Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection–what can we learn from earlier mistakes? J Comput Aided Mol Des. 2008;22:213–28.10.1007/s10822-007-9163-6Search in Google Scholar PubMed
[33] Braga RC, Andrade CH. Assessing the performance of 3D pharmacophore models in virtual screening: how good are they? Curr Top Med Chem. 2013;13:1127–38.10.2174/1568026611313090010Search in Google Scholar PubMed
[34] Triballeau N, Acher F, Brabet I, Pin JP, Bertrand HO. Virtual screening workflow development guided by the “receiver operating characteristic” curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. J Med Chem. 2005;48:2534–47.10.1021/jm049092jSearch in Google Scholar PubMed
[35] Zou KH, O’Malley AJ, Mauri L. Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation. 2007;115:654–7.10.1161/CIRCULATIONAHA.105.594929Search in Google Scholar PubMed
[36] Sheridan RP, Singh SB, Fluder EM, Kearsley SK. Protocols for bridging the peptide to nonpeptide gap in topological similarity searches. J Chem Inf Comput Sci. 2001;41:1395–406.10.1021/ci0100144Search in Google Scholar PubMed
[37] McInnes C. Virtual screening strategies in drug discovery. Curr Opin Chem Biol. 2007;11:494–502.10.1016/j.cbpa.2007.08.033Search in Google Scholar PubMed
[38] Sun H. Pharmacophore-based virtual screening. Curr Med Chem. 2008;15:1018–24.10.2174/092986708784049630Search in Google Scholar PubMed
[39] Yang SY. Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today. 2010;15:444–50.10.1016/j.drudis.2010.03.013Search in Google Scholar PubMed
[40] Ewing TJA, Kuntz ID. Critical evaluation of search algorithms for automated molecular docking and database screening. J Comput Chem. 1997;18:1175–89.10.1002/(SICI)1096-987X(19970715)18:9<1175::AID-JCC6>3.0.CO;2-OSearch in Google Scholar
[41] Rarey M, Kramer B, Lengauer T, Klebe G. A fast flexible docking method using an incremental construction algorithm. J Mol Biol. 1996;261:470–89.10.1006/jmbi.1996.0477Search in Google Scholar PubMed
[42] Jones G, Willett P, Glen RC, Leach AR, Taylor R. Development and validation of a genetic algorithm for flexible docking. J Mol Biol. 1997;267:727–48.10.1006/jmbi.1996.0897Search in Google Scholar PubMed
[43] Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, et al. Glide: a new approach for rapid, accurate docking and scoring. Method and assessment of docking accuracy. J Med Chem. 2004;47:1739–49.10.1021/jm0306430Search in Google Scholar PubMed
[44] Abagyan R, Totrov M. Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins. J Mol Biol. 1994;235:983–1002.10.1006/jmbi.1994.1052Search in Google Scholar PubMed
[45] McGann M. FRED and HYBRID docking performance on standardized datasets. J Comput Aided Mol Des. 2012;26:897–906.10.1007/s10822-012-9584-8Search in Google Scholar PubMed
[46] Halgren T. New method for fast and accurate binding-site identification and analysis. Chem Biol Drug Des. 2007;69:146–8.10.1111/j.1747-0285.2007.00483.xSearch in Google Scholar PubMed
[47] Baroni M, Cruciani G, Sciabola S, Perruccio F, Mason JS. A common reference framework for analyzing/comparing proteins and ligands. Fingerprints for ligands and proteins (FLAP): theory and application. J Chem Inf Model. 2007;47:279–94.10.1021/ci600253eSearch in Google Scholar PubMed
[48] Goodford PJ. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem. 1985;28:849–57.10.1021/jm00145a002Search in Google Scholar PubMed
[49] Cross S, Baroni M, Goracci L, Cruciani G. GRID-based three-dimensional pharmacophores I: fLAPpharm, a novel approach for pharmacophore elucidation. J Chem Inf Model. 2012;52:2587–98.10.1021/ci300153dSearch in Google Scholar PubMed
[50] Catalyst. Version 4.11. San Diego, CA, USA: Accelry’s Inc., 2007.Search in Google Scholar
[51] https://www.ncbi.nlm.nih.gov/.Search in Google Scholar
[52] Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, et al. PubChem substance and compound databases. Nucleic Acids Res. 2016;44:D1202–13.10.1093/nar/gkv951Search in Google Scholar PubMed
[53] Bento AP, Gaulton A, Hersey A, Bellis LJ, Chambers J, Davies M, et al. The ChEMBL bioactivity database: an update. Nucleic Acids Res. 2014;42:D1083–90.10.1093/nar/gkt1031Search in Google Scholar PubMed
[54] http://www.chemspider.com.Search in Google Scholar
[55] https://chemaxon.com/products/marvin.Search in Google Scholar
[56] https://chemaxon.com/products/instant-jchem.Search in Google Scholar
[57] http://www.mutalig.eu/.Search in Google Scholar
[58] http://www.unicz.chemotheca.it.Search in Google Scholar
[59] Ortuso F, Bagetta D, Maruca A, Talarico C, Bolognesi ML, Haider N, et al. The Mu.Ta.Lig. Chemotheca: a community-populated molecular database for multi-target ligands identification and compound-repurposing. Front Chem. 2018;6:130.10.3389/fchem.2018.00130Search in Google Scholar PubMed
[60] Rocca R, Moraca F, Costa G, Nadai M, Scalabrin M, Talarico C, et al. Identification of G-quadruplex DNA/RNA binders: structure-based virtual screening and biophysical characterization. Biochim Biophys Acta Gen Subj. 2017;1861:1329–40.10.1016/j.bbagen.2016.12.023Search in Google Scholar PubMed
[61] Brooks B, Karplus M. Harmonic dynamics of proteins: normal modes and fluctuations in bovine pancreatic trypsin inhibitor. Proc Natl Acad Sci USA. 1983;80:6571–5.10.1073/pnas.80.21.6571Search in Google Scholar
[62] Case DA, Karplus M. Dynamics of ligand binding to heme proteins. J Mol Biol. 1979;132:343–68.10.1016/0022-2836(79)90265-1Search in Google Scholar PubMed
[63] Colonna-Cesari F, Perahia D, Karplus M, Eklund H, Brädén CI, Tapia O. Interdomain motion in liver alcohol dehydrogenase. Structural and energetic analysis of the hinge bending mode. J Biol Chem. 1986;261:15273–80.10.1016/S0021-9258(18)66863-2Search in Google Scholar PubMed
[64] Harvey SC, Prabhakaran M, Mao B, McCammon JA. Phenylalanine transfer RNA: molecular dynamics simulation. Science. 1984;223:1189–91.10.1126/science.6560785Search in Google Scholar PubMed
[65] Karplus M, McCammon JA. Molecular dynamics simulations of biomolecules. Nat Struct Biol. 2002;9:646–52.10.1038/nsb0902-646Search in Google Scholar PubMed
[66] Fichthorn KA, Weinberg WH. Theoretical foundations of dynamical Monte Carlo simulations. J Chem Phys. 1991;95:1090–6.10.1063/1.461138Search in Google Scholar
[67] Minary P, Levitt M. Probing protein fold space with a simplified model. J Mol Biol. 2008;375:920–33.10.1016/j.jmb.2007.10.087Search in Google Scholar PubMed PubMed Central
[68] Huber T, van Gunsteren WF. SWARM-MD: searching conformational space by cooperative molecular dynamics. J Phys Chem A. 1998;102:5937-43.10.1021/jp9806258Search in Google Scholar
[69] Larsen EM, Wilson MR, Taylor RE. Conformation-activity relationships of polyketide natural products. Nat Prod Rep. 2015;32:1183–206.10.1039/C5NP00014ASearch in Google Scholar PubMed PubMed Central
[70] Allen MP. Computer simulation of liquids.. Oxford: Oxford University Press, 2007.Search in Google Scholar
[71] Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, et al. CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem. 2010;31:671–90.10.1002/jcc.21367Search in Google Scholar PubMed PubMed Central
[72] Arfken G. “The method of steepest descents.” § 7.4. Mathematical methods for physicists, 3rd ed. Orlando, FL: Academic Press, 1985:428–36.Search in Google Scholar
[73] Hestenes MR, Stiefel E. Methods of conjugate gradients for solving linear systems. J Res Natl Bur Stand. 1952;49:409–36.10.6028/jres.049.044Search in Google Scholar
[74] Nocedal J, Wright SJ. Numerical optimization Vol. 35. New York: Springer, 2006.Search in Google Scholar
[75] Kirkpatrick S, Gelatt CD, Vecchi MP. Optimization by simulated annealing. Science. 1983;220:671–80.10.1126/science.220.4598.671Search in Google Scholar PubMed
[76] Kohn W, Sham LJ. Self-consistent equations including exchange and correlation effects. PhysRev. 1965;140:A1133.10.1103/PhysRev.140.A1133Search in Google Scholar
[77] Darden T, York D, Pedersen L. Particle mesh Ewald: an N⋅log(N) method for Ewald sums in large systems. J Chem Phys. 1993;98:10089–92.10.1063/1.464397Search in Google Scholar
[78] Zhao H, Caflisch A. Molecular dynamics in drug design. Eur J Med Chem. 2015;91:4–14.10.1016/j.ejmech.2014.08.004Search in Google Scholar PubMed
[79] Rocca R, Moraca F, Costa G, Alcaro S, Distinto S, Maccioni E, et al. Structure-based virtual screening of novel natural alkaloid derivatives as potential binders of h-telo and c-myc DNA G-quadruplex conformations. Molecules. 2014;20:206–23.10.3390/molecules20010206Search in Google Scholar PubMed
[80] Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, et al. Scalable molecular dynamics with NAMD. J Comput Chem. 2005;26:1781–802.10.1002/jcc.20289Search in Google Scholar PubMed
[81] Harris SJ, Parry RV, Westwick J, Ward SG. Phosphoinositide lipid phosphatases: natural regulators of phosphoinositide 3-kinase signaling in T lymphocytes. J Biol Chem. 2008;283:2465–9.10.1074/jbc.R700044200Search in Google Scholar
[82] Wymann MP, Zvelebil M, Laffargue M. Phosphoinositide 3-kinase signalling which way to target? Trends Pharmacol Sci. 2003;24:366–76.10.1016/S0165-6147(03)00163-9Search in Google Scholar PubMed
[83] Sundstrom TJ, Anderson AC, Wright DL. Inhibitors of phosphoinositide-3-kinase: a structure-based approach to understanding potency and selectivity. Org Biomol Chem. 2009;7:840–50.10.1039/b819067bSearch in Google Scholar PubMed
[84] Marion F, Williams DE, Patrick BO, Hollander I, Mallon R, Kim SC, et al. Liphagal, a Selective inhibitor of PI3 kinase alpha isolated from the sponge akacoralliphaga: structure elucidation and biomimetic synthesis. Org Lett. 2006;8:321–4.10.1021/ol052744tSearch in Google Scholar PubMed
[85] Li T, Wang G. Computer-aided targeting of the PI3K/Akt/mTOR pathway: toxicity reduction and ther-apeutic opportunities. Int J Mol Sci. 2014;15:18856–91.10.3390/ijms151018856Search in Google Scholar PubMed PubMed Central
[86] Gao Y, Ma Y, Yang G, Li Y. Molecular dynamics simulations to investigate the binding mode of the natural product liphagal with phosphoinositide 3-Kinase α. Molecules. 2016;21:857.10.3390/molecules21070857Search in Google Scholar PubMed PubMed Central
[87] Ryckbosch SM, Wender PA, Pande VS. Molecular dynamics simulations reveal ligand-controlled positioning of a peripheral protein complex in membranes. Nat Commun. 2017;8:6.10.1038/s41467-016-0015-8Search in Google Scholar PubMed PubMed Central
[88] Limongelli V, Bonomi M, Marinelli L, Gervasio FL, Cavalli A, Novellino E, et al. Molecular basis of cyclooxygenase enzymes (COXs) selective inhibition. Proc Natl Acad Sci USA. 2010;107:5411–6.10.1073/pnas.0913377107Search in Google Scholar PubMed PubMed Central
[89] Troussicot L, Guillière F, Limongelli V, Walker O, Lancelin JM. Funnel-metadynamics and solution NMR to estimate protein−ligand affinities. J Am Chem Soc. 2015;137:1273–81.10.1021/ja511336zSearch in Google Scholar PubMed
[90] Yuan X, Raniolo S, Limongelli V, Xu Y. The molecular mechanism underlying ligand binding to the membrane-embedded site of a g-protein-coupled receptor. J Chem Theory Comput. 2018;14:2761–70.10.1021/acs.jctc.8b00046Search in Google Scholar PubMed
© 2019 Walter de Gruyter GmbH, Berlin/Boston
