Abstract
Supervised MD (SuMD) is a computational method that enables the exploration of ligand–receptor recognition pathway in a reduced timescale. The performance speedup is due to the incorporation of a tabu-like supervision algorithm on the ligand–receptor approaching distance into a classic molecular dynamics (MD) simulation. SuMD enables the investigation of ligand–receptor binding events independently from the starting position, chemical structure of the ligand (small molecules or peptides), and also from its receptor-binding affinity. The application of SuMD highlights an appreciable capability of the technique to reproduce the crystallographic structures of several ligand–protein complexes and can provide high-quality protein–ligand models of for which yet experimental confirmation of binding mode is not available.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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
Cooke RM, Brown AJH, Marshall FH, Mason JS (2015) Structures of G protein-coupled receptors reveal new opportunities for drug discovery. Drug Discov Today 20(11):1355–1364. https://doi.org/10.1016/j.drudis.2015.08.003
Dror RO, Pan AC, Arlow DH et al (2011) Pathway and mechanism of drug binding to G-protein-coupled receptors. Proc Natl Acad Sci U S A 108(32):13118–13123. https://doi.org/10.1073/pnas.1104614108
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):10184–10189. https://doi.org/10.1073/pnas.1103547108
Ferruz N, Harvey MJ, Mestres J, De Fabritiis G (2015) Insights from fragment hit binding assays by molecular simulations. J Chem Inf Model 55(10):2200–2205. https://doi.org/10.1021/acs.jcim.5b00453
Kuhn B, Guba W, Hert J et al (2016) A real-world perspective on molecular design. J Med Chem 59(9):4087–4102. https://doi.org/10.1021/acs.jmedchem.5b01875
Sabbadin D, Moro S (2014) Supervised molecular dynamics (SuMD) as a helpful tool to depict GPCR-ligand recognition pathway in a nanosecond time scale. J Chem Inf Model 54(2):372–376. https://doi.org/10.1021/ci400766b
Cuzzolin A, Sturlese M, Deganutti G et al (2016) Deciphering the complexity of ligand-protein recognition pathways using supervised molecular dynamics (SuMD) simulations. J Chem Inf Model 56(4):687–705. https://doi.org/10.1021/acs.jcim.5b00702
Jaakola V-P, Griffith MT, Hanson MA et al (2008) The 2.6 angstrom crystal structure of a human A2A adenosine receptor bound to an antagonist. Science 322(5905):1211–1217. https://doi.org/10.1126/science.1164772
Congreve M, Andrews SP, Doré AS et al (2012) Discovery of 1,2,4-triazine derivatives as adenosine A(2A) antagonists using structure based drug design. J Med Chem 55(5):1898–1903. https://doi.org/10.1021/jm201376w
Doré AS, Robertson N, Errey JC et al (2011) Structure of the adenosine A(2A) receptor in complex with ZM241385 and the xanthines XAC and caffeine. Structure 19(9):1283–1293. https://doi.org/10.1016/j.str.2011.06.014
Salmaso V, Sturlese M, Cuzzolin A, Moro S (2017) Exploring protein–peptide recognition pathways using a supervised molecular dynamics approach. Structure 25(4):655–662.e2. https://doi.org/10.1016/j.str.2017.02.009
Scott DE, Bayly AR, Abell C, Skidmore J (2016) Small molecules, big targets: drug discovery faces the protein–protein interaction challenge. Nat Rev Drug Discov 15(8):533–550. https://doi.org/10.1038/nrd.2016.29
Kussie PH, Gorina S, Marechal V et al (1996) Structure of the MDM2 oncoprotein bound to the p53 tumor suppressor transactivation domain. Science 274(5289):948–953. https://doi.org/10.1126/science.274.5289.948
Petros AM, Nettesheim DG, Wang Y et al (2000) Rationale for Bcl-xL/bad peptide complex formation from structure, mutagenesis, and biophysical studies. Protein Sci 9(12):2528–2534. https://doi.org/10.1110/ps.9.12.2528
Baek S, Kutchukian PS, Verdine GL et al (2012) Structure of the stapled p53 peptide bound to Mdm2. J Am Chem Soc 134(1):103–106. https://doi.org/10.1021/ja2090367
Deganutti G, Welihinda A, Moro S (2017) Comparison of the human A2A adenosine receptor recognition by adenosine and Inosine: new insight from supervised molecular dynamics simulations. ChemMedChem 12(16):1319–1326. https://doi.org/10.1002/cmdc.201700200
Cheng RKY, Segala E, Robertson N et al (2017) Structures of human A1 and A2A adenosine receptors with Xanthines reveal determinants of selectivity. Structure 25(8):1275–1285.e4. https://doi.org/10.1016/j.str.2017.06.012
Williams T, Kelley C Gnuplot 4.5: an interactive plotting program, version 4.5; http://gnuplot.info (Accessed October 2015)
Harvey MJ, Giupponi G, Fabritiis GD (2009) ACEMD: accelerating biomolecular dynamics in the microsecond time scale. J Chem Theory Comput 5(6):1632–1639. https://doi.org/10.1021/ct9000685
Case DA, Babin V, Berryman JT et al (2014) AMBER 14
Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25(9):1157–1174. https://doi.org/10.1002/jcc.20035
MacKerell AD, Bashford D, Bellott M et al (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 102(18):3586–3616. https://doi.org/10.1021/jp973084f
MacKerell AD, Banavali N, Foloppe N (2000) Development and current status of the CHARMM force field for nucleic acids. Biopolymers 56(4):257–265. https://doi.org/10.1002/1097-0282(2000)56:4<257::AID-BIP10029>3.0.CO;2-W
Vanommeslaeghe K, MacKerell AD (2012) Automation of the CHARMM general force field (CGenFF) I: bond perception and atom typing. J Chem Inf Model 52(12):3144–3154. https://doi.org/10.1021/ci300363c
Vanommeslaeghe K, Raman EP, MacKerell AD (2012) Automation of the CHARMM general force field (CGenFF) II: assignment of bonded parameters and partial atomic charges. J Chem Inf Model 52(12):3155–3168. https://doi.org/10.1021/ci3003649
Jakowiecki J, Filipek S (2016) Hydrophobic ligand entry and exit pathways of the CB1 cannabinoid receptor. J Chem Inf Model 56(12):2457–2466. https://doi.org/10.1021/acs.jcim.6b00499
Fronik P, Gaiser BI, Sejer Pedersen D (2017) Bitopic ligands and metastable binding sites: opportunities for G protein-coupled receptor (GPCR) medicinal chemistry. J Med Chem 60(10):4126–4134. https://doi.org/10.1021/acs.jmedchem.6b01601
Sabbadin D, Ciancetta A, Moro S (2014) Perturbation of fluid dynamics properties of water molecules during G protein-coupled receptor-ligand recognition: the human A2A adenosine receptor as a key study. J Chem Inf Model 54(10):2846–2855. https://doi.org/10.1021/ci500397y
Paoletta S, Sabbadin D, von Kügelgen I et al (2015) Modeling ligand recognition at the P2Y12 receptor in light of X-ray structural information. J Comput Aided Mol Des 29(8):737–756. https://doi.org/10.1007/s10822-015-9858-z
Lipkowitz K (1995) Abuses of molecular mechanics: pitfalls to avoid. J Chem Educ 72(12):1070. https://doi.org/10.1021/ed072p1070
Brameld KA, Kuhn B, Reuter DC, Stahl M (2008) Small molecule conformational preferences derived from crystal structure data. A medicinal chemistry focused analysis. J Chem Inf Model 48(1):1–24. https://doi.org/10.1021/ci7002494
Lovell SC, Word JM, Richardson JS, Richardson DC (2000) The penultimate rotamer library. Proteins 40(3):389–408. https://doi.org/10.1002/1097-0134(20000815)40:3<389::AID-PROT50>3.0.CO;2-2
Allen FH (2002) The Cambridge structural database: a quarter of a million crystal structures and rising. Acta Crystallogr B 58:380–388
Stahl M, Baier S (2015) How many molecules does it take to tell a story? Case studies, language, and an epistemic view of medicinal chemistry. ChemMedChem 10(6):949–956. https://doi.org/10.1002/cmdc.201500091
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
Sabbadin, D., Salmaso, V., Sturlese, M., Moro, S. (2018). Supervised Molecular Dynamics (SuMD) Approaches in Drug Design. 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_17
Download citation
DOI: https://doi.org/10.1007/978-1-4939-8630-9_17
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