Abstract
In the current drug development process, molecular dynamics (MD) simulations have proven to be very useful. This chapter provides an overview of the current applications of MD simulations in drug discovery, from detecting protein druggable sites and validating drug docking outcomes to exploring protein conformations and investigating the influence of mutations on its structure and functions. In addition, this chapter emphasizes various strategies to improve the conformational sampling efficiency in molecular dynamics simulations. With a growing computer power and developments in the production of force fields and MD techniques, the importance of MD simulations in helping the drug development process is projected to rise significantly in the future.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lin X (2022) Chapter 27 Applications of molecular dynamics simulations in drug discovery. In: Tripathi T, Dubey VK (eds) Advances in protein molecular and structural biology methods. Academic Press, pp 455–465. https://www.sciencedirect.com/science/article/pii/B9780323902649000271
Vakhrushev AV (2018) Introductory chapter: molecular dynamics: basic tool of nanotechnology simulations for “Production 4.0” revolution. In: Vakhrushev A (ed) Molecular dynamics. IntechOpen, Rijeka, p Ch. 1. https://doi.org/10.5772/intechopen.79045
Salo-Ahen OMH, Alanko I, Bhadane R et al (2021) Molecular dynamics simulations in drug discovery and pharmaceutical development. PRO 9(1):71. https://doi.org/10.3390/pr9010071
Hasan MR, Alsaiari AA, Fakhurji BZ, Molla MHR, Asseri AH, Sumon MAA, Park MN, Ahammad F, Kim B (2022) Application of mathematical modeling and computational tools in the modern drug design and development process. Molecules 27:4169. https://doi.org/10.3390/molecules27134169
Lindorff-Larsen K, Piana S, Dror RO, Shaw DE (1979) How fast-folding proteins fold. Science 334(6055):517–520. https://doi.org/10.1126/science.1208351
Liou SH, Myers WK, Oswald JD, Britt RD, Goodin DB (2017) Putidaredoxin binds to the same site on cytochrome P450cam in the open and closed conformation. Biochemistry 56(33):4371–4378. https://doi.org/10.1021/acs.biochem.7b00564
Lindorff-Larsen K, Maragakis P, Piana S, Eastwood MP, Dror RO, Shaw DE (2012) Systematic validation of protein force fields against experimental data. PLoS One 7(2):e32131. https://doi.org/10.1371/journal.pone.0032131
Salvatella X (2014) Understanding protein dynamics using conformational ensembles. In: Li HK, Zhang X, Jun YM (eds) Protein conformational dynamics [Internet]. Springer International Publishing, Cham, pp 67–85. https://doi.org/10.1007/978-3-319-02970-2_3
Orellana L (2019) Large-scale conformational changes and protein function: breaking the in silico barrier. Front Mol Biosci 6. https://www.frontiersin.org/articles/10.3389/fmolb.2019.00117
Allison JR (2020) Computational methods for exploring protein conformations. Biochem Soc Trans 48(4):1707–1724. https://doi.org/10.1042/BST20200193
Ma J, Sigler PB, Xu Z, Karplus M (2000) A dynamic model for the allosteric mechanism of GroEL11Edited by A. Fersht J Mol Biol 302(2):303–313. https://www.sciencedirect.com/science/article/pii/S0022283600940142
Li J, Shaikh SA, Enkavi G, Wen PC, Huang Z, Tajkhorshid E (2013) Transient formation of water-conducting states in membrane transporters. PNAS 110(19):7696–7701. https://doi.org/10.1073/pnas.1218986110
Orellana L, Yoluk O, Carrillo O, Orozco M, Lindahl E (2016) Prediction and validation of protein intermediate states from structurally rich ensembles and coarse-grained simulations. Nat Commun 7(1):12575. https://doi.org/10.1038/ncomms12575
Marrink SJ, Monticelli L, Melo MN, Alessandri R, Tieleman DP, Souza PCT (2022) Two decades of Martini: better beads, broader scope. WIREs Comput Mol Sci 13(1):e1620. https://doi.org/10.1002/wcms.1620
Zerze GH, Zheng W, Best RB, Mittal J (2019) Evolution of all-atom protein force fields to improve local and global properties. J Phys Chem Lett 10(9):2227–2234. https://doi.org/10.1021/acs.jpclett.9b00850
Hopkins CW, le Grand S, Walker RC, Roitberg AE (2015) Long-time-step molecular dynamics through hydrogen mass repartitioning. J Chem Theory Comput 11(4):1864–1874. https://doi.org/10.1021/ct5010406
Pawnikar S, Bhattarai A, Wang J, Miao Y (2022) Binding analysis using accelerated molecular dynamics simulations and future perspectives. Adv Appl Bioinform Chem 15:1–19. https://doi.org/10.2147/AABC.S247950
Augen J (2002) The evolving role of information technology in the drug discovery process. Drug Discov Today 7(5):315–323. https://www.sciencedirect.com/science/article/pii/S1359644602021736
Barducci A, Bussi G, Parrinello M (2008) Well-tempered Metadynamics: a smoothly converging and tunable free-energy method. Phys Rev Lett 100(2):20603. https://link.aps.org/doi/10.1103/PhysRevLett.100.020603
Duan L, Guo X, Cong Y, Feng G, Li Y, Zhang JZH (2019) Accelerated molecular dynamics simulation for helical proteins folding in explicit water. Front Chem 7:540. https://www.frontiersin.org/articles/10.3389/fchem.2019.00540
Fraccalvieri D, Pandini A, Stella F, Bonati L (2011) Conformational and functional analysis of molecular dynamics trajectories by Self-Organising Maps. BMC Bioinformatics 12(1):158. https://doi.org/10.1186/1471-2105-12-158
Mohd A, Mohammad T, Kumar V, Alajmi MF, Rehman MT, Hussain A et al (2019) Structural analysis and conformational dynamics of STN1 gene mutations involved in coat plus syndrome. Front Mol Biosci 6:41. https://www.frontiersin.org/articles/10.3389/fmolb.2019.00041
Chan WKB, DasGupta D, Carlson HA, Traynor JR (2021) Mixed-solvent molecular dynamics simulation-based discovery of a putative allosteric site on regulator of G protein signaling 4. J Comput Chem 42(30):2170–2180. https://doi.org/10.1002/jcc.26747
Tan YS, Reeks J, Brown CJ, Thean D, Ferrer Gago FJ, Yuen TY et al (2016) Benzene probes in molecular dynamics simulations reveal novel binding sites for ligand design. J Phys Chem Lett 7(17):3452–3457. https://doi.org/10.1021/acs.jpclett.6b01525
Prakash P, Hancock JF, Gorfe AA (2015) Binding hotspots on K-ras: consensus ligand binding sites and other reactive regions from probe-based molecular dynamics analysis. Proteins: Structure, Function, and Bioinformatics 83(5):898–909. https://doi.org/10.1002/prot.24786
Graham SE, Leja N, Carlson HA (2018) MixMD Probeview: robust binding site prediction from Cosolvent simulations. J Chem Inf Model 58(7):1426–1433. https://doi.org/10.1021/acs.jcim.8b00265
Sayyed-Ahmad A, Gorfe AA (2017) Mixed-probe simulation and probe-derived surface topography map analysis for ligand binding site identification. J Chem Theory Comput 13(4):1851–1861. https://doi.org/10.1021/acs.jctc.7b00130
Perez A, Morrone JA, Simmerling C, Dill KA (2016) Advances in free-energy-based simulations of protein folding and ligand binding. Curr Opin Struct Biol 36:25–31. https://www.sciencedirect.com/science/article/pii/S0959440X15001888
Kuntal BK, Aparoy P, Reddanna P (2010) EasyModeller: a graphical interface to MODELLER. BMC Res Notes 3(1):226. https://doi.org/10.1186/1756-0500-3-226
Leman JK, Weitzner BD, Lewis SM, Adolf-Bryfogle J, Alam N, Alford RF et al (2020) Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat Methods 17(7):665–680. https://doi.org/10.1038/s41592-020-0848-2
Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R et al (2018) SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46(W1):W296–W303. https://doi.org/10.1093/nar/gky427
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H et al (2000) The Protein Data Bank. Nucleic Acids Res 28(1):235–242. https://doi.org/10.1093/nar/28.1.235
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.2003
Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT 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
Eberhardt J, Santos-Martins D, Tillack AF, Forli S (2021) AutoDock Vina 1.2.0: new docking methods, expanded force field, and Python bindings. J Chem Inf Model 61(8):3891–3898. https://doi.org/10.1021/acs.jcim.1c00203
Molecular Operating Environment (MOE) (2022) 2022.02 Chemical Computing Group ULC, 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2022. Molecular Operating Environment (MOE), 202202 Chemical Computing Group ULC, 1010 Sherbooke St West, Suite #910, Montreal, QC, Canada, H3A 2R7
Wolf S, Lickert B, Bray S, Stock G (2020) Multisecond ligand dissociation dynamics from atomistic simulations. Nat Commun 11(1):2918. https://doi.org/10.1038/s41467-020-16655-1
Lindahl E, Hess B, van der Spoel D (2001) GROMACS 3.0: a package for molecular simulation and trajectory analysis. Molecular modeling annual 7(8):306–317. https://doi.org/10.1007/s008940100045
Phillips JC, Hardy DJ, Maia JDC, Stone JE, Ribeiro JV, Bernardi RC et al (2020) Scalable molecular dynamics on CPU and GPU architectures with NAMD. J Chem Phys 153(4):044130. https://doi.org/10.1063/5.0014475
Kappel K, Miao Y, McCammon JA (2015) Accelerated molecular dynamics simulations of ligand binding to a muscarinic G-protein-coupled receptor. Q Rev Biophys 48(4):479–487. https://doi.org/10.1017/S0033583515000153
Huang W, Manglik A, Venkatakrishnan AJ, Laeremans T, Feinberg EN, Sanborn AL et al (2015) Structural insights into μ-opioid receptor activation. Nature 524(7565):315–321. https://doi.org/10.1038/nature14886
Wacker D, Stevens RC, Roth BL (2017) How ligands illuminate GPCR molecular pharmacology. Cell 170(3):414–427. https://www.sciencedirect.com/science/article/pii/S0092867417308164
Wang L, Wu Y, Deng Y, Kim B, Pierce L, Krilov G et al (2015) Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J Am Chem Soc 25;137(7):2695–2703. https://doi.org/10.1021/ja512751q
Jin Y, Johannissen LO, Hay S (2021) Predicting new protein conformations from molecular dynamics simulation conformational landscapes and machine learning. Proteins 89(8):915–921. https://doi.org/10.1002/prot.26068
Hall BA, Kaye SL, Pang A, Perera R, Biggin PC (2007) Characterization of protein conformational states by normal-mode frequencies. J Am Chem Soc 129(37):11394–11401. https://doi.org/10.1021/ja071797y
Gur M, Blackburn EA, Ning J, Narayan V, Ball KL, Walkinshaw MD et al (2018) Molecular dynamics simulations of site point mutations in the TPR domain of cyclophilin 40 identify conformational states with distinct dynamic and enzymatic properties. J Chem Phys 148(14):145101. https://doi.org/10.1063/1.5019457
Ahmed M, Barakat K (2017) The too many faces of PD-L1: a comprehensive conformational analysis study. Biochemistry 56(40):5428–5439. https://doi.org/10.1021/acs.biochem.7b00655
Holyoake J, Sansom MSP (2007) Conformational change in an MFS protein: MD simulations of LacY. Structure 15(7):873–884. https://www.sciencedirect.com/science/article/pii/S0969212607002092
Ghattas MA, al Rawashdeh S, Atatreh N, Bryce RA (2020) How do small molecule aggregates inhibit enzyme activity? A molecular dynamics study. J Chem Inf Model 60(8):3901–3909. https://doi.org/10.1021/acs.jcim.0c00540
Amine K, Miri L, Naimi A, Saile R, Kharrim AEL, Mikou A et al (2015) Molecular dynamics approach in the comparison of wild-type and mutant Paraoxonase-1 Apoenzyme form. Bioinform Biol Insights 9:BBI.S25626. https://doi.org/10.4137/BBI.S25626
Panchal NK, Bhale A, Verma VK, Beevi SS (2020) Computational and molecular dynamics simulation approach to analyze the impact of XPD gene mutation on protein stability and function. bioRxiv 2020.07.18.209841.: http://biorxiv.org/content/early/2020/07/18/2020.07.18.209841.abstract
Hirano Y, Okimoto N, Fujita S, Taiji M (2021) Molecular dynamics study of conformational changes of Tankyrase 2 binding subsites upon ligand binding. ACS Omega 6(27):17609–17620. https://doi.org/10.1021/acsomega.1c02159
Audagnotto M, Czechtizky W, de Maria L, Käck H, Papoian G, Tornberg L et al (2022) Machine learning/molecular dynamic protein structure prediction approach to investigate the protein conformational ensemble. Sci Rep 12(1):10018. https://doi.org/10.1038/s41598-022-13714-z
Gedeon PC, Thomas JR, Madura JD (2015) Accelerated molecular dynamics and protein conformational change: a theoretical and practical guide using a membrane embedded model neurotransmitter transporter. In: Kukol A (ed) Molecular modeling of proteins. Springer New York, New York, pp 253–287. https://doi.org/10.1007/978-1-4939-1465-4_12
Bhattarai A, Pawnikar S, Miao Y (2021) Mechanism of ligand recognition by human ACE2 receptor. J Phys Chem Lett 12(20):4814–4822. https://doi.org/10.1021/acs.jpclett.1c01064
Laio A, Gervasio FL (2008) Metadynamics: a method to simulate rare events and reconstruct the free energy in biophysics, chemistry and material science. Rep Prog Phys 71(12):126601. https://doi.org/10.1088/0034-4885/71/12/126601
Bešker N, Gervasio FL (2012) Using metadynamics and path collective variables to study ligand binding and induced conformational transitions. In: Baron R (ed) Computational drug discovery and design. Springer New York, New York, pp 501–513. https://doi.org/10.1007/978-1-61779-465-0_29
Provasi D, Bortolato A, Filizola M (2009) Exploring molecular mechanisms of ligand recognition by opioid receptors with Metadynamics. Biochemistry 48(42):10020–10029. https://doi.org/10.1021/bi901494n
Casasnovas R, Limongelli V, Tiwary P, Carloni P, Parrinello M (2017) Unbinding kinetics of a p38 MAP kinase type II inhibitor from metadynamics simulations. J Am Chem Soc 139(13):4780–4788. https://doi.org/10.1021/jacs.6b12950
Torrie GM, Valleau JP (1977) Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling. J Comput Phys 23(2):187–199. https://www.sciencedirect.com/science/article/pii/0021999177901218
Darve E, Rodríguez-Gómez D, Pohorille A (2008) Adaptive biasing force method for scalar and vector free energy calculations. J Chem Phys 128(14):144120. https://doi.org/10.1063/1.2829861
Kalyaanamoorthy S, Lamothe SM, Hou X, Moon TC, Kurata HT, Houghton M et al (2020) A structure-based computational workflow to predict liability and binding modes of small molecules to hERG. Sci Rep 10(1):16262. https://doi.org/10.1038/s41598-020-72889-5
Souza PCT, Alessandri R, Barnoud J, Thallmair S, Faustino I, Grünewald F et al (2021) Martini 3: a general purpose force field for coarse-grained molecular dynamics. Nat Methods 18(4):382–388. https://doi.org/10.1038/s41592-021-01098-3
Cellmer T, Fawzi NL (2012) Coarse-grained simulations of protein aggregation. In: Voynov V, Caravella JA (eds) Therapeutic proteins: methods and protocols [Internet]. Humana Press, Totowa, pp 453–470. https://doi.org/10.1007/978-1-61779-921-1_27
Peroukidis SD, Stott IP, Mavrantzas VG (2022) Coarse-grained model incorporating short- and long-range effective potentials for the fast simulation of micelle formation in solutions of ionic surfactants. J Phys Chem B 126(29):5555–5569. https://doi.org/10.1021/acs.jpcb.2c02751
Frallicciardi J, Melcr J, Siginou P, Marrink SJ, Poolman B (2022) Membrane thickness, lipid phase and sterol type are determining factors in the permeability of membranes to small solutes. Nat Commun 13(1):1605. https://doi.org/10.1038/s41467-022-29272-x
Hoffmann C, Centi A, Menichetti R, Bereau T (2020) Molecular dynamics trajectories for 630 coarse-grained drug-membrane permeations. Sci Data 7(1):51. https://doi.org/10.1038/s41597-020-0391-0
Patmanidis I, Souza PCT, Sami S, Havenith RWA, de Vries AH, Marrink SJ (2022) Modelling structural properties of cyanine dye nanotubes at coarse-grained level. Nanoscale Adv 4(14):3033–3042. https://doi.org/10.1039/D2NA00158F
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
AlRawashdeh, S., Barakat, K.H. (2024). Applications of Molecular Dynamics Simulations in Drug Discovery. In: Gore, M., Jagtap, U.B. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 2714. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3441-7_7
Download citation
DOI: https://doi.org/10.1007/978-1-0716-3441-7_7
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-3440-0
Online ISBN: 978-1-0716-3441-7
eBook Packages: Springer Protocols