Generic placeholder image

Current Pharmaceutical Design

Editor-in-Chief

ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

Review Article

Use of Molecular Dynamics Simulations in Structure-Based Drug Discovery

Author(s): Indrani Bera* and Pavan V. Payghan

Volume 25, Issue 31, 2019

Page: [3339 - 3349] Pages: 11

DOI: 10.2174/1381612825666190903153043

Price: $65

Abstract

Background: Traditional drug discovery is a lengthy process which involves a huge amount of resources. Modern-day drug discovers various multidisciplinary approaches amongst which, computational ligand and structure-based drug designing methods contribute significantly. Structure-based drug designing techniques require the knowledge of structural information of drug target and drug-target complexes. Proper understanding of drug-target binding requires the flexibility of both ligand and receptor to be incorporated. Molecular docking refers to the static picture of the drug-target complex(es). Molecular dynamics, on the other hand, introduces flexibility to understand the drug binding process.

Objective: The aim of the present study is to provide a systematic review on the usage of molecular dynamics simulations to aid the process of structure-based drug design.

Method: This review discussed findings from various research articles and review papers on the use of molecular dynamics in drug discovery. All efforts highlight the practical grounds for which molecular dynamics simulations are used in drug designing program. In summary, various aspects of the use of molecular dynamics simulations that underline the basis of studying drug-target complexes were thoroughly explained.

Results: This review is the result of reviewing more than a hundred papers. It summarizes various problems that use molecular dynamics simulations.

Conclusion: The findings of this review highlight how molecular dynamics simulations have been successfully implemented to study the structure-function details of specific drug-target complexes. It also identifies the key areas such as stability of drug-target complexes, ligand binding kinetics and identification of allosteric sites which have been elucidated using molecular dynamics simulations.

Keywords: Molecular dynamics, drug design, ligand binding, receptor flexibility, allosteric sites, receptor modulation.

[1]
Fischer M, Coleman RG, Fraser JS, Shoichet BK. Incorporation of protein flexibility and conformational energy penalties in docking screens to improve ligand discovery. Nat Chem 2014; 6(7): 575-83.
[http://dx.doi.org/10.1038/nchem.1954] [PMID: 24950326]
[2]
Jorgensen WL. The many roles of computation in drug discovery. Science 2004; 303(5665): 1813-8.
[http://dx.doi.org/10.1126/science.1096361] [PMID: 15031495]
[3]
Boehr DD, Nussinov R, Wright PE. The role of dynamic conformational ensembles in biomolecular recognition. Nat Chem Biol 2009; 5(11): 789-96.
[http://dx.doi.org/10.1038/nchembio.232] [PMID: 19841628]
[4]
Borhani DW, Shaw DE. The future of molecular dynamics simulations in drug discovery. J Comput Aided Mol Des 2012; 26(1): 15-26.
[http://dx.doi.org/10.1007/s10822-011-9517-y] [PMID: 22183577]
[5]
Carlson HA. Protein flexibility and drug design: how to hit a moving target. Curr Opin Chem Biol 2002; 6(4): 447-52.
[http://dx.doi.org/10.1016/S1367-5931(02)00341-1] [PMID: 12133719]
[6]
Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. Br J Pharmacol 2011; 162(6): 1239-49.
[http://dx.doi.org/10.1111/j.1476-5381.2010.01127.x] [PMID: 21091654]
[7]
Totrov M, Abagyan R. Flexible ligand docking to multiple receptor conformations: a practical alternative. Curr Opin Struct Biol 2008; 18(2): 178-84.
[http://dx.doi.org/10.1016/j.sbi.2008.01.004] [PMID: 18302984]
[8]
Bottegoni G, Kufareva I, Totrov M, Abagyan R. Four-dimensional docking: a fast and accurate account of discrete receptor flexibility in ligand docking. J Med Chem 2009; 52(2): 397-406.
[http://dx.doi.org/10.1021/jm8009958] [PMID: 19090659]
[9]
Huang SY, Zou X. Ensemble docking of multiple protein structures: considering protein structural variations in molecular docking. Proteins 2007; 66(2): 399-421.
[http://dx.doi.org/10.1002/prot.21214] [PMID: 17096427]
[10]
Korb O, Olsson TS, Bowden SJ, et al. Potential and limitations of ensemble docking. J Chem Inf Model 2012; 52(5): 1262-74.
[http://dx.doi.org/10.1021/ci2005934] [PMID: 22482774]
[11]
Nandi S, Bagchi MC. 3D-QSAR and molecular docking studies of 4-anilinoquinazoline derivatives: a rational approach to anticancer drug design. Mol Divers 2010; 14(1): 27-38.
[http://dx.doi.org/10.1007/s11030-009-9137-9] [PMID: 19330460]
[12]
Nandi S, Bagchi MC. In silico design of potent EGFR kinase inhibitors using combinatorial libraries. Mol Simul 2011; 37: 196-209.
[http://dx.doi.org/10.1080/08927022.2010.536542]
[13]
Nandi S, Ahmed S, Saxena AK. Combinatorial design and virtual screening of potent anti-tubercular fluoroquinolone and isothiazoloquinolone compounds utilizing QSAR and pharmacophore modelling. SAR QSAR Environ Res 2018; 29(2): 151-70.
[http://dx.doi.org/10.1080/1062936X.2017.1419375] [PMID: 29347843]
[14]
Nandi S, Kaur R, Kumar M, Sharma A, Naaz A, Mandal SC. Current breakthroughs in structure-based design of synthetic and natural sourced inhibitors against zika viral targets. Curr Top Med Chem 2018; 18(20): 1792-803.
[http://dx.doi.org/10.2174/1568026619666181120125525] [PMID: 30465510]
[15]
Pang YP, Kozikowski AP. Prediction of the binding sites of huperzine A in acetylcholinesterase by docking studies. J Comput Aided Mol Des 1994; 8(6): 669-81.
[http://dx.doi.org/10.1007/BF00124014] [PMID: 7738603]
[16]
Jorgensen WL, Ravimohan C. Monte Carlo simulation of differences in free energies of hydration. J Chem Phys 1985; 83: 3050-4.
[http://dx.doi.org/10.1063/1.449208]
[17]
Jorgensen WL, Thomas LL. Perspective on free-energy perturbation calculations for chemical equilibria. J Chem Theory Comput 2008; 4(6): 869-76.
[http://dx.doi.org/10.1021/ct800011m] [PMID: 19936324]
[18]
Torrie GM, Valleau JP. Nonphysical sampling distributions in Monte Carlo free-energy estimation: umbrella sampling. J Comput Phys 1977; 23: 187-99.
[http://dx.doi.org/10.1016/0021-9991(77)90121-8]
[19]
Isralewitz B, Gao M, Schulten K. Steered molecular dynamics and mechanical functions of proteins. Curr Opin Struct Biol 2001; 11(2): 224-30.
[http://dx.doi.org/10.1016/S0959-440X(00)00194-9] [PMID: 11297932]
[20]
Sugita Y, Okamoto Y. Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 1999; 314: 141-51.
[http://dx.doi.org/10.1016/S0009-2614(99)01123-9]
[21]
Hamelberg D, Mongan J, McCammon JA. Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. J Chem Phys 2004; 120(24): 11919-29.
[http://dx.doi.org/10.1063/1.1755656] [PMID: 15268227]
[22]
Tsai CJ, Del Sol A, Nussinov R. Protein allostery, signal transmission and dynamics: a classification scheme of allosteric mechanisms. Mol Biosyst 2009; 5(3): 207-16.
[http://dx.doi.org/10.1039/b819720b] [PMID: 19225609]
[23]
Motlagh HN, Wrabl JO, Li J, Hilser VJ. The ensemble nature of allostery. Nature 2014; 508(7496): 331-9.
[http://dx.doi.org/10.1038/nature13001] [PMID: 24740064]
[24]
Hilser VJ, Wrabl JO, Motlagh HN. Structural and energetic basis of allostery. Annu Rev Biophys 2012; 41: 585-609.
[http://dx.doi.org/10.1146/annurev-biophys-050511-102319] [PMID: 22577828]
[25]
Nussinov R, Tsai CJ. The different ways through which specificity works in orthosteric and allosteric drugs. Curr Pharm Des 2012; 18(9): 1311-6.
[http://dx.doi.org/10.2174/138161212799436377] [PMID: 22316155]
[26]
Ma B, Nussinov R. Druggable orthosteric and allosteric hot spots to target protein-protein interactions. Curr Pharm Des 2014; 20(8): 1293-301.
[http://dx.doi.org/10.2174/13816128113199990073] [PMID: 23713780]
[27]
Gunasekaran K, Ma B, Nussinov R. Is allostery an intrinsic property of all dynamic proteins? Proteins 2004; 57(3): 433-43.
[http://dx.doi.org/10.1002/prot.20232] [PMID: 15382234]
[28]
Kumar S, Ma B, Tsai CJ, Sinha N, Nussinov R. Folding and binding cascades: dynamic landscapes and population shifts. Protein Sci 2000; 9(1): 10-9.
[http://dx.doi.org/10.1110/ps.9.1.10] [PMID: 10739242]
[29]
Kern D, Zuiderweg ER. The role of dynamics in allosteric regulation. Curr Opin Struct Biol 2003; 13(6): 748-57.
[http://dx.doi.org/10.1016/j.sbi.2003.10.008] [PMID: 14675554]
[30]
Boehr DD, Nussinov R, Wright PE. The role of dynamic conformational ensembles in biomolecular recognition. Nat Chem Biol 2009; 5(11): 789-96.
[http://dx.doi.org/10.1038/nchembio.232] [PMID: 19841628]
[31]
Kühlbrandt W. Biochemistry. The resolution revolution. Science 2014; 343(6178): 1443-4.
[http://dx.doi.org/10.1126/science.1251652] [PMID: 24675944]
[32]
De Vivo M, Masetti M, Bottegoni G, Cavalli A. Role of molecular dynamics and related methods in drug discovery. J Med Chem 2016; 59(9): 4035-61.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01684] [PMID: 26807648]
[33]
Vijayan RS, Trivedi N, Roy SN, et al. Modeling the closed and open state conformations of the GABA(A) ion channel--plausible structural insights for channel gating. J Chem Inf Model 2012; 52(11): 2958-69.
[http://dx.doi.org/10.1021/ci300189a] [PMID: 23116339]
[34]
Chupakhin VI, Palyulin VA, Zefirov NS. Modeling the open and closed forms of GABAA receptor: analysis of ligand-receptor interactions for the GABA-binding site. Dokl Biochem Biophys 2006; 408: 169-74.
[http://dx.doi.org/10.1134/S1607672906030173] [PMID: 16913422]
[35]
Bergmann R, Kongsbak K, Sørensen PL, Sander T, Balle T. A unified model of the GABA(A) receptor comprising agonist and benzodiazepine binding sites. PLoS One 2013; 8(1)e52323
[http://dx.doi.org/10.1371/journal.pone.0052323] [PMID: 23308109]
[36]
Payghan PV, Nath Roy S, Bhattacharyya D, Ghoshal N. Cross-talk between allosteric and orthosteric binding sites of γ-amino butyric acid type A receptors (GABAA-Rs): a computational study revealing the structural basis of selectivity. J Biomol Struct Dyn 2019; 37(12): 3065-80.
[http://dx.doi.org/10.1080/07391102.2018.1508367] [PMID: 30608219]
[37]
Cromer BA, Morton CJ, Parker MW. Anxiety over GABA(A) receptor structure relieved by AChBP. Trends Biochem Sci 2002; 27(6): 280-7.
[http://dx.doi.org/10.1016/S0968-0004(02)02092-3] [PMID: 12069787]
[38]
Henderson R, Baldwin JM, Ceska TA, Zemlin F, Beckmann E, Downing KH. Model for the structure of bacteriorhodopsin based on high-resolution electron cryo-microscopy. J Mol Biol 1990; 213(4): 899-929.
[http://dx.doi.org/10.1016/S0022-2836(05)80271-2] [PMID: 2359127]
[39]
Niv MY, Skrabanek L, Filizola M, Weinstein H. Modeling activated states of GPCRs: the rhodopsin template. J Comput Aided Mol Des 2006; 20(7-8): 437-48.
[http://dx.doi.org/10.1007/s10822-006-9061-3] [PMID: 17103019]
[40]
Bera I, Laskar A, Ghoshal N. Exploring the structure of opioid receptors with homology modeling based on single and multiple templates and subsequent docking: a comparative study. J Mol Model 2011; 17(5): 1207-21.
[http://dx.doi.org/10.1007/s00894-010-0803-8] [PMID: 20661609]
[41]
Strahs D, Weinstein H. Comparative modeling and molecular dynamics studies of the delta, kappa and mu opioid receptors. Protein Eng 1997; 10(9): 1019-38.
[http://dx.doi.org/10.1093/protein/10.9.1019] [PMID: 9464566]
[42]
Aburi M, Smith PE. Modeling and simulation of the human δ opioid receptor. Protein Sci 2004; 13(8): 1997-2008.
[http://dx.doi.org/10.1110/ps.04720304] [PMID: 15238638]
[43]
Payghan PV, Bera I, Bhattacharyya D, Ghoshal N. Capturing state-dependent dynamic events of GABAA-receptors: a microscopic look into the structural and functional insights. J Biomol Struct Dyn 2016; 34(8): 1818-37.
[http://dx.doi.org/10.1080/07391102.2015.1094410] [PMID: 26372345]
[44]
Hess B, Kutzner C, van der Spoel D, Lindahl E. GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 2008; 4(3): 435-47.
[http://dx.doi.org/10.1021/ct700301q] [PMID: 26620784]
[45]
Nosé S. A molecular dynamics method for simulations in the canonical ensemble. Mol Phys 1984; 52: 255-68.
[http://dx.doi.org/10.1080/00268978400101201]
[46]
Parrinello M, Rahman A. Polymorphic transitions in single crystals: a new molecular dynamics method. J Appl Phys 1981; 52: 7182-90.
[http://dx.doi.org/10.1063/1.328693]
[47]
Bera I, Marathe MV, Payghan PV, Ghoshal N. Identification of novel hits as highly prospective dual agonists for mu and kappa opioid receptors: an integrated in silico approach. J Biomol Struct Dyn 2018; 36(2): 279-301.
[http://dx.doi.org/10.1080/07391102.2016.1275810] [PMID: 28071341]
[48]
Gordo S, Martos V, Santos E, et al. Stability and structural recovery of the tetramerization domain of p53-R337H mutant induced by a designed templating ligand. Proc Natl Acad Sci USA 2008; 105(43): 16426-31.
[http://dx.doi.org/10.1073/pnas.0805658105] [PMID: 18940924]
[49]
Plattner N, Noé F. Protein conformational plasticity and complex ligand-binding kinetics explored by atomistic simulations and Markov models. Nat Commun 2015; 6: 7653.
[http://dx.doi.org/10.1038/ncomms8653] [PMID: 26134632]
[50]
Blondel A, Renaud JP, Fischer S, Moras D, Karplus M. Retinoic acid receptor: a simulation analysis of retinoic acid binding and the resulting conformational changes. J Mol Biol 1999; 291(1): 101-15.
[http://dx.doi.org/10.1006/jmbi.1999.2879] [PMID: 10438609]
[51]
Martínez L, Sonoda MT, Webb P, Baxter JD, Skaf MS, Polikarpov I. Molecular dynamics simulations reveal multiple pathways of ligand dissociation from thyroid hormone receptors. Biophys J 2005; 89(3): 2011-23.
[http://dx.doi.org/10.1529/biophysj.105.063818] [PMID: 15980170]
[52]
Valley CC, Cembran A, Perlmutter JD, et al. The methionine-aromatic motif plays a unique role in stabilizing protein structure. J Biol Chem 2012; 287(42): 34979-91.
[http://dx.doi.org/10.1074/jbc.M112.374504] [PMID: 22859300]
[53]
Dagliyan O, Shirvanyants D, Karginov AV, et al. Rational design of a ligand-controlled protein conformational switch. Proc Natl Acad Sci USA 2013; 110(17): 6800-4.
[http://dx.doi.org/10.1073/pnas.1218319110] [PMID: 23569285]
[54]
Young T, Abel R, Kim B, Berne BJ, Friesner RA. Motifs for molecular recognition exploiting hydrophobic enclosure in protein-ligand binding. Proc Natl Acad Sci USA 2007; 104(3): 808-13.
[http://dx.doi.org/10.1073/pnas.0610202104] [PMID: 17204562]
[55]
Patel JS, Berteotti A, Ronsisvalle S, Rocchia W, Cavalli A. Steered molecular dynamics simulations for studying protein-ligand interaction in cyclin-dependent kinase 5. J Chem Inf Model 2014; 54(2): 470-80.
[http://dx.doi.org/10.1021/ci4003574] [PMID: 24437446]
[56]
Xu Y, Shen J, Luo X, et al. How does huperzine A enter and leave the binding gorge of acetylcholinesterase? Steered molecular dynamics simulations. J Am Chem Soc 2003; 125(37): 11340-9.
[http://dx.doi.org/10.1021/ja029775t] [PMID: 16220957]
[57]
Colizzi F, Perozzo R, Scapozza L, Recanatini M, Cavalli A. Single-molecule pulling simulations can discern active from inactive enzyme inhibitors. J Am Chem Soc 2010; 132(21): 7361-71.
[http://dx.doi.org/10.1021/ja100259r] [PMID: 20462212]
[58]
Mai BK, Viet MH, Li MS. Top leads for swine influenza A/H1N1 virus revealed by steered molecular dynamics approach. J Chem Inf Model 2010; 50(12): 2236-47.
[http://dx.doi.org/10.1021/ci100346s] [PMID: 21090736]
[59]
Mai BK, Li MS. Neuraminidase inhibitor R-125489--a promising drug for treating influenza virus: steered molecular dynamics approach. Biochem Biophys Res Commun 2011; 410(3): 688-91.
[http://dx.doi.org/10.1016/j.bbrc.2011.06.057] [PMID: 21693105]
[60]
Khalili-Araghi F, Gumbart J, Wen PC, Sotomayor M, Tajkhorshid E, Schulten K. Molecular dynamics simulations of membrane channels and transporters. Curr Opin Struct Biol 2009; 19(2): 128-37.
[http://dx.doi.org/10.1016/j.sbi.2009.02.011] [PMID: 19345092]
[61]
Hub JS, de Groot BL. Mechanism of selectivity in aquaporins and aquaglyceroporins. Proc Natl Acad Sci USA 2008; 105(4): 1198-203.
[http://dx.doi.org/10.1073/pnas.0707662104] [PMID: 18202181]
[62]
Noskov SY, Roux B. Importance of hydration and dynamics on the selectivity of the KcsA and NaK channels. J Gen Physiol 2007; 129(2): 135-43.
[http://dx.doi.org/10.1085/jgp.200609633] [PMID: 17227917]
[63]
Fowler PW, Tai K, Sansom MS. The selectivity of K+ ion channels: testing the hypotheses. Biophys J 2008; 95(11): 5062-72.
[http://dx.doi.org/10.1529/biophysj.108.132035] [PMID: 18790851]
[64]
Yefimov S, van der Giessen E, Onck PR, Marrink SJ. Mechanosensitive membrane channels in action. Biophys J 2008; 94(8): 2994-3002.
[http://dx.doi.org/10.1529/biophysj.107.119966] [PMID: 18192351]
[65]
Wen PC, Tajkhorshid E. Dimer opening of the nucleotide binding domains of ABC transporters after ATP hydrolysis. Biophys J 2008; 95(11): 5100-10.
[http://dx.doi.org/10.1529/biophysj.108.139444] [PMID: 18790847]
[66]
Ivetac A, Campbell JD, Sansom MS. Dynamics and function in a bacterial ABC transporter: simulation studies of the BtuCDF system and its components. Biochemistry 2007; 46(10): 2767-78.
[http://dx.doi.org/10.1021/bi0622571] [PMID: 17302441]
[67]
Yin Y, Jensen MØ, Tajkhorshid E, Schulten K. Sugar binding and protein conformational changes in lactose permease. Biophys J 2006; 91(11): 3972-85.
[http://dx.doi.org/10.1529/biophysj.106.085993] [PMID: 16963502]
[68]
Bera I, Klauda JB. Structural events in a bacterial uniporter leading to translocation of glucose to the cytosol. J Mol Biol 2018; 430(18 Pt B): 3337-52.
[http://dx.doi.org/10.1016/j.jmb.2018.06.021] [PMID: 29913162]
[69]
Langley JN. On the reaction of cells and of nerve-endings to certain poisons, chiefly as regards the reaction of striated muscle to nicotine and to curari. J Physiol 1905; 33(4-5): 374-413.
[http://dx.doi.org/10.1113/jphysiol.1905.sp001128] [PMID: 16992819]
[70]
Copeland RA, Pompliano DL, Meek TD. Drug-target residence time and its implications for lead optimization. Nat Rev Drug Discov 2006; 5(9): 730-9.
[http://dx.doi.org/10.1038/nrd2082] [PMID: 16888652]
[71]
Lu H, Tonge PJ. Drug-target residence time: critical information for lead optimization. Curr Opin Chem Biol 2010; 14(4): 467-74.
[http://dx.doi.org/10.1016/j.cbpa.2010.06.176] [PMID: 20663707]
[72]
Folmer RHA. Drug target residence time: a misleading concept. Drug Discov Today 2018; 23(1): 12-6.
[http://dx.doi.org/10.1016/j.drudis.2017.07.016] [PMID: 28782685]
[73]
Laio A, Parrinello M. Escaping free-energy minima. Proc Natl Acad Sci USA 2002; 99(20): 12562-6.
[http://dx.doi.org/10.1073/pnas.202427399] [PMID: 12271136]
[74]
Patey GN, Valleau JP. A Monte Carlo method for obtaining the interionic potential of mean force in ionic solution. J Chem Phys 1975; 63: 2334-9.
[http://dx.doi.org/10.1063/1.431685]
[75]
Sugita Y, Okamoto Y. Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 1999; 314: 141-51.
[http://dx.doi.org/10.1016/S0009-2614(99)01123-9]
[76]
Hamelberg D, Mongan J, McCammon JA. Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. J Chem Phys 2004; 120(24): 11919-29.
[http://dx.doi.org/10.1063/1.1755656] [PMID: 15268227]
[77]
Lane TJ, Bowman GR, Beauchamp K, Voelz VA, Pande VS. Markov state model reveals folding and functional dynamics in ultra-long MD trajectories. J Am Chem Soc 2011; 133(45): 18413-9.
[http://dx.doi.org/10.1021/ja207470h] [PMID: 21988563]
[78]
Shukla D, Meng Y, Roux B, Pande VS. Activation pathway of Src kinase reveals intermediate states as targets for drug design. Nat Commun 2014; 5: 3397.
[http://dx.doi.org/10.1038/ncomms4397] [PMID: 24584478]
[79]
Zeller F, Luitz MP, Bomblies R, Zacharias M. Multiscale simulation of receptor-drug association kinetics: application to neuraminidase inhibitors. J Chem Theory Comput 2017; 13(10): 5097-105.
[http://dx.doi.org/10.1021/acs.jctc.7b00631] [PMID: 28820938]
[80]
Ermak DL, McCammon JA. Brownian dynamics with hydrodynamic interactions. J Chem Phys 1978; 69: 1352-60.
[http://dx.doi.org/10.1063/1.436761]
[81]
Zhou HX. Brownian dynamics study of the influences of electrostatic interaction and diffusion on protein-protein association kinetics. Biophys J 1993; 64(6): 1711-26.
[http://dx.doi.org/10.1016/S0006-3495(93)81543-1] [PMID: 8396447]
[82]
Northrup SH, Allison SA, McCammon JA. Brownian dynamics simulation of diffusion-influenced bimolecular reactions. J Chem Phys 1984; 80: 1517-24.
[http://dx.doi.org/10.1063/1.446900]
[83]
Wade RC, Luty BA, Demchuk E, et al. Simulation of enzyme-substrate encounter with gated active sites. Nat Struct Biol 1994; 1(1): 65-9.
[http://dx.doi.org/10.1038/nsb0194-65] [PMID: 7656010]
[84]
Tiwary P. Molecular determinants and bottlenecks in the dissociation dynamics of biotin-streptavidin. J Phys Chem B 2017; 121(48): 10841-9.
[http://dx.doi.org/10.1021/acs.jpcb.7b09510] [PMID: 29117680]
[85]
Tiwary P, Parrinello M. From metadynamics to dynamics. Phys Rev Lett 2013; 111(23)230602
[http://dx.doi.org/10.1103/PhysRevLett.111.230602] [PMID: 24476246]
[86]
Valsson O, Tiwary P, Parrinello M. Enhancing important fluctuations: Rare events and metadynamics from a conceptual viewpoint. Annu Rev Phys Chem 2016; 67: 159-84.
[http://dx.doi.org/10.1146/annurev-physchem-040215-112229] [PMID: 26980304]
[87]
Marino KA, Filizola M. Investigating small-molecule ligand binding to G protein-coupled receptors with biased or unbiased molecular dynamics simulations Computational Methods for GPCR Drug Discovery. New York, NY: Humana Press 2018; pp. 351-64.
[http://dx.doi.org/10.1007/978-1-4939-7465-8_17]
[88]
Copeland RA. The dynamics of drug-target interactions: drug-target residence time and its impact on efficacy and safety. Expert Opin Drug Discov 2010; 5(4): 305-10.
[http://dx.doi.org/10.1517/17460441003677725] [PMID: 22823083]
[89]
Schuetz DA, de Witte WEA, Wong YC, et al. Kinetics for drug discovery: an industry-driven effort to target drug residence time. Drug Discov Today 2017; 22(6): 896-911.
[http://dx.doi.org/10.1016/j.drudis.2017.02.002] [PMID: 28412474]
[90]
Tummino PJ, Copeland RA. Residence time of receptor-ligand complexes and its effect on biological function. Biochemistry 2008; 47(20): 5481-92.
[http://dx.doi.org/10.1021/bi8002023] [PMID: 18412369]
[91]
Fu H, Gumbart JC, Chen H, Shao X, Cai W, Chipot C. BFEE: a user-friendly graphical interface facilitating absolute binding free-energy calculations. J Chem Inf Model 2018; 58(3): 556-60.
[http://dx.doi.org/10.1021/acs.jcim.7b00695] [PMID: 29405709]
[92]
Genheden S, Ryde U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 2015; 10(5): 449-61.
[http://dx.doi.org/10.1517/17460441.2015.1032936] [PMID: 25835573]
[93]
Wagner JR, Lee CT, Durrant JD, Malmstrom RD, Feher VA, Amaro RE. Emerging computational methods for the rational discovery of allosteric drugs. Chem Rev 2016; 116(11): 6370-90.
[http://dx.doi.org/10.1021/acs.chemrev.5b00631] [PMID: 27074285]
[94]
Grover AK. Use of allosteric targets in the discovery of safer drugs. Med Princ Pract 2013; 22(5): 418-26.
[http://dx.doi.org/10.1159/000350417] [PMID: 23711993]
[95]
Kenakin TP. Ligand detection in the allosteric world. J Biomol Screen 2010; 15(2): 119-30.
[http://dx.doi.org/10.1177/1087057109357789] [PMID: 20086210]
[96]
Nussinov R, Tsai CJ. The different ways through which specificity works in orthosteric and allosteric drugs. Curr Pharm Des 2012; 18(9): 1311-6.
[http://dx.doi.org/10.2174/138161212799436377] [PMID: 22316155]
[97]
Wenthur CJ, Gentry PR, Mathews TP, Lindsley CW. Drugs for allosteric sites on receptors. Annu Rev Pharmacol Toxicol 2014; 54: 165-84.
[http://dx.doi.org/10.1146/annurev-pharmtox-010611-134525] [PMID: 24111540]
[98]
Wood MR, Hopkins CR, Brogan JT, Conn PJ, Lindsley CW. “Molecular switches” on mGluR allosteric ligands that modulate modes of pharmacology. Biochemistry 2011; 50(13): 2403-10.
[http://dx.doi.org/10.1021/bi200129s] [PMID: 21341760]
[99]
Schueler-Furman O, Wodak SJ. Computational approaches to investigating allostery. Curr Opin Struct Biol 2016; 41: 159-71.
[http://dx.doi.org/10.1016/j.sbi.2016.06.017] [PMID: 27607077]
[100]
Lu S, Ji M, Ni D, Zhang J. Discovery of hidden allosteric sites as novel targets for allosteric drug design. Drug Discov Today 2018; 23(2): 359-65.
[http://dx.doi.org/10.1016/j.drudis.2017.10.001] [PMID: 29030241]
[101]
Durrant JD, McCammon JA. Molecular dynamics simulations and drug discovery. BMC Biol 2011; 9(1): 71.
[http://dx.doi.org/10.1186/1741-7007-9-71] [PMID: 22035460]
[102]
Wodak SJ, Paci E, Dokholyan NV, et al. Allostery in its many disguises: from theory to applications. Structure 2019; 27(4): 566-78.
[http://dx.doi.org/10.1016/j.str.2019.01.003] [PMID: 30744993]
[103]
Dror RO, Pan AC, Arlow DH, et al. Pathway and mechanism of drug binding to G-protein-coupled receptors. Proc Natl Acad Sci USA 2011; 108(32): 13118-23.
[http://dx.doi.org/10.1073/pnas.1104614108] [PMID: 21778406]
[104]
Baumann SW, Baur R, Sigel E. Individual properties of the two functional agonist sites in GABA(A) receptors. J Neurosci 2003; 23(35): 11158-66.
[http://dx.doi.org/10.1523/JNEUROSCI.23-35-11158.2003] [PMID: 14657175]
[105]
Mozrzymas JW, Barberis A, Mercik K, Zarnowska ED. Binding sites, singly bound states, and conformation coupling shape GABA-evoked currents. J Neurophysiol 2003; 89(2): 871-83.
[http://dx.doi.org/10.1152/jn.00951.2002] [PMID: 12574465]
[106]
Sriram K, Insel PA. G protein-coupled receptors as targets for approved drugs: how many targets and how many drugs? Mol Pharmacol 2018; 93(4): 251-8.
[http://dx.doi.org/10.1124/mol.117.111062] [PMID: 29298813]
[107]
Fredriksson R, Lagerström MC, Lundin LG, Schiöth HB. The G-protein-coupled receptors in the human genome form five main families. Phylogenetic analysis, paralogon groups, and fingerprints. Mol Pharmacol 2003; 63(6): 1256-72.
[http://dx.doi.org/10.1124/mol.63.6.1256] [PMID: 12761335]
[108]
Miao Y, Goldfeld DA, Moo EV, et al. Accelerated structure-based design of chemically diverse allosteric modulators of a muscarinic G protein-coupled receptor. Proc Natl Acad Sci USA 2016; 113(38): E5675-84.
[http://dx.doi.org/10.1073/pnas.1612353113] [PMID: 27601651]
[109]
Ahuja LG, Taylor SS, Kornev AP. Tuning the “violin” of protein kinases: the role of dynamics-based allostery. IUBMB Life 2019; 71(6): 685-96.
[http://dx.doi.org/10.1002/iub.2057] [PMID: 31063633]
[110]
Piao L, Chen Z, Li Q, et al. Molecular dynamics simulations of wild type and mutants of SAPAP in complexed with shank3. Int J Mol Sci 2019; 20(1) E224
[http://dx.doi.org/10.3390/ijms20010224] [PMID: 30626119]
[111]
Gur M, Blackburn EA, Ning J, et al. 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 2018; 148(14) 145101
[http://dx.doi.org/10.1063/1.5019457] [PMID: 29655319]
[112]
Abrusán G, Marsh JA. Ligand-binding-site structure shapes allosteric signal transduction and the evolution of allostery in protein complexes. Mol Biol Evol 2019; 36(8): 1711-27.
[http://dx.doi.org/10.1093/molbev/msz093] [PMID: 31004156]
[113]
Vesper MD, de Groot BL. Collective dynamics underlying allosteric transitions in hemoglobin. PLOS Comput Biol 2013; 9(9) e1003232
[http://dx.doi.org/10.1371/journal.pcbi.1003232] [PMID: 24068910]
[114]
Cavalli A, Carloni P, Recanatini M. Target-related applications of first principles quantum chemical methods in drug design. Chem Rev 2006; 106(9): 3497-519.
[http://dx.doi.org/10.1021/cr050579p] [PMID: 16967914]
[115]
Lv WL, Arnesano F, Carloni P, Natile G, Rossetti G. Effect of in vivo post-translational modifications of the HMGB1 protein upon binding to platinated DNA: a molecular simulation study. Nucleic Acids Res 2018; 46(22): 11687-97.
[http://dx.doi.org/10.1093/nar/gky1082] [PMID: 30407547]
[116]
Chiappori F, Mattiazzi L, Milanesi L, Merelli I. A novel molecular dynamics approach to evaluate the effect of phosphorylation on multimeric protein interface: the αB-Crystallin case study. BMC Bioinformatics 2016; 17(Suppl. 4): 57.
[http://dx.doi.org/10.1186/s12859-016-0909-9] [PMID: 26961246]
[117]
Margreitter C, Petrov D, Zagrovic B. Vienna-PTM web server: a toolkit for MD simulations of protein post-translational modifications. Nucleic Acids Res 2013; 41(Web Server issue): W422-6.
[http://dx.doi.org/10.1093/nar/gkt416] [PMID: 23703210]
[118]
Raha K, Peters MB, Wang B, et al. The role of quantum mechanics in structure-based drug design. Drug Discov Today 2007; 12(17-18): 725-31.
[http://dx.doi.org/10.1016/j.drudis.2007.07.006] [PMID: 17826685]
[119]
El Hage K, Hédin F, Gupta PK, Meuwly M, Karplus M. Valid molecular dynamics simulations of human hemoglobin require a surprisingly large box size. eLife 2018; 7 e35560
[http://dx.doi.org/10.7554/eLife.35560] [PMID: 29998846]
[120]
Payghan PV, Bera I, Bhattacharyya D, Ghoshal N. Computational studies for structure-based drug designing against transmembrane receptors: pLGICs and class A GPCRs. Front Phys 2018; 6: 52.
[http://dx.doi.org/10.3389/fphy.2018.00052]
[121]
Liu X, Shi D, Zhou S, Liu H, Liu H, Yao X. Molecular dynamics simulations and novel drug discovery. Expert Opin Drug Discov 2018; 13(1): 23-37.
[http://dx.doi.org/10.1080/17460441.2018.1403419] [PMID: 29139324]
[122]
Zhang C, Feng LJ, Huang Y, et al. Discovery of novel phosphodiesterase-2A inhibitors by structure-based virtual screening, structural optimization, and bioassay. J Chem Inf Model 2017; 57(2): 355-64.
[http://dx.doi.org/10.1021/acs.jcim.6b00551] [PMID: 28055196]
[123]
Hou T, McLaughlin WA, Wang W. Evaluating the potency of HIV-1 protease drugs to combat resistance. Proteins 2008; 71(3): 1163-74.
[http://dx.doi.org/10.1002/prot.21808] [PMID: 18004760]
[124]
Pan P, Li L, Li Y, Li D, Hou T. Insights into susceptibility of antiviral drugs against the E119G mutant of 2009 influenza A (H1N1) neuraminidase by molecular dynamics simulations and free energy calculations. Antiviral Res 2013; 100(2): 356-64.
[http://dx.doi.org/10.1016/j.antiviral.2013.09.006] [PMID: 24055835]
[125]
Woods CJ, Malaisree M, Pattarapongdilok N, Sompornpisut P, Hannongbua S, Mulholland AJ. Long time scale GPU dynamics reveal the mechanism of drug resistance of the dual mutant I223R/H275Y neuraminidase from H1N1-2009 influenza virus. Biochemistry 2012; 51(21): 4364-75.
[http://dx.doi.org/10.1021/bi300561n] [PMID: 22574858]
[126]
Vass M, Schmidt É, Horti F, Keserű GM. Virtual fragment screening on GPCRs: a case study on dopamine D3 and histamine H4 receptors. Eur J Med Chem 2014; 77: 38-46.
[http://dx.doi.org/10.1016/j.ejmech.2014.02.034] [PMID: 24607587]
[127]
Miao Y, Goldfeld DA, Moo EV, et al. Accelerated structure-based design of chemically diverse allosteric modulators of a muscarinic G protein-coupled receptor. Proc Natl Acad Sci USA 2016; 113(38): E5675-84.
[http://dx.doi.org/10.1073/pnas.1612353113] [PMID: 27601651]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy