Skip to main content
SpringerLink
Log in

Molecular Dynamics Simulation: Methods and Application

  • Chapter
  • First Online:
Frontiers in Protein Structure, Function, and Dynamics

Abstract

The complexity of the 3D structure of a protein is still challenging in the area of structural biology. Thermodynamics-based methods, including molecular dynamics (MD) simulations, enable our understanding of protein’s conformational detail at the atomic level. Proteins are flexible molecules. MD simulation provides information about the dynamic perturbations that occur in a protein or protein–ligand complex. As compared to docking, MD simulation also considers the physiological conditions such as temperature, pH, presence of water, ions, and other molecules of the system. The force field and the software packages are several choices involved in large-scale simulation, which analyze the single protein, protein–ligand, and protein–protein structure, respectively. In this chapter, we discuss all the approaches along with packages that cover the entire molecular-level to cellular-level features of proteins. MD simulation approaches are very useful in assessing the stability of a protein model or protein–ligand complex and mutational study as well.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Aamir M, Singh VK, Dubey MK, Meena M, Kashyap SP, Katari SK, Upadhyay RS, Umamaheswari A, Singh S (2018) In silico prediction, characterization, molecular docking, and dynamic studies on fungal SDRS as novel targets for searching potential fungicides against fusarium wilt in tomato. Front Pharmacol 9:1038

    CAS  PubMed  PubMed Central  Google Scholar 

  • Accelrys Software Inc (2012) Discovery studio, release 3.0. Accelrys Inc., San Diego, CA. www.accelrys.com

    Google Scholar 

  • Adcock SA, McCammon JA (2006) Molecular dynamics: survey of methods for simulating the activity of proteins. Chem Rev 106:1589–1615

    CAS  PubMed  PubMed Central  Google Scholar 

  • Alder BJ, Wainwright TE (1959) Studies in molecular dynamics I. General method. J Chem Phys 31:459–466

    CAS  Google Scholar 

  • Alder BJ, Wainwright TE (1960) Studies in molecular dynamics. II. Behavior of a small number of elastic spheres. J Chem Phys 33:1439–1451

    CAS  Google Scholar 

  • Bahn S, Jacobsen K (2002) An object-oriented scripting interface to a legacy electronic structure code. Comput Sci Eng 4:56–66

    CAS  Google Scholar 

  • Beck DA, Daggett V (2004) Methods for molecular dynamics simulations of protein folding/unfolding in solution. Methods 34:112–120

    CAS  PubMed  Google Scholar 

  • Bonomi M, Branduardi D, Bussi G, Camilloni C, Provasi D, Raiteri P, Donadio D, Marinelli F, Pietrucci F, Broglia RA, Parrinello M (2009) PLUMED: a portable plugin for free-energy calculations with molecular dynamics. Comput Phys Commun 180:1961–1972

    CAS  Google Scholar 

  • Brandsdal BO, Osterberg F, Almlöf M, Feierberg I, Luzhkov VB, Aqvist J (2003) Free energy calculations and ligand binding. Adv Protein Chem 66:123–158

    CAS  PubMed  Google Scholar 

  • Breda A, Valadares NF, de Souza ON, Garratt RC (2007) Protein structure, modelling and applications. In: Bioinformatics in tropical disease research: a practical and case-study approach. National Center for Biotechnology Information, Bethesda

    Google Scholar 

  • Brooks BR, Brooks CL III, Mackerell AD Jr, Nilsson L, Petrella RJ, Roux B, Won Y, Archontis G, Bartels C, Boresch S, Caflisch A (2009) CHARMM: the biomolecular simulation program. J Comput Chem 30:1545–1614

    CAS  PubMed  PubMed Central  Google Scholar 

  • Case DA, Cheatham TE III, Darden T, Gohlke H, Luo R, Merz KM Jr, Onufriev A, Simmerling C, Wang B, Woods RJ (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688

    CAS  PubMed  PubMed Central  Google Scholar 

  • Chipot C, Pohorille A (2007) Free energy calculations. In: Springer series in chemical physics. Springer, Berlin

    Google Scholar 

  • Chong SH, Ham S (2019) Folding free energy landscape of ordered and intrinsically disordered proteins. Sci Rep 9:1–9

    Google Scholar 

  • Collier TA, Piggot TJ, Allison JR (2020) Molecular dynamics simulation of proteins. Methods Mol Biol 2073:311–327

    CAS  PubMed  Google Scholar 

  • David CC, Jacobs DJ (2014) Principal component analysis: a method for determining the essential dynamics of proteins. Methods Mol Biol 1084:193–226

    CAS  PubMed  PubMed Central  Google Scholar 

  • David CC, Singam ERA, Jacobs DJ (2017) JED: a Java essential dynamics program for comparative analysis of protein trajectories. BMC Bioinf 18:271

    Google Scholar 

  • Desmond Molecular Dynamics System, version 3.1 (2012) DE Shaw Research, New York, Maestro–Desmond Interoperability Tools, version 3.1, 2012, Schrödinger, New York

    Google Scholar 

  • Djidjev HN, Hahn G, Mniszewski SM, Negre CFA, Niklasson AMN (2019) Using graph partitioning for scalable distributed quantum molecular dynamics. Algorithms 12:187

    Google Scholar 

  • Dolado JS, Griebel M, Hamaekers J, Heber F (2010) The nano-branched structure of cementitious calcium-silicate-hydrate gel. J Mater Chem A 21:4445–4449

    Google Scholar 

  • Dror RO, Jensen MØ, Borhani DW, Shaw DE (2010) Exploring atomic resolution physiology on a femtosecond to millisecond timescale using molecular dynamics simulations. J Gen Physiol 135:555–562

    CAS  PubMed  PubMed Central  Google Scholar 

  • Du X, Li Y, Xia YL, Ai SM, Liang J, Sang P, Ji XL, Liu SQ (2016) Insights into protein-ligand interactions: mechanisms, models, and methods. Int J Mol Sci 17:144

    PubMed Central  Google Scholar 

  • Dubbeldam D, Walton KS, Vlugt TJ, Calero S (2019) Design, parameterization, and implementation of atomic force fields for adsorption in nanoporous materials. Adv Theory Simul 2:1900135. https://doi.org/10.1002/adts.201900135

    Article  CAS  Google Scholar 

  • Elmore DE (2016) Why should biochemistry students be introduced to molecular dynamics simulations—and how can we introduce them? Biochem Mol Biol Educ 44:118–123

    CAS  PubMed  Google Scholar 

  • Eriksson MA, Pitera J, Kollman PA (1999) Prediction of the binding free energies of new TIBO-like HIV-1 reverse transcriptase inhibitors using a combination of PROFEC, PB/SA, CMC/MD, and free energy calculations. J Med Chem 42:868–881

    CAS  PubMed  Google Scholar 

  • Forster MJ (2002) Molecular modelling in structural biology. Micron 33:365–384

    CAS  PubMed  Google Scholar 

  • Fraccalvieri D, Pandini A, Stella F, Bonati L (2011) Conformational and functional analysis of molecular dynamics trajectories by self-organising maps. BMC Bioinf 12:158

    Google Scholar 

  • Frauenfelder H, Sligar S, Wolynes P (1991) The energy landscapes and motions of proteins. Science 254:1598–1603

    CAS  PubMed  Google Scholar 

  • Geng H, Chen F, Ye J, Jiang F (2019) Applications of molecular dynamics simulation in structure prediction of peptides and proteins. Comput Struct Biotechnol J 17:1162–1170

    CAS  PubMed  PubMed Central  Google Scholar 

  • Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discovery 10:449–461

    CAS  Google Scholar 

  • Gentle JE (2009) Computational statistics. Springer, New York

    Google Scholar 

  • Gilson MK, Zhou HX (2007) Calculation of protein-ligand binding affinities. Annu Rev Biophys Biomol Struct 36:21–42

    CAS  PubMed  Google Scholar 

  • Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Oper Res 13:533–549

    Google Scholar 

  • Glover F (1990) Tabu search—part 2. ORSA J Comput 2:4–32

    Google Scholar 

  • Grindon C, Harris S, Evans T, Novik K, Coveney P, Laughton C (2004) Large-scale molecular dynamics simulation of DNA: implementation and validation of the AMBER98 force-field in LAMMPS. Philos Transact A Math Phys Eng Sci 362:1373–1386

    Google Scholar 

  • Gutiérrez-de-Terán H, Aqvist J (2012) Linear interaction energy: method and applications in drug design. Methods Mol Biol 819:305–323

    PubMed  Google Scholar 

  • Guvench O, MacKerell AD Jr (2008) Comparison of protein force fields for molecular dynamics simulations. Methods Mol Biol 443:63–88

    CAS  PubMed  Google Scholar 

  • Hackenberger BK (2019) Genetics without genes: application of genetic algorithms in medicine. Croat Med J 60:177

    PubMed  PubMed Central  Google Scholar 

  • Hao GF, Xu WF, Yang SG, Yang GF (2015) Multiple simulated annealing-molecular dynamics (msa-md) for conformational space search of peptide and miniprotein. Sci Rep 5:15568. https://doi.org/10.1038/srep15568

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hernández-Rodríguez M, Rosales-Hernández MC, Mendieta-Wejebe JE, Martínez-Archundia M, Basurto JC (2016) Current tools and methods in molecular dynamics (MD) simulations for drug design. Curr Med Chem 23:3909–3924

    PubMed  Google Scholar 

  • Hollingsworth SA, Dror RO (2018) Molecular dynamics simulation for all. Neuron 99:1129–1143

    CAS  PubMed  PubMed Central  Google Scholar 

  • Homeyer N, Gohlke H (2012) Free energy calculations by the molecular mechanics Poisson-Boltzmann surface area method. Mol Inf 31:114–122

    CAS  Google Scholar 

  • Hospital A, Andrio P, Fenollosa C, Cicin-Sain D, Orozco M, Gelpí JL (2012) MDWeb and MDMoby: an integrated web-based platform for molecular dynamics simulations. Bioinformatics 28:1278–1279

    CAS  PubMed  Google Scholar 

  • Hospital A, Goñi JR, Orozco M, Gelpí JL (2015) Molecular dynamics simulations: advances and applications. Adv Appl Bioinforma Chem 8:37

    Google Scholar 

  • Hubbard D, Samuelson DA (2009) Modeling without measurements: how the decision analysis culture’s lack of empiricism reduces its effectiveness. OR/MS Today 36:26–31

    Google Scholar 

  • Hug S (2013) Classical molecular dynamics in a nutshell. Methods Mol Biol 924:127–152

    CAS  PubMed  Google Scholar 

  • Hypercube (2002) HyperChem 7.52: molecular visualization and simulation program package. Hyperchbe, Gainsville, FL

    Google Scholar 

  • Ingólfsson HI, Arnarez C, Periole X, Marrink SJ (2016) Computational ‘microscopy’ of cellular membranes. J Cell Sci 129:257–268

    PubMed  Google Scholar 

  • Ivankov DN, Bogatyreva NS, Lobanov MY, Galzitskaya OV (2009) Coupling between properties of the protein shape and the rate of protein folding. PLoS One 4(8):e6476

    PubMed  PubMed Central  Google Scholar 

  • Ivanova L, Tammiku-Taul J, García-Sosa AT, Sidorova Y, Saarma M, Karelson M (2018) Molecular dynamics simulations of the interactions between glial cell line-derived neurotrophic factor family receptor GFRα1 and small-molecule ligands. ACS Omega 3:11407–11414

    CAS  PubMed  PubMed Central  Google Scholar 

  • Jacobson MP, Kaminski GA, Friesner RA, Rapp CS (2002) Force-field validation using protein side chain prediction. J Phys Chem B 106:11673–11680

    CAS  Google Scholar 

  • Jaillet L, Artemova S, Redon S (2017) IM-UFF: extending the universal force-field for interactive molecular modeling. J Mol Graph Model 77:350–362

    CAS  PubMed  Google Scholar 

  • Jämbeck JP, Lyubartsev AP (2012) Derivation and systematic validation of a refined all-atom force-field for phosphatidylcholine lipids. J Phys Chem B 116:3164–3179

    PubMed  PubMed Central  Google Scholar 

  • Jolliffe IT, Cadima J (2016) Principal component analysis: a review and recent developments. Philos Trans R Soc A Math Phys Eng Sci 374:20150202

    Google Scholar 

  • Jorgensen WL (2009) Efficient drug lead discovery and optimization. Acc Chem Res 42:724–733

    CAS  PubMed  PubMed Central  Google Scholar 

  • Kalita J, Shukla R, Shukla H, Gadhave K, Giri R, Tripathi T (2017) Comprehensive analysis of the catalytic and structural properties of a mu-class glutathione s-transferase from Fasciola gigantica. Sci Rep 7:17547

    PubMed  PubMed Central  Google Scholar 

  • Kalita P, Shukla H, Gadhave K, Giri R, Tripathi T (2018) Role of the glutaredoxin domain and FAD in the stabilization of thioredoxin glutathione reductase. Arch Biochem Biophys 656:38–45

    CAS  PubMed  Google Scholar 

  • Kalita J, Shukla R, Tripathi T (2019a) Structural basis of urea-induced unfolding of Fasciola gigantica glutathione S-transferase. J Cell Physiol 234(4):4491–4503

    CAS  PubMed  Google Scholar 

  • Kalita P, Das KC, Shukla H, Tripathi T (2019b) Conserved Arg451 residue is critical for maintaining the stability and activity of thioredoxin glutathione reductase. Arch Biochem Biophys 674:108098

    CAS  PubMed  Google Scholar 

  • Kantarci-Carsibasi N, Haliloglu T, Doruker P (2008) Conformational transition pathways explored by Monte Carlo simulation integrated with collective modes. Biophys J 95:5862–5873

    CAS  PubMed  PubMed Central  Google Scholar 

  • Karplus M (2006) Spinach on the ceiling: a theoretical chemist’s return to biology. Annu Rev Biophys Biomol Struct 35:1–47

    CAS  PubMed  Google Scholar 

  • Karplus M, McCammon JA (2002) Molecular dynamics simulations of biomolecules. Nat Struct Biol 9:646–652

    CAS  PubMed  Google Scholar 

  • Klamt A (2005) COSMO-RS from quantum chemistry to fluid phase thermodynamics and drug design, 1st edn. Elsevier, Amsterdam

    Google Scholar 

  • Kraft D (2017) Self-consistent gradient flow for shape optimization. Optim Methods Softw 32:790–812

    CAS  PubMed  Google Scholar 

  • Kufareva I, Abagyan R (2012) Methods of protein structure comparison. Methods Mol Biol 857:231–257

    CAS  PubMed  PubMed Central  Google Scholar 

  • Kühne TD (2014) Second generation Car–Parrinello molecular dynamics. WIRES Comput Mol Sci 4:391–406

    Google Scholar 

  • Laino T, Mohamed F, Laio A, Parrinello M (2005) An efficient real space multigrid QM/MM electrostatic coupling. J Chem Theory Comput 1:1176–1184

    CAS  PubMed  Google Scholar 

  • Land H, Humble MS (2018) YASARA: a tool to obtain structural guidance in biocatalytic investigations. Methods Mol Biol 1685:43–67

    CAS  PubMed  Google Scholar 

  • Lee EH, Hsin J, Sotomayor M, Comellas G, Schulten K (2009) Discovery through the computational microscope. Structure 17:1295–1306

    CAS  PubMed  PubMed Central  Google Scholar 

  • Levitt M, Lifson S (1969) Refinement of protein conformations using a macromolecular energy minimization procedure. J Mol Biol 46:269–279

    CAS  PubMed  Google Scholar 

  • Lindahl E, Hess B, van der Spoel D (2001) GROMACS 3.0: a package for molecular simulation and trajectory analysis. J Mol Model 7:306–317

    CAS  Google Scholar 

  • Lorenz C, Doltsinis NL (2012) Molecular dynamics simulation: from “ab initio” to “coarse grained”. In: Leszczynski J (ed) Handbook of computational chemistry. Springer, Dordrecht, pp 195–238

    Google Scholar 

  • Loukatou S, Papageorgiou L, Fakourelis P, Filntisi A, Polychronidou E, Bassis I, Megalooikonomou V, Makałowski W, Vlachakis D, Kossida S (2014) Molecular dynamics simulations through GPU video games technologies. J Mol Biochem 3:64

    PubMed  PubMed Central  Google Scholar 

  • Luehr N, Jin AG, Martínez TJ (2015) Ab initio interactive molecular dynamics on graphical processing units (GPUs). J Chem Theory Comput 11:4536–4544

    CAS  PubMed  Google Scholar 

  • Lyubartsev AP, Laaksonen AM (2000) Dyna Mix–a scalable portable parallel md simulation package for arbitrary molecular mixtures. Comput Phys Commun 128:565–589

    CAS  Google Scholar 

  • Macchiagodena M, Del Frate G, Brancato G, Chandramouli B, Mancini G, Barone V (2017) Computational study of the DPAP molecular rotor in various environments: from force-field development to molecular dynamics simulations and spectroscopic calculations. Phys Chem Chem Phys 19:30590–30602

    CAS  PubMed  PubMed Central  Google Scholar 

  • Maisuradze GG, Liwo A, Scheraga HA (2009) Principal component analysis for protein folding dynamics. J Mol Biol 385:312–329

    CAS  PubMed  Google Scholar 

  • Marchand N, Lienard P, Siehl HU, Izato H (2014) Applications of molecular simulation software SCIGRESS in industry and university. FUJITSU Sci Tech J 50:46–51

    Google Scholar 

  • Marelius J, Kolmodin K, Feierberg I, Aqvist J (1998) Q: a molecular dynamics program for free energy calculations and empirical valence bond simulations in biomolecular systems. J Mol Graph Model 16:213–225

    CAS  PubMed  Google Scholar 

  • Marsili S, Signorini GF, Chelli R, Marchi M, Procacci P (2010) ORAC: a molecular dynamics simulation program to explore free energy surfaces in biomolecular systems at the atomistic level. J Comput Chem 31:1106–1116

    CAS  PubMed  Google Scholar 

  • McCammon JA, Gelin BR, Karplus M (1977) Dynamics of folded proteins. Nature 267:585–590

    CAS  PubMed  Google Scholar 

  • Mehra R, Dehury B, Kepp KP (2020) Cryo-temperature effects on membrane protein structure and dynamics. Phys Chem Chem Phys 22:5427–5438

    CAS  PubMed  Google Scholar 

  • Molecular Operating Environment (MOE) (2016) Chemical Computing Group Inc., Montreal, QC, Canada

    Google Scholar 

  • Moore S, Briggs E, Hodak M, Lu W, Bernholc J, Lee CW (2002) Scaling the RMG quantum mechanics code. In: Proceedings of the Extreme Scaling Workshop, vol 8, pp 1–6

    Google Scholar 

  • Neelamraju S, Wales DJ, Gosavi S (2019) Go-Kit: a tool to enable energy landscape exploration of proteins. J Chem Inf Model 59:1703–1708

    CAS  PubMed  Google Scholar 

  • Ohto T, Dodia M, Xu J, Imoto S, Tang F, Zysk F, Kühne TD, Shigeta Y, Bonn M, Wu X, Nagata Y (2019) Accessing the accuracy of density functional theory through structure and dynamics of the water–air interface. J Phys Chem Lett 10:4914–4919

    CAS  PubMed  PubMed Central  Google Scholar 

  • Pandey T, Shukla R, Shukla H, Sonkar A, Tripathi T, Singh AK (2017) A combined biochemical and computational studies of the rho-class glutathione s-transferase sll1545 of Synechocystis PCC 6803. Int J Biol Macromol 94:378–385

    CAS  PubMed  Google Scholar 

  • Paquet E, Viktor HL (2015) Molecular dynamics, Monte Carlo simulations, and langevin dynamics: a computational review. Biomed Res Int 2015:183918

    PubMed  PubMed Central  Google Scholar 

  • Pearlman DA (1999) Free energy grids: a practical qualitative application of free energy perturbation to ligand design using the OWFEG method. J Med Chem 42:4313–4324

    CAS  PubMed  Google Scholar 

  • Pearson K (1901) On lines and planes of closest fit to systems of points in space. Phil Mag 2:559–572

    Google Scholar 

  • Pensak DA (1989) Molecular modelling: scientific and technological boundaries. Pure Appl Chem 61:601–603

    CAS  Google Scholar 

  • Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, Chipot C, Skeel RD, Kalé L, Schulten K (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26:1781–1802

    CAS  PubMed  PubMed Central  Google Scholar 

  • Pirhadi S, Sunseri J, Koes DR (2016) Open source molecular modeling. J Mol Graph Model 69:127–143

    CAS  PubMed  PubMed Central  Google Scholar 

  • Pol-Fachin L, Rusu VH, Verli H, Lins RD (2012) GROMOS 53A6GLYC, an improved GROMOS force-field for hexopyranose-based carbohydrates. J Chem Theory Comput 8:4681–4690

    CAS  PubMed  Google Scholar 

  • Rackers JA, Wang Z, Lu C, Laury ML, Lagardère L, Schnieders MJ, Piquemal JP, Ren P, Ponder JW (2018) Tinker 8: software tools for molecular design. J Chem Theory Comput 14:5273–5289

    CAS  PubMed  PubMed Central  Google Scholar 

  • Rahman A (1964) Correlations in the motion of atoms in liquid argon. Phys Rev 136:A405

    Google Scholar 

  • Ryazantsev MN, Nikolaev DM, Struts AV, Brown MF (2019) Quantum mechanical and molecular mechanics modeling of membrane-embedded rhodopsins. J Membr Biol 252:425–449

    CAS  PubMed  Google Scholar 

  • Salmaso V, Moro S (2018) Bridging molecular docking to molecular dynamics in exploring ligand-protein recognition process: an overview. Front Pharmacol 9:923

    PubMed  PubMed Central  Google Scholar 

  • Salsbury FR Jr (2010) Molecular dynamics simulations of protein dynamics and their relevance to drug discovery. Curr Opin Pharmacol 10:738–744

    CAS  PubMed  PubMed Central  Google Scholar 

  • Sang P, Du X, Yang LQ, Meng ZH, Liu SQ (2017) Molecular motions and free-energy landscape of serine proteinase K in relation to its cold-adaptation: a comparative molecular dynamics simulation study and the underlying mechanisms. RSC Adv 7:28580–28590

    CAS  Google Scholar 

  • Seeber M, Cecchini M, Rao F, Settanni G, Caflisch A (2007) Wordom: a program for efficient analysis of molecular dynamics simulations. Bioinformatics 23:2625–2627

    CAS  PubMed  Google Scholar 

  • Sharma S (2019) Molecular dynamics simulation of nanocomposites using BIOVIA materials studio, lammps and gromacs. Elsevier, Amsterdam

    Google Scholar 

  • Shkurti A, Goni R, Andrio P, Breitmoser E, Bethune I, Orozco M, Laughton CA (2016) pyPcazip: a PCA-based toolkit for compression and analysis of molecular simulation data. SoftwareX 5:44–50

    Google Scholar 

  • Shukla H, Shukla R, Sonkar A, Tripathi T (2017a) Alterations in conformational topology and interaction dynamics caused by L418A mutation leads to activity loss of Mycobacterium tuberculosis isocitrate lyase. Biochem Biophys Res Commun 490(2):276–282

    CAS  PubMed  Google Scholar 

  • Shukla H, Shukla R, Sonkar A, Pandey T, Tripathi T (2017b) Distant Phe345 mutation compromises the stability and activity of Mycobacterium tuberculosis isocitrate lyase by modulating its structural flexibility. Sci Rep 7:1058

    PubMed  PubMed Central  Google Scholar 

  • Shukla R, Chetri PB, Sonkar A, Pakharukova MY, Mordvinov VA, Tripathi T (2018a) Identification of novel natural inhibitors of Opisthorchis felineus cytochrome P450 using structure-based screening and molecular dynamic simulation. J Biomol Struct Dyn 36(13):3541–3556

    CAS  PubMed  Google Scholar 

  • Shukla R, Shukla H, Tripathi T (2018b) Activity loss by H46 mutation in Mycobacterium tuberculosis isocitrate lyase is due to decrease in structural plasticity and collective motions of the active site. Tuberculosis 108:143–150

    CAS  PubMed  Google Scholar 

  • Shukla R, Shukla H, Kalita P, Tripathi T (2018c) Structural insights into natural compounds as inhibitors of Fasciola gigantica thioredoxin glutathione reductase. J Cell Biochem 119:3067–3080

    CAS  PubMed  Google Scholar 

  • Shukla R, Shukla H, Kalita P, Sonkar A, Pandey T, Singh DB, Kumar A, Tripathi T (2018d) Identification of potential inhibitors of Fasciola gigantica thioredoxin1: computational screening, molecular dynamics simulation and binding free energy studies. J Biomol Struct Dyn 36(8):2147–2162

    CAS  PubMed  Google Scholar 

  • Shukla R, Shukla H, Sonkar A, Pandey T, Tripathi T (2018e) Structure-based screening and molecular dynamics simulations offer novel natural compounds as potential inhibitors of Mycobacterium tuberculosis isocitrate lyase. J Biomol Struct Dyn 36(8):2045–2057

    CAS  PubMed  Google Scholar 

  • Shukla R, Shukla H, Tripathi T (2019) Structural and energetic understanding of novel natural inhibitors of Mycobacterium tuberculosis malate synthase. J Cell Biochem 120:2469–2482

    CAS  Google Scholar 

  • Singh S, Singh VK, Rai G (2020) Identification of differentially expressed hematopoiesis-associated genes in term low birth weight newborns by systems genomics approach. Current Genomics 20:469–482

    Google Scholar 

  • Smith LG, Zhao J, Mathews DH, Turner DH (2017) Physics-based all-atom modeling of RNA energetics and structure. Wiley Interdiscip Rev RNA 8(5):10.1002/wrna.1422

    PubMed Central  Google Scholar 

  • Smidstrup S, Markussen T, Vancraeyveld P, Wellendorff J, Schneider J, Gunst T, Verstichel B, Stradi D, Khomyakov PA, Vej-Hansen UG, Lee ME (2019) QuantumATK: an integrated platform of electronic and atomic-scale modelling tools. J Phys Condens Matter 32:015901

    PubMed  Google Scholar 

  • Sonkar A, Shukla H, Shukla R, Kalita J, Tripathi T (2019) Unfolding of Acinetobacter baumannii MurA proceeds through a metastable intermediate: a combined spectroscopic and computational investigation. Int J Biol Macromol 126:941–951

    CAS  PubMed  Google Scholar 

  • Sonne J, Jensen MOØ, Hansen FY, Hemmingsen L, Peters GH (2007) Reparameterization of all-atom dipalmitoylphosphatidylcholine lipid parameters enables simulation of fluid bilayers at zero tension. Biophys J 92:4157–4167

    CAS  PubMed  PubMed Central  Google Scholar 

  • Spitznagel B, Pritchett PR, Messina TC, Goadrich M, Rodriguez J (2016) An undergraduate laboratory activity on molecular dynamics simulations. Biochem Mol Biol Edu 44:130–139

    CAS  Google Scholar 

  • Stone JE, Hynninen AP, Phillips JC, Schulten K (2016) Early experiences porting the NAMD and VMD molecular simulation and analysis software to GPU-accelerated OpenPOWER platforms. High Perform Comput 9945:188–206

    Google Scholar 

  • Straatsma TP, McCammon JA (2001) IBM Syst J 40:328

    Google Scholar 

  • Sushko GB, Solov’yov IA, Solov’yov AV (2019) ModelingMesoBioNano systems with MBN studio made easy. J Mol Graph Model 88:247–260

    CAS  PubMed  Google Scholar 

  • Sweere AJ, Fraaije JG (2017) Accuracy test of the OPLS-AA force-field for calculating free energies of mixing and comparison with PAC-MAC. J Chem Theory Comput 13:1911–1923

    CAS  PubMed  PubMed Central  Google Scholar 

  • Troyer JM, Cohen FE (1995) Protein conformational landscapes: energy minimization and clustering of a long molecular dynamics trajectory. Proteins Struct Funct Genet 23:97–110

    CAS  PubMed  Google Scholar 

  • Vanommeslaeghe K, Guvench O (2014) Molecular mechanics. Curr Pharm Des 20:3281–3292

    CAS  PubMed  PubMed Central  Google Scholar 

  • Vanommeslaeghe K, MacKerell AD Jr (2015) CHARMM additive and polarizable force fields for biophysics and computer-aided drug design. Biochim Biophys Acta 1850:861–871

    CAS  PubMed  Google Scholar 

  • Vijayakumar R, Shukla R, Shukla H, Tripathi T (2018) Structure-function studies of the asparaginyl-tRNA synthetase from Fasciola gigantica: understanding the role of catalytic and non-catalytic domains. Biochem J 475(21):3377–3391

    Google Scholar 

  • Wang L, Veenstra DL, Radmer RJ, Kollman PA (1998) Can one predict protein stability? An attempt to do so for residue 133 of T4 lysozyme using a combination of free energy derivatives, PROFEC, and free energy perturbation methods. Proteins 32:438–458

    CAS  PubMed  Google Scholar 

  • Watts KS, Dalal P, Tebben AJ, Cheney DL, Shelley JC (2014) Macrocycle conformational sampling with MacroModel. J Chem Inf Model 54:2680–2696

    CAS  PubMed  Google Scholar 

  • Wong S, Amaro RE, McCammon JA (2009) MM-PBSA captures key role of intercalating water molecules at a protein-protein interface. J Chem Theory Comput 5:422–429

    CAS  PubMed  PubMed Central  Google Scholar 

  • Yao H, Dai Q, You Z, Bick A, Wang M (2018) Modulus simulation of asphalt binder models using molecular dynamics (MD) method. Constr Build Mater 162:430–441

    CAS  Google Scholar 

  • Young DC (2001) Computational chemistry: a practical guide for applying techniques to real-world problems. Wiley-Interscience, New York

    Google Scholar 

  • Yurtkuran A (2019) An improved electromagnetic field optimization for the global optimization problems. Comput Intel Neurosc 2019:6759106. https://doi.org/10.1155/2019/6759106

    Article  Google Scholar 

  • Zhu X, Lopes PE, Mackerell AD Jr (2012) Recent developments and applications of the CHARMM force fields. Wiley Interdiscip Rev Comput Mol Sci 2(1):167–185

    CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Molecular and Human Genetics, Institute of Science, Banaras Hindu University, Varanasi, India

    Sakshi Singh

  2. Centre for Bioinformatics, School of Biotechnology, Institute of Science, Banaras Hindu University, Varanasi, India

    Vinay Kumar Singh

Authors
  1. Sakshi Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vinay Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Biosciences and Biotechnology, Chhatrapati Shahu Ji Maharaj University, Kanpur, Uttar Pradesh, India

    Dev Bukhsh Singh

  2. Department of Biochemistry, North Eastern Hill University, Shillong, Meghalaya, India

    Timir Tripathi

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Singh, S., Singh, V.K. (2020). Molecular Dynamics Simulation: Methods and Application. In: Singh, D., Tripathi, T. (eds) Frontiers in Protein Structure, Function, and Dynamics. Springer, Singapore. https://doi.org/10.1007/978-981-15-5530-5_9

Download citation

Publish with us

Policies and ethics

Search

Navigation