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Molecular Dynamics Simulations with NAMD2

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Docking Screens for Drug Discovery

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2053))

Abstract

X-ray diffraction crystallography is the primary technique to determine the three-dimensional structures of biomolecules. Although a robust method, X-ray crystallography is not able to access the dynamical behavior of macromolecules. To do so, we have to carry out molecular dynamics simulations taking as an initial system the three-dimensional structure obtained from experimental techniques or generated using homology modeling. In this chapter, we describe in detail a tutorial to carry out molecular dynamics simulations using the program NAMD2. We chose as a molecular system to simulate the structure of human cyclin-dependent kinase 2.

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References

  1. Depristo MA, de Bakker PI, Johnson RJ, Blundell TL (2005) Crystallographic refinement by knowledge-based exploration of complex energy landscapes. Structure 13:1311–1319

    Article  CAS  PubMed  Google Scholar 

  2. Adams PD, Pannu NS, Read RJ, Brünger AT (1997) Cross-validated maximum likelihood enhances crystallographic simulated annealing refinement. Proc Natl Acad Sci U S A 94:5018–5023

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Rice LM, Brünger AT (1994) Torsion angle dynamics: reduced variable conformational sampling enhances crystallographic structure refinement. Proteins 19:277–290

    Article  CAS  PubMed  Google Scholar 

  4. Clarage JB, Phillips GN Jr (1994) Cross-validation tests of time-averaged molecular dynamics refinements for determination of protein structures by X-ray crystallography. Acta Crystallogr D Biol Crystallogr 50:24–36

    Article  CAS  PubMed  Google Scholar 

  5. Gros P, Betzel C, Dauter Z, Wilson KS, Hol WG (1989) Molecular dynamics refinement of a thermitase-eglin-c complex at 1.98 A resolution and comparison of two crystal forms that differ in calcium content. J Mol Biol 210:347–367

    Article  CAS  PubMed  Google Scholar 

  6. Kuriyan J, Petsko GA, Levy RM, Karplus M (1986) Effect of anisotropy and anharmonicity on protein crystallographic refinement. An evaluation by molecular dynamics. J Mol Biol 190:227–254

    Article  CAS  PubMed  Google Scholar 

  7. Westhof E, Chevrier B, Gallion SL, Weiner PK, Levy RM (1986) Temperature-dependent molecular dynamics and restrained X-ray refinement simulations of a Z-DNA hexamer. J Mol Biol 191:699–712

    Article  CAS  PubMed  Google Scholar 

  8. Wendoloski JJ, Wasserman ZR, Salemme FR (1988) Computer simulation of biological interactions and reactivity. J Comput Aided Mol Des 1:313–322

    Article  CAS  PubMed  Google Scholar 

  9. Ichiye T, Karplus M (1988) Anisotropy and anharmonicity of atomic fluctuations in proteins: implications for X-ray analysis. Biochemistry 27:3487–3497

    Article  CAS  PubMed  Google Scholar 

  10. Postma JP, Parker MW, Tsernoglou D (1989) Application of molecular dynamics in the crystallographic refinement of colicin A. Acta Crystallogr A 45:471–477

    Article  PubMed  Google Scholar 

  11. Gros P, Fujinaga M, Dijkstra BW, Kalk KH, Hol WG (1989) Crystallographic refinement by incorporation of molecular dynamics: thermostable serine protease thermitase complexed with eglin c. Acta Crystallogr B 45:488–499

    Article  PubMed  Google Scholar 

  12. Canduri F, de Azevedo WF (2008) Protein crystallography in drug discovery. Curr Drug Targets 9:1048–1053

    Article  CAS  PubMed  Google Scholar 

  13. Campagne S, Krepl M, Sponer J, Allain FH (2019) Combining NMR spectroscopy and molecular dynamic simulations to solve and analyze the structure of protein-RNA complexes. Methods Enzymol 614:393–422

    Article  PubMed  Google Scholar 

  14. Kämpf K, Izmailov SA, Rabdano SO, Groves AT, Podkorytov IS, Skrynnikov NR (2018) What drives 15N spin relaxation in disordered proteins? combined NMR/MD study of the H4 histone tail. Biophys J 115:2348–2367

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  15. Bochicchio A, Krepl M, Yang F, Varani G, Sponer J, Carloni P (2018) Molecular basis for the increased affinity of an RNA recognition motif with re-engineered specificity: a molecular dynamics and enhanced sampling simulations study. PLoS Comput Biol 14:e1006642

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Purslow JA, Nguyen TT, Egner TK, Dotas RR, Khatiwada B, Venditti V (2018) Active site breathing of human Alkbh5 revealed by solution NMR and accelerated molecular dynamics. Biophys J 115:1895–1905

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Quinn CM, Wang M, Fritz MP, Runge B, Ahn J, Xu C et al (2018) Dynamic regulation of HIV-1 capsid interaction with the restriction factor TRIM5α identified by magic-angle spinning NMR and molecular dynamics simulations. Proc Natl Acad Sci U S A 115:11519–11524

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Cousin SF, Kadeřávek P, Bolik-Coulon N, Gu Y, Charlier C, Carlier L (2018) Time-resolved protein side-chain motions unraveled by high-resolution relaxometry and molecular dynamics simulations. J Am Chem Soc 140:13456–13465

    Article  CAS  PubMed  Google Scholar 

  19. Papaleo E, Camilloni C, Teilum K, Vendruscolo M, Lindorff-Larsen K (2018) Molecular dynamics ensemble refinement of the heterogeneous native state of NCBD using chemical shifts and NOEs. PeerJ 6:e5125

    Article  PubMed  PubMed Central  Google Scholar 

  20. Sforça ML, Oyama S Jr, Canduri F, Lorenzi CC, Pertinhez TA, Konno K et al (2004) How C-terminal carboxyamidation alters the biological activity of peptides from the venom of the eumenine solitary wasp. Biochemistry 43:5608–5617

    Article  PubMed  CAS  Google Scholar 

  21. Fadel V, Bettendorff P, Herrmann T, de Azevedo WF Jr, Oliveira EB, Yamane T et al (2005) Automated NMR structure determination and disulfide bond identification of the myotoxin crotamine from Crotalus durissus terrificus. Toxicon 46:759–767

    Article  CAS  PubMed  Google Scholar 

  22. de Azevedo WF Jr (2011) Molecular dynamics simulations of protein targets identified in Mycobacterium tuberculosis. Curr Med Chem 18:1353–1366

    Article  PubMed  Google Scholar 

  23. Ganai SA (2018) Designing isoform-selective inhibitors against classical HDACs for effective anticancer therapy: insight and perspectives from in silico. Curr Drug Targets 19:815–824

    Article  CAS  PubMed  Google Scholar 

  24. Abdolmaleki A, Ghasemi JB, Ghasemi F (2017) Computer aided drug design for multi-target drug design: SAR /QSAR, molecular docking and pharmacophore methods. Curr Drug Targets 18:556–575

    Article  CAS  PubMed  Google Scholar 

  25. Kontoyianni M, Lacy B (2018) Toward computational understanding of molecular recognition in the human metabolizing cytochrome P450s. Curr Med Chem 25:3353–3373

    Article  CAS  PubMed  Google Scholar 

  26. Gentile L, Uccella NA, Sivakumar G (2017) Oleuropein: molecular dynamics and computation. Curr Med Chem 24:4315–4328

    CAS  PubMed  Google Scholar 

  27. 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

    Article  PubMed  CAS  Google Scholar 

  28. Tamay-Cach F, Villa-Tanaca ML, Trujillo-Ferrara JG, Alemán-González-Duhart D, Quintana-Pérez JC, González-Ramírez IA et al (2016) In silico studies most employed in the discovery of new antimicrobial agents. Curr Med Chem 23:3360–3373

    Article  CAS  PubMed  Google Scholar 

  29. Perricone U, Gulotta MR, Lombino J, Parrino B, Cascioferro S, Diana P et al (2018) An overview of recent molecular dynamics applications as medicinal chemistry tools for the undruggable site challenge. Medchemcomm 9:920–936

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wang W, Donini O, Reyes CM, Kollman PA (2001) Biomolecular simulations: recent developments in force fields, simulations of enzyme catalysis, protein-ligand, protein-protein, and protein-nucleic acid noncovalent interactions. Annu Rev Biophys Biomol Struct 30:211–243

    Article  CAS  PubMed  Google Scholar 

  31. Ray A, Jatana N, Thukral L (2017) Lipidated proteins: Spotlight on protein-membrane binding interfaces. Prog Biophys Mol Biol 128:74–84

    Article  CAS  PubMed  Google Scholar 

  32. Mackerell AD Jr, Nilsson L (2008) Molecular dynamics simulations of nucleic acid-protein complexes. Curr Opin Struct Biol 18:194–199

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Arnautova YA, Jagielska A, Scheraga HÁ (2006) A new force field (ECEPP-05) for peptides, proteins, and organic molecules. J Phys Chem B 110:5025–5044

    Article  CAS  PubMed  Google Scholar 

  34. Arnautova YA, Vorobjev YN, Vila JA, Scheraga HÁ (2009) Identifying native-like protein structures with scoring functions based on all-atom ECEPP force fields, implicit solvent models and structure relaxation. Proteins 77:38–51

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz KM, Ferguson DM et al (1995) A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J Am Chem Soc 117:5179–5197

    Article  CAS  Google Scholar 

  36. Duan Y, Wu C, Chowdhury S, Lee MC, Xiong G, Zhang W et al (2003) A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. Comput Chem 24:1999–2002

    Article  CAS  Google Scholar 

  37. AD MK Jr, Bashford D, Bellott M, Dunbrack RL Jr, Evanseck J, Field MJ et al (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. Phys Chem B 102:3586–3616

    Article  Google Scholar 

  38. Oostenbrink C, Soares TA, van der Vegt NF, van Gunsteren WF (2005) Validation of the 53A6 GROMOS force field. Eur Biophys J 34:273–384

    Article  CAS  PubMed  Google Scholar 

  39. Soares TA, Hünenberger PH, Kastenholz MA, Kräutler V, Lenz T, Lins RD et al (2005) An improved nucleic acid parameter set for the GROMOS force field. J Comput Chem 26:725–737

    Article  CAS  PubMed  Google Scholar 

  40. Lin Z, van Gunsteren WF (2013) Refinement of the application of the GROMOS 54A7 force field to β-peptides. J Comput Chem 34:2796–2805

    Article  CAS  PubMed  Google Scholar 

  41. Ewig CS, Berry R, Dinur U, Hill J-R, Hwang M-J, Li H et al (2001) Derivation of class II force fields. VIII. Derivation of a general quantum mechanical force field for organic compounds. J Comput Chem 22:1782–1800

    Article  CAS  PubMed  Google Scholar 

  42. Kaminski GA, Friesner RA, Tirado-Rives J, Jorgensen WL (2001) Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J Phys Chem B 105:6474–6487

    Article  CAS  Google Scholar 

  43. Adeniyi AA, Soliman MES (2017) Implementing QM in docking calculations: is it a waste of computational time? Drug Discov Today 22:1216–1223

    Article  CAS  PubMed  Google Scholar 

  44. Crespo A, Rodriguez-Granillo A, Lim VT (2017) Quantum-mechanics methodologies in drug discovery: applications of docking and scoring in lead optimization. Curr Top Med Chem 17:2663–2680

    Article  CAS  PubMed  Google Scholar 

  45. Yilmazer ND, Korth M (2016) Recent progress in treating protein-ligand interactions with quantum-mechanical methods. Int J Mol Sci 17:742

    Article  PubMed Central  CAS  Google Scholar 

  46. Cavasotto CN, Adler NS, Aucar MG (2018) Quantum chemical approaches in structure-based virtual screening and lead optimization. Front Chem 6:188

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Hitzenberger M, Schuster D, Hofer TS (2017) The binding mode of the sonic hedgehog inhibitor Robotnikinin, a combined docking and QM/MM MD study. Front Chem 5:76

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Ekhteiari Salmas R, Serhat Is Y, Durdagi S, Stein M, Yurtsever M (2018) A QM protein-ligand investigation of antipsychotic drugs with the dopamine D2 receptor (D2R). J Biomol Struct Dyn 36:2668–2677

    Article  CAS  PubMed  Google Scholar 

  49. Phipps MJ, Fox T, Tautermann CS, Skylaris CK (2017) Intuitive density functional theory-based energy decomposition analysis for protein-ligand interactions. J Chem Theory Comput 13:1837–1850

    Article  CAS  PubMed  Google Scholar 

  50. Hylsová M, Carbain B, Fanfrlík J, Musilová L, Haldar S, Köprülüoğlu C et al (2017) Explicit treatment of active-site waters enhances quantum mechanical/implicit solvent scoring: Inhibition of CDK2 by new pyrazolo[1,5-a]pyrimidines. Eur J Med Chem 126:1118–1128

    Article  PubMed  CAS  Google Scholar 

  51. Pecina A, Meier R, Fanfrlík J, Lepšík M, Řezáč J, Hobza P et al (2016) The SQM/COSMO filter: reliable native pose identification based on the quantum-mechanical description of protein-ligand interactions and implicit COSMO solvation. Chem Commun (Camb) 52:3312–3315

    Article  CAS  Google Scholar 

  52. Yang Z, Liu Y, Chen Z, Xu Z, Shi J, Chen K et al (2015) A quantum mechanics-based halogen bonding scoring function for protein-ligand interactions. J Mol Model 21:138

    Article  PubMed  CAS  Google Scholar 

  53. Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E et al (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26:1781–1802

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Humphrey W, Dalke A, Schulten K (1996) VMD—visual molecular dynamics. J Mol Graph 14:33–38

    Article  CAS  PubMed  Google Scholar 

  55. Brünger AT, Kuriyan J, Karplus M (1987) Crystallographic R factor refinement by molecular dynamics. Science 235:458–460

    Article  PubMed  Google Scholar 

  56. de Azevedo WF Jr, Canduri F, Fadel V, Teodoro LG, Hial V, Gomes RA (2001) Molecular model for the binary complex of uropepsin and pepstatin. Biochem Biophys Res Commun 287:277–281

    Article  PubMed  CAS  Google Scholar 

  57. De Azevedo WF, Leclerc S, Meijer L, Havlicek L, Strnad M, Kim SH (1997) Inhibition of cyclin-dependent kinases by purine analogues: crystal structure of human cdk2 complexed with roscovitine. Eur J Biochem 243:518–526

    Article  PubMed  Google Scholar 

  58. Morgan DO (1995) Principles of CDK regulation. Nature 374:131–134

    Article  CAS  PubMed  Google Scholar 

  59. Murray AW (1994) Cyclin-dependent kinases: regulators of the cell cycle and more. Chem Biol 1:191–195

    Article  CAS  PubMed  Google Scholar 

  60. Kim SH, Schulze-Gahmen U, Brandsen J, de Azevedo Junior WF (1996) Structural basis for chemical inhibition of CDK2. Prog Cell Cycle Res 2:137–145

    Article  CAS  PubMed  Google Scholar 

  61. De Azevedo WF Jr, Mueller-Dieckmann HJ, Schulze-Gahmen U, Worland PJ, Sausville E, Kim SH (1996) Structural basis for specificity and potency of a flavonoid inhibitor of human CDK2, a cell cycle kinase. Proc Natl Acad Sci U S A 93:2735–2740

    Article  PubMed  PubMed Central  Google Scholar 

  62. Canduri F, de Azevedo WF Jr (2005) Structural basis for interaction of inhibitors with cyclin-dependent kinase 2. Curr Comput Aided Drug Des 1:53–64

    Article  CAS  Google Scholar 

  63. Krystof V, Cankar P, Frysová I, Slouka J, Kontopidis G, Dzubák P (2006) 4-arylazo-3,5-diamino-1H-pyrazole CDK inhibitors: SAR study, crystal structure in complex with CDK2, selectivity, and cellular effects. J Med Chem 49:6500–6509

    Article  CAS  PubMed  Google Scholar 

  64. de Azevedo WF Jr (2016) Opinion paper: targeting multiple cyclin-dependent kinases (CDKs): a new strategy for molecular docking studies. Curr Drug Targets 17:2

    Article  PubMed  CAS  Google Scholar 

  65. Levin NM, Pintro VO, de Ávila MB, de Mattos BB, De Azevedo WF Jr (2017) Understanding the structural basis for inhibition of cyclin-dependent kinases. New pieces in the molecular puzzle. Curr Drug Targets 18:1104–1111

    Article  PubMed  CAS  Google Scholar 

  66. de Ávila MB, Xavier MM, Pintro VO, de Azevedo WF (2017) Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2. Biochem Biophys Res Commun 494:305–310

    Article  PubMed  CAS  Google Scholar 

  67. Levin NMB, Pintro VO, Bitencourt-Ferreira G, Mattos BB, Silvério AC, de Azevedo WF Jr (2018) Development of CDK-targeted scoring functions for prediction of binding affinity. Biophys Chem 235:1–8

    Article  CAS  PubMed  Google Scholar 

  68. Volkart PA, Bitencourt-Ferreira G, Souto AA, de Azevedo WF (2019) Cyclin-dependent kinase 2 in cellular senescence and cancer. A structural and functional review. Curr Drug Targets 20(7):716–726. https://doi.org/10.2174/1389450120666181204165344

    Article  CAS  PubMed  Google Scholar 

  69. Thomsen R, Christensen MH (2006) MolDock: a new technique for high-accuracy molecular docking. J Med Chem 49:3315–3321

    Article  CAS  PubMed  Google Scholar 

  70. De Bondt HL, Rosenblatt J, Jancarik J, Jones HD, Morgan DO, Kim SH (1993) Crystal structure of cyclin-dependent kinase 2. Nature 363:595–602

    Article  PubMed  Google Scholar 

  71. Schulze-Gahmen U, De Bondt HL, Kim SH (1996) High-resolution crystal structures of human cyclin-dependent kinase 2 with and without ATP: bound waters and natural ligand as guides for inhibitor design. J Med Chem 39:4540–4546

    Article  CAS  PubMed  Google Scholar 

  72. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H et al (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Berman HM, Battistuz T, Bhat TN, Bluhm WF, Bourne PE, Burkhardt K et al (2002) The Protein Data Bank. Acta Crystallogr D Biol Crystallogr 58:899–907

    Article  PubMed  CAS  Google Scholar 

  74. Westbrook J, Feng Z, Chen L, Yang H, Berman HM (2003) The Protein Data Bank and structural genomics. Nucleic Acids Res 31:489–491

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Sali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234:779–815

    Article  CAS  PubMed  Google Scholar 

  76. Uchôa HB, Jorge GE, Freitas Da Silveira NJ, Camera JC Jr, Canduri F, De Azevedo WF Jr (2004) Parmodel: a web server for automated comparative modeling of proteins. Biochem Biophys Res Commun 325:1481–1486

    Article  PubMed  CAS  Google Scholar 

  77. Daniyan MO, Ojo OT (2019) In silico identification and evaluation of potential interaction of Azadirachta indica phytochemicals with Plasmodium falciparum heat shock protein 90. J Mol Graph Model 87:144–164

    Article  CAS  PubMed  Google Scholar 

  78. Chandra N, Biswas S, Rout J, Basu G, Tripathy U (2018) Stability of β-turn in LaR2C-N7 peptide for its translation-inhibitory activity against hepatitis C viral infection: A molecular dynamics study. Spectrochim Acta A Mol Biomol Spectrosc 211:26–33

    Article  PubMed  CAS  Google Scholar 

  79. Uba AI, Yelekçi K (2018) Pharmacophore-based virtual screening for identification of potential selective inhibitors of human histone deacetylase 6. Comput Biol Chem 77:318–330

    Article  CAS  PubMed  Google Scholar 

  80. Miao Y, Bhattarai A, Nguyen ATN, Christopoulos A, May LT (2018) Structural basis for binding of allosteric drug leads in the adenosine A1 receptor. Sci Rep 8:16836

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Liamas E, Kubiak-Ossowska K, Black RA, Thomas ORT, Zhang ZJ, Mulheran PA (2018) Adsorption of fibronectin fragment on surfaces using fully atomistic molecular dynamics simulations. Int J Mol Sci 19:3321

    Article  PubMed Central  CAS  Google Scholar 

  82. Rezapour N, Rasekh B, Mofradnia SR, Yazdian F, Rashedi H, Tavakoli Z (2019) Molecular dynamics studies of polysaccharide carrier based on starch in dental cavities. Int J Biol Macromol 121:616–624

    Article  CAS  PubMed  Google Scholar 

  83. Jiang W, Thirman J, Jo S, Roux B (2018) Reduced free energy perturbation/hamiltonian replica exchange molecular dynamics method with unbiased alchemical thermodynamic axis. J Phys Chem B 122:9435–9442

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Zhang R, Zhang L, Zheng Q, Gao P, Zhao J, Yang J (2018) Direct Z-scheme water splitting photocatalyst based on two-dimensional Van Der Waals heterostructures. J Phys Chem Lett 9:5419–5424

    Article  CAS  PubMed  Google Scholar 

  85. Kulke M, Geist N, Möller D, Langel W (2018) Replica-based protein structure sampling methods: compromising between explicit and implicit solvents. J Phys Chem B 122:7295–7307

    Article  CAS  PubMed  Google Scholar 

  86. Sarkar R, Habib M, Pal S, Prezhdo OV (2018) Ultrafast, asymmetric charge transfer and slow charge recombination in porphyrin/CNT composites demonstrated by time-domain atomistic simulation. Nanoscale 10:12683–12694

    Article  CAS  PubMed  Google Scholar 

  87. Chen H, Fu H, Shao X, Chipot C, Cai W (2018) ELF: an extended-lagrangian free energy calculation module for multiple molecular dynamics engines. J Chem Inf Model 58:1315–1318

    Article  CAS  PubMed  Google Scholar 

  88. Childers MC, Daggett V (2018) Validating molecular dynamics simulations against experimental observables in light of underlying conformational ensembles. J Phys Chem B 122:6673–6689

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Uba AI, Yelekçi K (2018) Carboxylic acid derivatives display potential selectivity for human histone deacetylase 6: Structure-based virtual screening, molecular docking and dynamics simulation studies. Comput Biol Chem 75:131–142

    Article  CAS  PubMed  Google Scholar 

  90. Mishra V, Pathak C (2018) Structural insights into pharmacophore-assisted in silico identification of protein-protein interaction inhibitors for inhibition of human toll-like receptor 4 - myeloid differentiation factor-2 (hTLR4-MD-2) complex. J Biomol Struct Dyn 29:1–24

    Google Scholar 

  91. Serçinoglu O, Ozbek P (2018) gRINN: a tool for calculation of residue interaction energies and protein energy network analysis of molecular dynamics simulations. Nucleic Acids Res 46:554–562

    Article  CAS  Google Scholar 

  92. Banu H, Joseph MC, Nisar MN (2018) In-silico approach to investigate death domains associated with nano-particle-mediated cellular responses. Comput Biol Chem 75:11–23

    Article  CAS  PubMed  Google Scholar 

  93. Mena-Ulecia K, MacLeod-Carey D (2018) Interactions of 2-phenyl-benzotriazole xenobiotic compounds with human Cytochrome P450-CYP1A1 by means of docking, molecular dynamics simulations and MM-GBSA calculations. Comput Biol Chem 74:253–262

    Article  CAS  PubMed  Google Scholar 

  94. Kurniawan F, Kartasasmita RE, Yoshioka N, Mutalib A, Tjahjono DH (2018) Computational study of imidazolylporphyrin derivatives as a radiopharmaceutical ligand for melanoma. Curr Comput Aided Drug Des 14:191–199

    Article  CAS  PubMed  Google Scholar 

  95. Khezri A, Karimi A, Yazdian F, Jokar M, Mofradnia SR, Rashedi H et al (2018) Molecular dynamic of curcumin/chitosan interaction using a computational molecular approach: emphasis on biofilm reduction. Int J Biol Macromol 114:972–978

    Article  CAS  PubMed  Google Scholar 

  96. Subasri S, Chaudhary SK, Sekar K, Kesherwani M, Velmurugan D (2017) Molecular docking and molecular dynamics simulations of fumarate hydratase and its mutant H235N complexed with pyromellitic acid and citrate. J Bioinforma Comput Biol 15:1750026

    Article  CAS  Google Scholar 

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Acknowledgments

This work was supported by grants from CNPq (Brazil) (308883/2014-4). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior—Brasil (CAPES)—Finance Code 001. GB-F acknowledges support from PUCRS/BPA fellowship. WFA is a senior researcher for CNPq (Brazil) (Process Numbers: 308883/2014-4 and 309029/2018-0).

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Correspondence to Walter Filgueira de Azevedo Jr. .

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Bitencourt-Ferreira, G., de Azevedo, W.F. (2019). Molecular Dynamics Simulations with NAMD2. In: de Azevedo Jr., W. (eds) Docking Screens for Drug Discovery. Methods in Molecular Biology, vol 2053. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9752-7_8

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