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Computational Predictions for Multi-Target Drug Design

  • Neelima GuptaEmail author
  • Prateek Pandya
  • Seema Verma
Protocol
Part of the Methods in Pharmacology and Toxicology book series (MIPT)

Abstract

Computational techniques have proven to be an essential tool in modern drug discovery research. These tools offer powerful methods for prediction of ligand–receptor interaction events at atomic details, without attempting exhaustive experimental setup. Single ligand–single target strategies for the discovery of new drug molecules have become outdated due to the factors like drug resistance, increased side effects, reduced efficacy, etc., in addition to the involvement of long time period for validation of a new molecule by toxicology and pharmacokinetic studies. Multi-target drug designing approach can offer a paradigm shift for alternative usage of known drugs for complex diseases. These approaches combine knowledge of complex disease networks, chemical and physical characteristics of drugs, and biological receptors. With the availability of advanced computational resources, a number of tools have been developed that help in the identification of new and multiple targets for the already known or new drugs. In the present chapter, an attempt has been made to highlight the current state-of-the-art methodologies used in multi-target identification for therapeutic effects of known drugs or new drug candidates.

Keywords

Binding interactions Machine learning Molecular docking Molecular dynamics Multi-target drug design (MTDD) Polypharmacology QM–MM approach QSAR Systems approach 

References

  1. 1.
    Hanahan D, Weinberg RA (2000) The hallmarks of cancer. Cell 100:57–70.  https://doi.org/10.1016/S0092-8674(00)81683-9CrossRefGoogle Scholar
  2. 2.
    Oechsner M, Buhmann C, Strauss J, Stuerenburg HJ (2002) COMT-inhibition increases serum levels of dihydroxyphenylacetic acid (DOPAC) in patients with advanced Parkinson’s disease. J Neural Transm 109(1):69–75.  https://doi.org/10.1007/s702-002-8237-zCrossRefPubMedGoogle Scholar
  3. 3.
    Hartman IVJL, Garvik B, Hartwel L (2001) Principles for the buffering of genetic variation. Science 291:1001–1004.  https://doi.org/10.1126/science.1056072CrossRefPubMedGoogle Scholar
  4. 4.
    Bonander N, Bill RM (2009) Relieving the first bottleneck in the drug discovery pipeline: using array technologies to rationalize membrane protein production. Expert Rev Proteomics 6:501–505.  https://doi.org/10.1586/epr.09.65CrossRefPubMedGoogle Scholar
  5. 5.
    Gillespie SH, Singh K (2011) XDR-TB, what is it; how is it treated; and why is therapeutic failure so high? Recent Pat Antiinfect Drug Discov 6:77–83CrossRefGoogle Scholar
  6. 6.
    Horst JA, Laurenzi A, Bernard B, Samudrala R (2012) Computational multitarget drug discovery. In: Polypharmacology in drug discovery. Wiley, New York, pp 263–301CrossRefGoogle Scholar
  7. 7.
    Sacks LV, Behrman RE (2009) Challenges, successes and hopes in the development of novel TB therapeutics. Future Med Chem 1:749–756.  https://doi.org/10.4155/fmc.09.53CrossRefPubMedGoogle Scholar
  8. 8.
    Keith CT, Borisy AA, Stockwell BR (2005) Innovation: multicomponent therapeutics for networked systems. Nat Rev Drug Discov 4:71–78.  https://doi.org/10.1038/nrd1609CrossRefPubMedGoogle Scholar
  9. 9.
    Ekins S, Williams AJ, Krasowski MD, Freundlich JS (2011) In silico repositioning of approved drugs for rare and neglected diseases. Drug Discov Today 16.  https://doi.org/10.1016/j.drudis.2011.02.016CrossRefGoogle Scholar
  10. 10.
    Jenwitheesuk E, Horst JA, Rivas KL, Van Voorhis WC, Samudrala R (2008) Novel paradigms for drug discovery: computational multitarget screening. Trends Pharmacol Sci 29:62–71.  https://doi.org/10.1016/J.TIPS.2007.11.007CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Jenwitheesuk E, Samudrala R (2005) Identification of potential multitarget antimalarial drugs. JAMA 294:1487.  https://doi.org/10.1001/jama.294.12.1490CrossRefGoogle Scholar
  12. 12.
    Minie M, Chopra G, Sethi G, Horst J, White G, Roy A, Hatti K, Samudrala R (2014) CANDO and the infinite drug discovery frontier. Drug Discov Today 19:1353–1363.  https://doi.org/10.1016/J.DRUDIS.2014.06.018CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Ren J, Xie L, Li WW, Bourne PE (2010) SMAP-WS: a parallel web service for structural proteome-wide ligand-binding site comparison. Nucleic Acids Res 38:W441–W444.  https://doi.org/10.1093/nar/gkq400CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Swamidass SJ (2011) Mining small-molecule screens to repurpose drugs. Brief Bioinform 12:327–335.  https://doi.org/10.1093/bib/bbr028CrossRefPubMedGoogle Scholar
  15. 15.
    Xu K, Cote TR (2011) Database identifies FDA-approved drugs with potential to be repurposed for treatment of orphan diseases. Brief Bioinform 12:341–345.  https://doi.org/10.1093/bib/bbr006CrossRefPubMedGoogle Scholar
  16. 16.
    Anighoro A, Bajorath J, Rastelli G (2014) Polypharmacology: challenges and opportunities in drug discovery. J Med Chem 57(19):7874–7887.  https://doi.org/10.1021/jm5006463CrossRefGoogle Scholar
  17. 17.
    Hopkins AL, Mason JS, Overington JP (2006) Can we rationally design promiscuous drugs? Curr Opin Struct Biol 16(1):127–136.  https://doi.org/10.1016/j.sbi.2006.01.013CrossRefPubMedGoogle Scholar
  18. 18.
    Krug M, Hilgeroth A (2008) Recent advances in the development of multi-kinase inhibitors. Mini Rev Med Chem 8(13):1312–1327.  https://doi.org/10.2174/138955708786369591CrossRefPubMedGoogle Scholar
  19. 19.
    Adrian G, Marcel V, Robert B, Richard T (2007) A comparison of physicochemical property profiles of marketed oral drugs and orally bioavailable anti-cancer protein kinase inhibitors in clinical development. Curr Top Med Chem 7(14):1408–1422.  https://doi.org/10.2174/156802607781696819CrossRefGoogle Scholar
  20. 20.
    Nahta R, Yu D, Hung MC, Hortobagyi GN, Esteva FJ (2006) Mechanisms of disease: understanding resistance to HER2-targeted therapy in human breast cancer. Nat Clin Pract Oncol 3(5):269–280.  https://doi.org/10.1038/ncponc0509CrossRefPubMedGoogle Scholar
  21. 21.
    Sergina NV, Rausch M, Wang D, Blair J, Hann B, Shokat KM, Moasser MM (2007) Escape from HER-family tyrosine kinase inhibitor therapy by the kinase-inactive HER3. Nature.  https://doi.org/10.1038/nature05474CrossRefGoogle Scholar
  22. 22.
    Tabernero J (2007) The role of VEGF and EGFR inhibition: implications for combining anti-VEGF and anti-EGFR agents. Mol Cancer Res 5:203–220CrossRefGoogle Scholar
  23. 23.
    Cheng F, Liu C, Jiang J, Lu W, Li W, Liu G, Zhou W, Huang J, Tang Y (2012) Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput Biol 8.  https://doi.org/10.1371/journal.pcbi.1002503CrossRefGoogle Scholar
  24. 24.
    O’Meara MJ, Ballouz S, Shoichet BK, Gillis J (2016) Ligand similarity complements sequence, physical interaction, and co-expression for gene function prediction. PLoS One 11(7):e0160098.  https://doi.org/10.1371/journal.pone.0160098CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Bhattacharjee P, Sarkar S, Pandya P, Bhadra K (2016) Targeting different RNA motifs by beta carboline alkaloid, harmalol: a comparative photophysical, calorimetric, and molecular docking approach. J Biomol Struct Dyn 34(12):2722–2740.  https://doi.org/10.1080/07391102.2015.1126694CrossRefPubMedGoogle Scholar
  26. 26.
    Sarkar S, Pandya P, Bhadra K (2014) Sequence specific binding of beta carboline alkaloid harmalol with deoxyribonucleotides: binding heterogeneity, conformational, thermodynamic and cytotoxic aspects. PLoS One 9(9):e108022.  https://doi.org/10.1371/journal.pone.0108022CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Pandya P, Agarwal LK, Gupta N, Pal S (2014) Molecular recognition pattern of cytotoxic alkaloid vinblastine with multiple targets. J Mol Graph Model 54:1–9.  https://doi.org/10.1016/j.jmgm.2014.09.001CrossRefPubMedGoogle Scholar
  28. 28.
    Masum AA, Chakraborty M, Pandya P, Halder UC, Islam MM, Mukhopadhyay S (2014) Thermodynamic study of rhodamine 123-calf thymus DNA interaction: determination of calorimetric enthalpy by optical melting study. J Phys Chem B 118(46):13151–13161.  https://doi.org/10.1021/jp509326rCrossRefPubMedGoogle Scholar
  29. 29.
    Islam MM, Chakraborty M, Pandya P, Al Masum A, Gupta N, Mukhopadhyay S (2013) Binding of DNA with Rhodamine B: spectroscopic and molecular modeling studies. Dyes Pigments 99(2):412–422.  https://doi.org/10.1016/j.dyepig.2013.05.028CrossRefGoogle Scholar
  30. 30.
    Pandya P, Gupta SP, Pandav K, Barthwal R, Jayaram B, Kumar S (2012) DNA binding studies of Vinca alkaloids: experimental and computational evidence. Nat Prod Commun 7(3):305–309PubMedGoogle Scholar
  31. 31.
    Pandya P, Islam MM, Kumar GS, Jayaram B, Kumar S (2010) DNA minor groove binding of small molecules: experimental and computational evidence. J Chem Sci 122(2):247–257CrossRefGoogle Scholar
  32. 32.
    Islam MM, Pandya P, Kumar S, Kumar GS (2009) RNA targeting through binding of small molecules: studies on t-RNA binding by the cytotoxic protoberberine alkaloid coralyne. Mol Biosyst 5(3):244–254.  https://doi.org/10.1039/b816480kCrossRefPubMedGoogle Scholar
  33. 33.
    Islam MM, Pandya P, Chowdhury SR, Kumar S, Kumar GS (2008) Binding of DNA-binding alkaloids berberine and palmatine to tRNA and comparison to ethidium: spectroscopic and molecular modeling studies. J Mol Struct 891(1–3):498–507.  https://doi.org/10.1016/j.molstruc.2008.04.043CrossRefGoogle Scholar
  34. 34.
    Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461.  https://doi.org/10.1002/jcc.21334CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Chang MW, Ayeni C, Breuer S, Torbett BE (2010) Virtual screening for HIV protease inhibitors: a comparison of AutoDock 4 and Vina. PLoS One 5(8):e11955.  https://doi.org/10.1371/journal.pone.0011955CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Shaikh SA, Jayaram B (2007) A swift all-atom energy-based computational protocol to predict DNA-ligand binding affinity and Delta Tm. J Med Chem 50(9):2240–2244.  https://doi.org/10.1021/jm060542cCrossRefPubMedGoogle Scholar
  37. 37.
    Gupta A, Sharma P, Jayaram B (2007) ParDOCK: an all atom energy based Monte Carlo docking protocol for protein-ligand complexes. Protein Pept Lett 14(7):632–646.  https://doi.org/10.2174/092986607781483831CrossRefPubMedGoogle Scholar
  38. 38.
    Lin J-H, Perryman AL, Schames JR, McCammon JA (2002) Computational drug design accommodating receptor flexibility: the relaxed complex scheme. J Am Chem Soc 124(20):5632–5633.  https://doi.org/10.1021/ja0260162CrossRefPubMedGoogle Scholar
  39. 39.
    Amaro RE, Baron R, McCammon JA (2008) An improved relaxed complex scheme for receptor flexibility in computer-aided drug design. J Comput Aided Mol Des 22(9):693–705.  https://doi.org/10.1007/s10822-007-9159-2CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Yuriev E, Agostino M, Ramsland PA (2011) Challenges and advances in computational docking: 2009 in review. J Mol Recognit 24(2):149–164.  https://doi.org/10.1002/jmr.1077CrossRefPubMedGoogle Scholar
  41. 41.
    Ellingson SR, Smith JC, Baudry J (2013) VinaMPI: facilitating multiple receptor high-throughput virtual docking on high-performance computers. J Comput Chem 34(25):2212–2221.  https://doi.org/10.1002/jcc.23367CrossRefPubMedGoogle Scholar
  42. 42.
    Pan JB, Ji N, Pan W, Hong R, Wang H, Ji ZL (2014) High-throughput identification of off-targets for the mechanistic study of severe adverse drug reactions induced by analgesics. Toxicol Appl Pharmacol 274(1):24–34.  https://doi.org/10.1016/j.taap.2013.10.017CrossRefPubMedGoogle Scholar
  43. 43.
    Amaro RE, Schnaufer A, Interthal H, Hol W, Stuart KD, McCammon JA (2008) Discovery of drug-like inhibitors of an essential RNA-editing ligase in Trypanosoma brucei. Proc Natl Acad Sci U S A 105(45):17278–17283.  https://doi.org/10.1073/pnas.0805820105CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Hui-Fang L, Qing S, Jian Z, Wei F (2010) Evaluation of various inverse docking schemes in multiple targets identification. J Mol Graph Model 29(3):326–330.  https://doi.org/10.1016/j.jmgm.2010.09.004CrossRefPubMedGoogle Scholar
  45. 45.
    Chopra G, Samudrala R (2016) Exploring polypharmacology in drug discovery and repurposing using the CANDO platform. Curr Pharm Des 22:3109–3123.  https://doi.org/10.2174/1381612822666160325121943CrossRefPubMedGoogle Scholar
  46. 46.
    Lodola A, De Vivo M (2012) The increasing role of QM/MM in drug discovery. Adv Protein Chem Struct Biol 87:337–362.  https://doi.org/10.1016/B978-0-12-398312-1.00011-1CrossRefPubMedGoogle Scholar
  47. 47.
    Gleeson MP, Gleeson D (2009) QM/MM calculations in drug discovery: a useful method for studying binding phenomena? J Chem Inf Model 49(3):670–677.  https://doi.org/10.1021/ci800419jCrossRefPubMedGoogle Scholar
  48. 48.
    Schrödinger L (2017) Schrödinger Suite 2017–4 QM-Polarized Ligand Docking protocol; Glide, Schrödinger, LLC, New York, NY, 2017; Jaguar, Schrödinger, LLC, New York, NY, 2017; QSite, Schrödinger, LLC, New York, NY, 2017Google Scholar
  49. 49.
    Speck-Planche A, Cordeiro MN (2015) Multitasking models for quantitative structure-biological effect relationships: current status and future perspectives to speed up drug discovery. Expert Opin Drug Discov 10(3):245–256.  https://doi.org/10.1517/17460441.2015.1006195CrossRefPubMedGoogle Scholar
  50. 50.
    Liu X, Zhu F, Ma X, Shi Z, Yang S, Wei Y, Chen Y (2013) Predicting targeted polypharmacology for drug repositioning and multi-target drug discovery. Curr Med Chem 20(13):1646–1661CrossRefGoogle Scholar
  51. 51.
    Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz’min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57(12):4977–5010.  https://doi.org/10.1021/jm4004285CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Roy K, Kar S, Das RN (2015) Selected statistical methods in QSAR. In: Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. Academic Press, Amsterdam, pp 191–229.  https://doi.org/10.1016/b978-0-12-801505-6.00006-5CrossRefGoogle Scholar
  53. 53.
    Nantasenamat C, Worachartcheewan A, Jamsak S, Preeyanon L, Shoombuatong W, Simeon S, Mandi P, Isarankura-Na-Ayudhya C, Prachayasittikul V (2015) AutoWeka: toward an automated data mining software for QSAR and QSPR studies. Methods Mol Biol 1260:119–147.  https://doi.org/10.1007/978-1-4939-2239-0_8CrossRefPubMedGoogle Scholar
  54. 54.
    Yang Y, Lin T, Weng XL, Darr JA, Wang XZ (2011) Data flow modeling, data mining and QSAR in high-throughput discovery of functional nanomaterials. Comput Chem Eng 35(4):671–678.  https://doi.org/10.1016/j.compchemeng.2010.04.018CrossRefGoogle Scholar
  55. 55.
    Vina D, Uriarte E, Orallo F, Gonzalez-Diaz H (2009) Alignment-free prediction of a drug-target complex network based on parameters of drug connectivity and protein sequence of receptors. Mol Pharm 6(3):825–835.  https://doi.org/10.1021/mp800102cCrossRefPubMedGoogle Scholar
  56. 56.
    Geronikaki A, Druzhilovsky D, Zakharov A, Poroikov V (2008) Computer-aided prediction for medicinal chemistry via the Internet. SAR QSAR Environ Res 19(1–2):27–38.  https://doi.org/10.1080/10629360701843649CrossRefPubMedGoogle Scholar
  57. 57.
    Marzaro G, Chilin A, Guiotto A, Uriarte E, Brun P, Castagliuolo I, Tonus F, Gonzalez-Diaz H (2011) Using the TOPS-MODE approach to fit multi-target QSAR models for tyrosine kinases inhibitors. Eur J Med Chem 46(6):2185–2192.  https://doi.org/10.1016/j.ejmech.2011.02.072CrossRefGoogle Scholar
  58. 58.
    Rosenbaum L, Dorr A, Bauer MR, Boeckler FM, Zell A (2013) Inferring multi-target QSAR models with taxonomy-based multi-task learning. J Cheminform 5(1):33.  https://doi.org/10.1186/1758-2946-5-33CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Prado-Prado FJ, Gonzalez-Diaz H, de la Vega OM, Ubeira FM, Chou KC (2008) Unified QSAR approach to antimicrobials. Part 3: First multi-tasking QSAR model for input-coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds. Bioorg Med Chem 16(11):5871–5880.  https://doi.org/10.1016/j.bmc.2008.04.068CrossRefPubMedGoogle Scholar
  60. 60.
    Prado-Prado FJ, Martinez de la Vega O, Uriarte E, Ubeira FM, Chou KC, Gonzalez-Diaz H (2009) Unified QSAR approach to antimicrobials. 4. Multi-target QSAR modeling and comparative multi-distance study of the giant components of antiviral drug-drug complex networks. Bioorg Med Chem 17(2):569–575.  https://doi.org/10.1016/j.bmc.2008.11.075CrossRefPubMedGoogle Scholar
  61. 61.
    Cruz-Monteagudo M, Borges F, Cordeiro MN, CagideFajin JL, Morell C, Ruiz RM, Canizares-Carmenate Y, Dominguez ER (2008) Desirability-based methods of multiobjective optimization and ranking for global QSAR studies. Filtering safe and potent drug candidates from combinatorial libraries. J Comb Chem 10(6):897–913.  https://doi.org/10.1021/cc800115yCrossRefPubMedGoogle Scholar
  62. 62.
    Prado-Prado FJ, Garcia-Mera X, Gonzalez-Diaz H (2010) Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species. Bioorg Med Chem 18(6):2225–2231.  https://doi.org/10.1016/j.bmc.2010.01.068CrossRefPubMedGoogle Scholar
  63. 63.
    Speck-Planche A, Cordeiro M (2017) Advanced in silico approaches for drug discovery: mining information from multiple biological and chemical data through mtk-QSBER and pt-QSPR strategies. Curr Med Chem 24(16):1687–1704.  https://doi.org/10.2174/0929867324666170124152746CrossRefPubMedGoogle Scholar
  64. 64.
    Gonzalez-Diaz H, Aguero G, Cabrera MA, Molina R, Santana L, Uriarte E, Delogu G, Castanedo N (2005) Unified Markov thermodynamics based on stochastic forms to classify drugs considering molecular structure, partition system, and biological species: distribution of the antimicrobial G1 on rat tissues. Bioorg Med Chem Lett 15(3):551–557.  https://doi.org/10.1016/j.bmcl.2004.11.059CrossRefPubMedGoogle Scholar
  65. 65.
    Lagunin A, Zakharov A, Filimonov D, Poroikov V (2011) QSAR modelling of rat acute toxicity on the basis of PASS prediction. Mol Inform 30(2–3):241–250.  https://doi.org/10.1002/minf.201000151CrossRefPubMedGoogle Scholar
  66. 66.
    Allen MP (2004) Introduction of molecular dynamics simulation. In: Attig N, Binder K, Grubmuller H, Kremer K (eds) Computational soft matter: from synthetic polymers to proteins, Lecture notes, NIC series, vol 23. John von Neumann Institute for Computing, Julich, pp 1–28Google Scholar
  67. 67.
    Sagui C, Darden TA (1999) Molecular dynamics simulations of biomolecules: long-range electrostatic effects. Annu Rev Biophys Biomol Struct 28:155–179.  https://doi.org/10.1146/annurev.biophys.28.1.155CrossRefPubMedGoogle Scholar
  68. 68.
    Verlet L (1967) Computer “experiments” on classical fluids. I. Thermodynamical properties of Lennard-Jones molecules. Phys Rev 159(1):98CrossRefGoogle Scholar
  69. 69.
    Atkins P, Paula J (2006) Physical chemistry for the life sciences. W H Freeman & Co, New YorkGoogle Scholar
  70. 70.
    Zhao H, Caflisch A (2015) Molecular dynamics in drug design. Eur J Med Chem 91:4–14.  https://doi.org/10.1016/j.ejmech.2014.08.004CrossRefPubMedGoogle Scholar
  71. 71.
    Hospital A, Goñi JR, Orozco M, Gelpí JL (2015) Molecular dynamics simulations: advances and applications. Adv Appl Bioinform Chem 8:37–47.  https://doi.org/10.2147/AABC.S70333CrossRefPubMedPubMedCentralGoogle Scholar
  72. 72.
    Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, Chipot C, Skeel RD, Kale L, Schulten K (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26(16):1781–1802.  https://doi.org/10.1002/jcc.20289CrossRefPubMedPubMedCentralGoogle Scholar
  73. 73.
    Berendsen HJC, van der Spoel D, van Drunen R (1995) GROMACS: a message-passing parallel molecular dynamics implementation. Comput Phys Commun 91(1–3):43–56.  https://doi.org/10.1016/0010-4655(95)00042-eCrossRefGoogle Scholar
  74. 74.
    Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M (1983) CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 4(2):187–217.  https://doi.org/10.1002/jcc.540040211CrossRefGoogle Scholar
  75. 75.
    Case DA, Cerutti DS, Cheatham TE III, Darden TA, Duke RE, Giese TJ, Gohlke H, Goetz AW, Greene D, Homeyer N, Izadi S, Kovalenko A, Lee TS, LeGrand S, Li P, Lin C, Liu J, Luchko T, Luo R, Mermelstein D, Merz KM, Monard G, Nguyen H, Omelyan I, Onufriev A, Pan F, Qi R, Roe DR, Roitberg A, Sagui C, Simmerling CL, Botello-Smith WM, Swails J, Walker RC, Wang J, Wolf RM, Wu X, Xiao L, York DM, Kollman PA (2017) AMBER 2017, 17th edn. University of California, San FranciscoGoogle Scholar
  76. 76.
    Scott WRP, Hünenberger PH, Tironi IG, Mark AE, Billeter SR, Fennen J, Torda AE, Huber T, Krüger P, van Gunsteren WF (1999) The GROMOS biomolecular simulation program package. J Phys Chem A 103(19):3596–3607.  https://doi.org/10.1021/jp984217fCrossRefGoogle Scholar
  77. 77.
    Lagardère L, Jolly L-H, Lipparini F, Aviat F, Stamm B, Jing ZF, Harger M, Torabifard H, Cisneros GA, Schnieders MJ, Gresh N, Maday Y, Ren PY, Ponder JW, Piquemal J-P (2018) Tinker-HP: a massively parallel molecular dynamics package for multiscale simulations of large complex systems with advanced point dipole polarizable force fields. Chem Sci 9(4):956–972.  https://doi.org/10.1039/c7sc04531jCrossRefPubMedGoogle Scholar
  78. 78.
    Harger M, Li D, Wang Z, Dalby K, Lagardere L, Piquemal JP, Ponder J, Ren P (2017) Tinker-OpenMM: absolute and relative alchemical free energies using AMOEBA on GPUs. J Comput Chem 38(23):2047–2055.  https://doi.org/10.1002/jcc.24853CrossRefPubMedPubMedCentralGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Centre of Advanced Study, Department of ChemistryUniversity of RajasthanJaipurIndia
  2. 2.Amity Institute of Forensic SciencesAmity UniversityNoidaIndia

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