Advertisement

Combinatorial Designing of Novel Lead Molecules Towards the Putative Drug Targets of Extreme Drug-Resistant Mycobacterium tuberculosis: A Future Insight for Molecular Medicine

  • Nikhil Bachappanavar
  • Sinosh SkariyachanEmail author
Chapter

Abstract

Mycobacterium tuberculosis (Mtb) is one of the notorious pathogens which has led to high mortality rates and demonstrated extreme drug resistance (XDR) to most of the conventional drugs and become a potential threat to public health worldwide. Hence, there is high demand and need to screen novel drug targets and alternate lead molecules that can be used as starting point of developing potential therapies against this pathogen. The proposed chapter illustrates the application of computer-aided virtual screening for screening novel and probable drug targets of Mycobacterium tuberculosis and identification of novel lead molecules as therapeutic remedies by computational biology tools and approaches. The chapter initially focuses on the recent perspectives on XDR-Mtb, major metabolic pathways responsible for the pathogenesis, conventional therapies and associated drug resistance and challenges and scope of computational drug screening. This chapter further illustrates potential drug targets, various approaches for the prediction of these targets, molecular modelling works, screening of novel lead molecules by computational virtual screening with ideal drug likeliness and ADMET (absorption, distribution, metabolism, excretion and toxicity) features, application of docking studies and simulation. Thus, the present chapter provides latest developments in molecular medicine and computational drug discovery to combat tuberculosis (TB) and thereby open new paradigm for the development of novel leads against potential drug targets for XDR-Mtb.

Keywords

Mycobacterium tuberculosis Extreme drug resistance Novel drug targets Computer-aided virtual screening Molecular modelling Novel natural leads 

References

  1. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E (2015) GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX.  https://doi.org/10.1016/j.softx.2015.06.001CrossRefGoogle Scholar
  2. Agyeman AA, Ofori-Asenso R (2016) Efficacy and safety profile of linezolid in the treatment of multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis: a systematic review and meta-analysis. Ann Clin Microbiol Antimicrob 15(1):41PubMedPubMedCentralCrossRefGoogle Scholar
  3. Alderwick LJ, Seidel M, Sahm H, Besra GS, Eggeling L (2006) Identification of a novel arabinofuranosyltransferase (AftA) involved in cell wall arabinan biosynthesis in Mycobacterium tuberculosis. J Biol Chem 281(23):15653–15661PubMedCrossRefGoogle Scholar
  4. Almeida-Da-Silva PE, Palomino JC (2011) Molecular basis and mechanisms of drug resistance in Mycobacterium tuberculosis: classical and new drugs. J Antimicrob Chemother 66(7):1417–1430PubMedCrossRefGoogle Scholar
  5. Amir A, Rana K, Arya A, Kapoor N, Kumar H, Siddiqui MA (2014) Mycobacterium tuberculosis H37Rv: in silico drug targets identification by metabolic pathways analysis. Int J Evol Biol 2014:284170PubMedPubMedCentralCrossRefGoogle Scholar
  6. Anastasio TJ (2017) Editorial: computational and experimental approaches in multi-target pharmacology. Front Pharmacol 8:443PubMedPubMedCentralCrossRefGoogle Scholar
  7. Averbukh I, Ben-Zvi D, Mishra S, Barkai N (2014) Scaling morphogen gradients during tissue growth by a cell division rule. Development 141(10):2150–2156PubMedCrossRefGoogle Scholar
  8. Ayaz F, Küçükboyacı N, Demirci B (2017) Chemical composition and antimicrobial activity of the essential oil of Conyza canadensis (L.) cronquist from Turkey. J Essent Oil Res 29(4):336–343CrossRefGoogle Scholar
  9. Baek M, Shin WH, Chung HW, Seok C (2017) GalaxyDock BP2 score: a hybrid scoring function for accurate protein-ligand docking. J Comput Aided Mol Des 31(7):653–666PubMedCrossRefGoogle Scholar
  10. Baldi A (2010) Computational approaches for drug design and discovery: an overview. Sys Rev Pharm 1(1):95–105CrossRefGoogle Scholar
  11. Bashiri G, Rehan AM, Sreebhavan S, Baker HM, Baker EN, Squire CJ (2016) Elongation of the poly-γ-glutamate tail of F420 requires both domains of the F420:γ-glutamyl ligase (FbiB) of Mycobacterium tuberculosis. J Biol Chem 291(13):6882–6894PubMedPubMedCentralCrossRefGoogle Scholar
  12. Bates PA, Kelley LA, MacCallum RM, Sternberg MJ (2001) Enhancement of protein modeling by human intervention in applying the automatic programs 3D-JIGSAW and 3D-PSSM. Proteins 5:39–46PubMedCrossRefGoogle Scholar
  13. Baugh L, Phan I, Begley DW, Clifton MC, Armour B, Dranow DM, Taylor BM, Muruthi MM, Abendroth J, Fairman JW, Fox D 3rd, Dieterich SH, Staker BL, Gardberg AS, Choi R, Hewitt SN, Napuli AJ, Myers J, Barrett LK, Zhang Y, Ferrell M, Mundt E, Thompkins K, Tran N, Lyons-Abbott S, Abramov A, Sekar A, Serbzhinskiy D, Lorimer D, Buchko GW, Stacy R, Stewart LJ, Edwards TE, Van Voorhis WC, Myler PJ (2015) Increasing the structural coverage of tuberculosis drug targets. Tuberculosis (Edinb) 95(2):142–148CrossRefGoogle Scholar
  14. Belanger AE, Besra GS, Ford ME, Mikusová K, Belisle JT, Brennan PJ, Inamine JM (1996) The embAB genes of Mycobacterium avium encode an arabinosyl transferase involved in cell wall arabinan biosynthesis that is the target for the antimycobacterial drug ethambutol. Proc Natl Acad Sci U S A 93(21):11919–11924PubMedPubMedCentralCrossRefGoogle Scholar
  15. Bell LCK, Noursadeghi M (2018) Pathogenesis of HIV-1 and Mycobacterium tuberculosis co-infection. Nat Rev Microbiol 16(2):80–90PubMedCrossRefGoogle Scholar
  16. Berrada ZL, Lin SY, Rodwell TC, Nguyen D, Schecter GF, Pham L, Janda JM, Elmaraachli W, Catanzaro A, Desmond E (2016) Rifabutin and rifampin resistance levels and associated rpoB mutations in clinical isolates of Mycobacterium tuberculosis complex. Diagn Microbiol Infect Dis 85(2):177–181PubMedPubMedCentralCrossRefGoogle Scholar
  17. Bickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL (2012) Quantifying the chemical beauty of drugs. Nat Chem 4(2):90–98PubMedPubMedCentralCrossRefGoogle Scholar
  18. Blaszczyk M, Jamroz M, Kmiecik S, Kolinski A (2013) CABS-fold: server for the de novo and consensus-based prediction of protein structure. Nucleic Acids Res 41(Web Server issue):W406–W411PubMedPubMedCentralCrossRefGoogle Scholar
  19. Bohm HJ (1992) The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J Comput Aided Mol Des 6(1):61–78PubMedCrossRefGoogle Scholar
  20. Bradley P, Misura KM, Baker D (2005) Toward high-resolution de novo structure prediction for small proteins. Science 309(5742):1868–1871CrossRefGoogle Scholar
  21. Brooks BR, Brooks CL, Mackerell AD Jr, Nilsson L, Petrella RJ, Roux B, Won Y, Archontis G, Bartels C, Boresch S, Caflisch A, Caves L, Cui Q, Dinner AR, Feig M, Fischer S, Gao J, Hodoscek M, Im W, Kuczera K, Lazaridis T, Ma J, Ovchinnikov V, Paci E, Pastor RW, Post CB, Pu JZ, Schaefer M, Tidor B, Venable RM, Woodcock HL, Wu X, Yang W, York DM, Karplus M (2009) CHARMM: the biomolecular simulation program. J Comput Chem 30(10):1545–1614PubMedPubMedCentralCrossRefGoogle Scholar
  22. Bruning JB, Murillo AC, Chacon O, Barletta RG, Sacchettini JC (2011) Structure of the Mycobacterium tuberculosis D-alanine:D-alanine ligase, a target of the antituberculosis drug D-cycloserine. Antimicrob Agents Chemother 55(1):291–301PubMedCrossRefGoogle Scholar
  23. Brylinski M, Skolnick J (2008) Q-Dock: low-resolution flexible ligand docking with pocket-specific threading restraints. J Biol Chem 29(10):1574–1588Google Scholar
  24. Burkhard P, Taylor P, Walkinshaw MD (1998) An example of a protein ligand found by database mining: description of the docking method and its verification by a 2.3 A X-ray structure of a thrombinligand complex. J Mol Biol 277(2):449–466PubMedCrossRefGoogle Scholar
  25. Bushra E, Adem J (2016) Mycobacterial metabolic pathways as drug targets: a review. Int J Microbiol Res 7(3):74–87Google Scholar
  26. Case DA, Cerutti DS, Cheatham TE, 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. University of California, San FranciscoGoogle Scholar
  27. Centers for Disease Control and Prevention (CDC). (2018) https://www.cdc.gov/tb/topic/research/default.htm. Accessed 10 Apr 2018
  28. Chambers HF, Turner J, Schecter GF, Kawamura M, Hopewell PC (2005) Imipenem for treatment of tuberculosis in mice and humans. Antimicrob Agents Chemother 49(7):2816–2821PubMedPubMedCentralCrossRefGoogle Scholar
  29. Chandra N (2011) Computational approaches for drug target identification in pathogenic diseases. Expert Opin Drug Discov 6(10):975–979PubMedCrossRefGoogle Scholar
  30. Chang DT, Oyang YJ, Lin JH (2005) MEDock: a web server for efficient prediction of ligand binding sites based on a novel optimization algorithm. Nucleic Acids Res 33(Web Server issue):W233–W238PubMedPubMedCentralCrossRefGoogle Scholar
  31. Chaudhary KK, Mishra N (2016) A review on molecular docking: novel tool for drug discovery. JSM Chem 4(3):1029Google Scholar
  32. Chen HM, Liu BF, Huang HL, Hwang SF, Ho SY (2007) SODOCK: swarm optimization for highly flexible protein-ligand docking. J Biol Chem 28(2):612–623Google Scholar
  33. Chen J, Zhang S, Cui P, Shi W, Zhang W, Zhang Y (2017) Identification of novel mutations associated with cycloserine resistance in Mycobacterium tuberculosis. J Antimicrob Chemother 72(12):3272–3276PubMedPubMedCentralCrossRefGoogle Scholar
  34. Cheng T, Li Q, Zhou Z, Wang Y, Bryant SH (2012) Structure-based virtual screening for drug discovery: a problem-centric review. AAPS J 14(1):133–141PubMedPubMedCentralCrossRefGoogle Scholar
  35. Chinsembu KC (2016) Tuberculosis and nature’s pharmacy of putative anti-tuberculosis agents. Acta Trop 153:46–56PubMedCrossRefPubMedCentralGoogle Scholar
  36. Choi V (2005) YUCCA: an efficient algorithm for small-molecule docking. Chem Biodivers 2(11):1517–1524PubMedCrossRefPubMedCentralGoogle Scholar
  37. Chung JY, Cho SJ, Hah JM (2011) A python-based docking program utilizing a receptor bound ligand shape: PythDock. Arch Pharm Res 34(9):1451–1458PubMedCrossRefGoogle Scholar
  38. Clark KP (1995) Flexible ligand docking without parameter adjustment across four ligand-receptor complexes. J Comput Chem 16:1210–1226CrossRefGoogle Scholar
  39. Clark DE (2003) In-silico prediction of blood–brain barrier permeation. Drug Discov Today 8(20):927–933PubMedCrossRefGoogle Scholar
  40. Cole ST, Brosch R, Parkhill J, Garnier T, Churcher C, Harris D, Gordon SV, Eiglmeier K, Gas S, Barry CE 3rd, Tekaia F, Badcock K, Basham D, Brown D, Chillingworth T, Connor R, Davies R, Devlin K, Feltwell T, Gentles S, Hamlin N, Holroyd S, Hornsby T, Jagels K, Krogh A, McLean J, Moule S, Murphy L, Oliver K, Osborne J, Quail MA, Rajandream MA, Rogers J, Rutter S, Seeger K, Skelton J, Squares R, Squares S, Sulston JE, Taylor K, Whitehead S, Barrell BG (1998) Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature 393(6685):537–544CrossRefGoogle Scholar
  41. Coll F, Phelan J, Hill-Cawthorne GA, Nair MB, Mallard K, Ali S, Abdallah AM, Alghamdi S, Alsomali M, Ahmed AO, Portelli S, Oppong Y, Alves A, Bessa TB, Campino S, Caws M, Chatterjee A, Crampin AC, Dheda K, Furnham N, Glynn JR, Grandjean L, Minh-Ha D, Hasan R, Hasan Z, Hibberd ML, Joloba M, Jones-López EC, Matsumoto T, Miranda A, Moore DJ, Mocillo N, Panaiotov S, Parkhill J, Penha C, Perdigão J, Portugal I, Rchiad Z, Robledo J, Sheen P, Shesha NT, Sirgel FA, Sola C, Oliveira Sousa E, Streicher EM, Helden PV, Viveiros M, Warren RM, McNerney R, Pain A, Clark TG (2018) Genome-wide analysis of multi- and extensively drug-resistant Mycobacterium tuberculosis. Nat Genet 50(2):307–316PubMedCrossRefPubMedCentralGoogle Scholar
  42. D’Ambrosio L, Centis R, Tiberi S, Tadolini M, Dalcolmo M, Rendon A, Esposito S, Migliori GB (2017) Delamanid and bedaquiline to treat multidrug-resistant and extensively drug-resistant tuberculosis in children: a systematic review. J Thorac Dis 9(7):2093–2101PubMedPubMedCentralCrossRefGoogle Scholar
  43. Dar AM, Mir S (2017) Molecular docking: approaches, types, applications and basic challenges. J Anal Bioanal Tech 8:356CrossRefGoogle Scholar
  44. de-Mendonça JD, Ely F, Palma MS, Frazzon J, Basso LA, Santos DS (2007) Functional characterization by genetic complementation of aroB-encoded dehydroquinate synthase from Mycobacterium tuberculosis H37Rv and its heterologous expression and purification. J Bacteriol 189(17):6246–6252PubMedPubMedCentralCrossRefGoogle Scholar
  45. de-Ruyck J, Brysbaert G, Blossey R, Lensink MF (2016) Molecular docking as a popular tool in drug design, an in silico travel. Adv Appl Bioinform Chem 9:1–11PubMedPubMedCentralGoogle Scholar
  46. De-Vivo M, Masetti M, Bottegoni G, Cavalli A (2016) Role of molecular dynamics and related methods in drug discovery. J Med Chem 59(9):4035–4061PubMedCrossRefPubMedCentralGoogle Scholar
  47. Dheda K, Chang KC, Guglielmetti L, Furin J, Schaaf HS, Chesov D, Esmail A, Lange C (2017) Clinical management of adults and children with multidrug-resistant and extensively drug-resistant tuberculosis. Clin Microbiol Infect 23(3):131–140PubMedCrossRefPubMedCentralGoogle Scholar
  48. Dominguez C, Boelens R, Bonvin AM (2003) HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. J Am Chem Soc 125(7):1731–1737CrossRefGoogle Scholar
  49. Dookie N, Rambaran S, Padayatchi N, Mahomed S, Naidoo K (2018) Evolution of drug resistance in Mycobacterium tuberculosis: a review on the molecular determinants of resistance and implications for personalized care. J Antimicrob Chemother.  https://doi.org/10.1093/jac/dkx506PubMedPubMedCentralCrossRefGoogle Scholar
  50. Dundas J, Ouyang Z, Tseng J, Binkowski A, Turpaz Y, Liang J (2006) CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Res 34(Web Server issue):W116–W118PubMedPubMedCentralCrossRefGoogle Scholar
  51. Durrant JD, McCammon JA (2011) Molecular dynamics simulations and drug discovery. BMC Biol 9:71PubMedPubMedCentralCrossRefGoogle Scholar
  52. Engin HB, Gursoy A, Nussinov R, Keskin O (2014) Network-based strategies can help mono- and poly-pharmacology drug discovery: a systems biology view. Curr Pharm Des 20(8):1201–1207PubMedCrossRefGoogle Scholar
  53. Errey JC, Blanchard JS (2005) Functional characterization of a novel ArgA from Mycobacterium tuberculosis. J Bacteriol 187(9):3039–3044PubMedPubMedCentralCrossRefGoogle Scholar
  54. European Center for Disease Prevention and Control (ECDC). (2018) https://ecdc.europa.eu/en/publications-data/tuberculosis-surveillance-and-monitoring-europe-2018. Accessed 10 Apr 2018
  55. Ewing TJA, Makino S, Skillman AG, Kuntz ID (2001) DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Des 15(5):411–428PubMedCrossRefGoogle Scholar
  56. Fakhar Z, Naiker S, Alves CN, Govender T, Maguire GE, Lameira J, Lamichhane G, Kruger HG, Honarparvar B (2016) A comparative modeling and molecular docking study on Mycobacterium tuberculosis targets involved in peptidoglycan biosynthesis. J Biomol Struct Dyn 34(11):2399–2417PubMedCrossRefGoogle Scholar
  57. Fan H, Schneidman-Duhovny D, Irwin JJ, Dong G, Shoichet BK, Sali A (2011) Statistical potential for modeling and ranking of protein–ligand interactions. J Chem Inf Model 51(12):3078–3092PubMedPubMedCentralCrossRefGoogle Scholar
  58. Ferraris DM, Spallek R, Oehlmann W, Singh M, Rizzi M (2015) Structures of citrate synthase and malate dehydrogenase of Mycobacterium tuberculosis. Proteins 83(2):389–394PubMedCrossRefGoogle Scholar
  59. Ferreira LG, Dos Santos RN, Oliva G, Andricopulo AD (2015) Molecular docking and structure-based drug design strategies. Molecules 20(7):13384–13421PubMedPubMedCentralCrossRefGoogle Scholar
  60. Field SK (2015) Bedaquiline for the treatment of multidrug-resistant tuberculosis: great promise or disappointment? Ther Adv Chronic Dis 6(4):170–184PubMedPubMedCentralCrossRefGoogle Scholar
  61. Fleischmann RD, Alland D, Eisen JA, Carpenter L, White O, Peterson J, DeBoy R, Dodson R, Gwinn M, Haft D, Hickey E, Kolonay JF, Nelson WC, Umayam LA, Ermolaeva M, Salzberg SL, Delcher A, Utterback T, Weidman J, Khouri H, Gill J, Mikula A, Bishai W, Jacobs WR Jr, Venter JC, Fraser CM (2002) Whole-genome comparison of Mycobacterium tuberculosis clinical and laboratory strains. J Bacteriol 184(19):5479–5490PubMedPubMedCentralCrossRefGoogle Scholar
  62. Forrellad MA, Klepp LI, Gioffré A, Sabio-y-García J, Morbidoni HR, de la Paz Santangelo M, Cataldi AA, Bigi F (2013) Virulence factors of the Mycobacterium tuberculosis complex. Virulence 4(1):3–66PubMedPubMedCentralCrossRefGoogle Scholar
  63. Gabb HA, Jackson RM, Sternberg MJ (1997) Modelling protein docking using shape complementarity, electrostatics and biochemical information. J Mol Biol 272(1):106–120PubMedCrossRefGoogle Scholar
  64. Gao Z, Li H, Zhang H, Liu X, Kang L, Luo X, Zhu W, Chen K, Wang X, Jiang H (2008) PDTD: a web-accessible protein database for drug target identification. BMC Bioinformatics 9:104PubMedPubMedCentralCrossRefGoogle Scholar
  65. Gaudreault F, Najmanovich RJ (2015) FlexAID: revisiting docking on non-native-complex structures. J Chem Inf Model 55(7):1323–1336PubMedCrossRefGoogle Scholar
  66. Geromichalos GD (2012) Virtual screening strategies and application in drug designing. Drug Des 2:e109CrossRefGoogle Scholar
  67. Ghose AK, Herbertz T, Salvino JM, Mallamo JP (2006) Knowledge-based chemoinformatic approaches to drug discovery. Drug Discov Today 11(23–24):1107–1114PubMedCrossRefGoogle Scholar
  68. Gonzalo X, Drobniewski F (2013) Is there a place for β-lactams in the treatment of multidrug-resistant/extensively drug-resistant tuberculosis? Synergy between meropenem and amoxicillin/clavulanate. J Antimicrob Chemother 68(2):366–369PubMedCrossRefGoogle Scholar
  69. Graham DE, Xu H, White RH (2002) Identification of coenzyme M biosynthetic phosphosulfolactate synthase: a new family of sulfonate-biosynthesizing enzymes. J Biol Chem 277(16):13421–13429PubMedCrossRefGoogle Scholar
  70. Grochowski LL, Xu H, White RH (2008) Identification and characterization of the 2-phospho-L-lactate guanylyltransferase involved in coenzyme F420 biosynthesis. Biochemistry 47(9):3033–3037PubMedCrossRefGoogle Scholar
  71. Grosdidier A, Zoete V, Michielin O (2007) EADock: docking of small molecules into protein active sites with a multi objective evolutionary optimization. Proteins 67(4):1010–1025PubMedCrossRefGoogle Scholar
  72. Grosdidier A, Zoete V, Michielin O (2011) SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res 39.(Web Server issue:W270–W277PubMedPubMedCentralCrossRefGoogle Scholar
  73. Gupta A, Gandhimathi 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):632–646PubMedCrossRefGoogle Scholar
  74. Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47(7):1750–1759CrossRefPubMedGoogle Scholar
  75. Hallam SJ, Putnam N, Preston CM, Detter JC, Rokhsar D, Richardson PM, DeLong EF (2004) Reverse methanogenesis: testing the hypothesis with environmental genomics. Science 305(5689):1457–1462PubMedCrossRefGoogle Scholar
  76. Hart TN, Read RJ (1992) A multiple-start Monte Carlo docking method. Proteins 13(3):206–222PubMedCrossRefGoogle Scholar
  77. Hazai E, Kovács S, Demkó L, Bikádi Z (2009) DockingServer: molecular docking calculations online. Acta Pharm Hung 79(1):17–21PubMedGoogle Scholar
  78. Huang B (2009) MetaPocket: a meta approach to improve protein ligand binding site prediction. OMICS 13(4):325–330PubMedPubMedCentralCrossRefGoogle Scholar
  79. Huang SY, Li M, Wang J, Pan Y (2016) HybridDock: A hybrid protein-ligand docking protocol integrating protein- and ligand-based approaches. J Chem Inf Model 56(6):1078–1087PubMedCrossRefGoogle Scholar
  80. Hung CL, Chen CC (2014) Computational approaches for drug discovery. Drug Dev Res 75(6):412–418PubMedCrossRefGoogle Scholar
  81. Irwin JJ, Shoichet BK, Mysinger MM, Huang N, Colizzi F, Wassam P, Cao Y (2009) Automated docking screens: a feasibility study. J Med Chem 52(18):5712–5720PubMedPubMedCentralCrossRefGoogle Scholar
  82. Jabeen K, Shakoor S, Hasan R (2015) Fluoroquinolone-resistant tuberculosis: implications in settings with weak healthcare systems. Int J Infect Dis 32:118–123PubMedCrossRefGoogle Scholar
  83. Janardhan S, John L, Prasanthi M, Poroikov V, Narahari-Sastry G (2017) A QSAR and molecular modelling study towards new lead finding: polypharmacological approach to Mycobacterium tuberculosis. SAR QSAR Environ Res 28(10):815–832PubMedCrossRefGoogle Scholar
  84. Jiang F, Kim SH (1991) “Soft docking”: matching of molecular surface cubes. J Mol Biol 219(1):79–102PubMedCrossRefGoogle Scholar
  85. Jones DT (1999) GenTHREADER: an efficient and reliable protein folds recognition method for genomic sequences. J Mol Biol 287(4):797–815PubMedCrossRefGoogle Scholar
  86. Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267(3):727–748PubMedCrossRefGoogle Scholar
  87. Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M (2016) KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 44(D1):D457–D462CrossRefGoogle Scholar
  88. Kar S, Roy K (2013) How far can virtual screening take us in drug discovery? Expert Opin Drug Discov 8(3):245–261PubMedCrossRefGoogle Scholar
  89. Katsila T, Spyroulias GA, Patrinos GP, Matsoukas MT (2016) Computational approaches in target identification and drug discovery. Comput Struct Biotechnol J 14:177–184PubMedPubMedCentralCrossRefGoogle Scholar
  90. Kaur G, Pandey B, Grover A, Garewal N, Grover A, Kaur J (2018) Drug targeted virtual screening and molecular dynamics of LipU protein of Mycobacterium tuberculosis and Mycobacterium leprae. J Biomol Struct Dyn.  https://doi.org/10.1080/07391102.2018.1454852PubMedCrossRefGoogle Scholar
  91. Kelley BP, Brown SP, Warren GL, Muchmore SW (2015a) POSIT: flexible shape-guided docking for pose prediction. J Chem Inf Model 55(8):1771–1780PubMedCrossRefGoogle Scholar
  92. Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ (2015b) The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10(6):845–858PubMedPubMedCentralCrossRefGoogle Scholar
  93. Kim DS, Kim CM, Won CI, Kim JK, Ryu J, Cho Y, Bhak J (2011) BetaDock: shape-priority docking method based on beta-complex. J Biomol Struct Dyn 29(1):219–242PubMedCrossRefGoogle Scholar
  94. Kneidinger B, Marolda C, Graninger M, Zamyatina A, McArthur F, Kosma P, Valvano MA, Messner P (2002) Biosynthesis pathway of ADP-L-glycero-beta-D-manno-heptose in Escherichia coli. J Bacteriol 184(2):363–369PubMedPubMedCentralCrossRefGoogle Scholar
  95. Ko Y, Choi I (2016) Putative 3D structure of QcrB from Mycobacterium tuberculosis cytochrome bc1 complex, a novel drug-target for new series of antituberculosis agent Q203. Bull Kor Chem Soc 37:725–731CrossRefGoogle Scholar
  96. Korb O, Stützle T, Exner TE (2006) PLANTS: application of ant colony optimization to structure-based drug design. In: Dorigo M, Gambardella LM, Birattari M, Martinoli A, Poli R, Stützle T (eds) Ant colony optimization and swarm intelligence, vol 4150. Springer, Berlin, Heidelberg, pp 247–258CrossRefGoogle Scholar
  97. Krüüner A, Jureen P, Levina K, Ghebremichael S, Hoffner S (2003) Discordant resistance to kanamycin and amikacin in drug-resistant Mycobacterium tuberculosis. Antimicrob Agents Chemother 47(9):2971–2973PubMedPubMedCentralCrossRefGoogle Scholar
  98. Kumar P, Arora K, Lloyd JR, Lee IY, Nair V, Fischer E, Boshoff HI, Barry CE 3rd (2012) Meropenem inhibits D,D-carboxypeptidase activity in Mycobacterium tuberculosis. Mol Microbiol 86(2):367–381PubMedPubMedCentralCrossRefGoogle Scholar
  99. Laskowski RA (1995) SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions. J Mol Graph 13(5):323–330, 307–308PubMedCrossRefGoogle Scholar
  100. Laurie AT, Jackson RM (2005) Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites. Bioinformatics 21(9):1908–1916PubMedCrossRefGoogle Scholar
  101. Lee GR, Seok C (2016) Galaxy7TM: flexible GPCR-ligand docking by structure refinement. Nucleic Acids Res 44(W1):W502–W506PubMedPubMedCentralCrossRefGoogle Scholar
  102. Lee HS, Zhang Y (2012) BSP-SLIM: a blind low-resolution ligand-protein docking approach using predicted protein structures. Proteins 80(1):93–110PubMedCrossRefGoogle Scholar
  103. Leelananda SP, Lindert S (2016) Computational methods in drug discovery. Beilstein J Org Chem 12:2694–2718PubMedPubMedCentralCrossRefGoogle Scholar
  104. Leeson PD, Davis AM, Steele J (2004) Drug-like properties: guiding principles for design–or chemical prejudice? Drug Discov Today Technol 1(3):189–195PubMedCrossRefGoogle Scholar
  105. LeMagueres P, Im H, Ebalunode J, Strych U, Benedik MJ, Briggs JM, Kohn H, Krause KL (2005) The 1.9 A crystal structure of alanine racemase from Mycobacterium tuberculosis contains a conserved entryway into the active site. Biochemistry 44(5):1471–1481PubMedCrossRefGoogle Scholar
  106. Li YH, Yu CY, Li XX, Zhang P, Tang J, Yang Q, Fu T, Zhang X, Cui X, Tu G, Zhang Y, Li S, Yang F, Sun Q, Qin C, Zeng X, Chen Z, Chen YZ, Zhu F (2018) Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res 46(D1):D1121–D1127PubMedGoogle Scholar
  107. Lindorff-Larsen K, Piana S, Palmo K, Maragakis P, Klepeis JL, Dror RO, Shaw DE (2010) Improved side-chain torsion potentials for the amber ff99SB protein force field. Proteins 78(8):1950–1958PubMedPubMedCentralGoogle Scholar
  108. Lionta E, Spyrou G, Vassilatis DK, Cournia Z (2014) Structure-based virtual screening for drug discovery: principles, applications and recent advances. Curr Top Med Chem 14(16):1923–1938PubMedPubMedCentralCrossRefGoogle Scholar
  109. Lipinski CA (2004) Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol 1(4):337–341CrossRefGoogle Scholar
  110. Liu M, Wang S (1999) MCDOCK: a Monte Carlo simulation approach to the molecular docking problem. J Comput Aided Mol Des 13(5):435–451PubMedCrossRefGoogle Scholar
  111. Liu T, Tang GW, Capriotti E (2011) Comparative modeling: the state of the art and protein drug target structure prediction. Comb Chem High Throughput Screen 14(6):532–547PubMedCrossRefGoogle Scholar
  112. Liu X, Shi D, Zhou S, Liu H, Liu H, Yao X (2018) Molecular dynamics simulations and novel drug discovery. Expert Opin Drug Discov 13(1):23–37PubMedCrossRefGoogle Scholar
  113. London N, Raveh B, Cohen E, Fathi G, Schueler-Furman O (2011) Rosetta FlexPepDock web server--high resolution modeling of peptide-protein interactions. Nucleic Acids Res 39(Web Server issue):W249–W253PubMedPubMedCentralCrossRefGoogle Scholar
  114. Lone MY, Athar M, Gupta VK, Jha PC (2017a) Identification of Mycobacterium tuberculosis enoyl-acyl carrier protein reductase inhibitors: a combined in-silico and in-vitro analysis. J Mol Graph Model 76:172–180PubMedCrossRefGoogle Scholar
  115. Lone MY, Athar M, Gupta VK, Jha PC (2017b) Prioritization of natural compounds against Mycobacterium tuberculosis 3-dehydroquinate dehydratase: A combined in-silico and in-vitro study. Biochem Biophys Res Commun 491(4):1105–1111PubMedCrossRefPubMedCentralGoogle Scholar
  116. Lone MY, Manhas A, Athar M, Jha PC (2017c) Identification of InhA inhibitors: a combination of virtual screening, molecular dynamics simulations and quantum chemical studies. J Biomol Struct Dyn.  https://doi.org/10.1080/07391102.2017.1372313PubMedCrossRefGoogle Scholar
  117. Maganti L, OSDD Consortium, Ghoshal N (2015) 3D-QSAR studies and shape based virtual screening for identification of novel hits to inhibit MbtA in Mycobacterium tuberculosis. J Biomol Struct Dyn 33(2):344–364PubMedCrossRefGoogle Scholar
  118. Maitre T, Aubry A, Jarlier V, Robert J, Veziris N, CNR-MyRMA (2017) Multidrug and extensively drug-resistant tuberculosis. Med Mal Infect 47(1):3–10PubMedCrossRefGoogle Scholar
  119. Manikandan K, Geerlof A, Zozulya AV, Svergun DI, Weiss MS (2011) Structural studies on the enzyme complex isopropylmalate isomerase (LeuCD) from Mycobacterium tuberculosis. Proteins 79(1):35–49PubMedCrossRefGoogle Scholar
  120. Mansuri R, Ansari MY, Singh J, Rana S, Sinha S, Sahoo GC, Dikhit MR, Das P (2016) Computational elucidation of structural basis for ligand binding with Mycobacterium tuberculosis glucose-1-phosphate thymidylyltransferase (RmlA). Curr Pharm Biotechnol 17(12):1089–1099PubMedCrossRefGoogle Scholar
  121. Mao C, Shukla M, Larrouy-Maumus G, Dix FL, Kelley LA, Sternberg MJ, Sobral BW, de-Carvalho LP (2013) Functional assignment of Mycobacterium tuberculosis proteome revealed by genome-scale fold-recognition. Tuberculosis (Edinb) 93(1):40–46CrossRefGoogle Scholar
  122. Marialke J, Tietze S, Apostolakis J (2008) Similarity based docking. J Chem Inf Model 48(1):186–196PubMedCrossRefGoogle Scholar
  123. Marks DS, Colwell LJ, Sheridan R, Hopf TA, Pagnani A, Zecchina R, Sander C (2011) Protein 3D structure computed from evolutionary sequence variation. PLoS One 6(12):e28766PubMedPubMedCentralCrossRefGoogle Scholar
  124. Matteelli A, Roggi A, Carvalho AC (2014) Extensively drug-resistant tuberculosis: epidemiology and management. Clin Epidemiol 6:111–118PubMedPubMedCentralCrossRefGoogle Scholar
  125. Maus CE, Plikaytis BB, Shinnick TM (2005a) Mutation of tlyA confers capreomycin resistance in Mycobacterium tuberculosis. Antimicrob Agents Chemother 49(2):571–577PubMedPubMedCentralCrossRefGoogle Scholar
  126. Maus CE, Plikaytis BB, Shinnick TM (2005b) Molecular analysis of cross-resistance to capreomycin, kanamycin, amikacin, and viomycin in Mycobacterium tuberculosis. Antimicrob Agents Chemother 49(8):3192–3197PubMedPubMedCentralCrossRefGoogle Scholar
  127. McGann MR, Almond HR, Nicholls A, Grant JA, Brown FK (2003) Gaussian docking functions. Biopolymers 68(1):76–90PubMedCrossRefGoogle Scholar
  128. McMartin C, Bohacek RS (1997) QXP: powerful, rapid computer algorithms for structure-based drug design. J Comput Aided Mol Des 11(4):333–344PubMedCrossRefGoogle Scholar
  129. Mehra R, Rani C, Mahajan P, Vishwakarma RA, Khan IA, Nargotra A (2016) Computationally guided identification of novel Mycobacterium tuberculosis GlmU lnhibitory leads, their optimization, and in vitro validation. ACS Comb Sci 18(2):100–116PubMedCrossRefGoogle Scholar
  130. Meng XY, Zhang HX, Mezei M, Cui M (2011) Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des 7(2):146–157PubMedPubMedCentralCrossRefGoogle Scholar
  131. Miller MD, Kearsley SK, Underwood DJ, Sheridan RP (1994) FLOG: a system to select ‘quasi-flexible’ ligands complementary to a receptor of known three-dimensional structure. J Comput Aided Mol Des 8(2):153–174PubMedCrossRefPubMedCentralGoogle Scholar
  132. Mizutani MY, Tomioka N, Itai A (1994) Rational automatic search method for stable docking models of protein and ligand. J Mol Biol 243(2):310–326PubMedCrossRefGoogle Scholar
  133. Mohamad S, Ismail NN, Parumasivam T, Ibrahim P, Osman H, A Wahab H (2018) Antituberculosis activity, phytochemical identification of Costus speciosus (J. Koenig) Sm., Cymbopogon citratus (DC. Ex Nees) Stapf., and Tabernaemontana coronaria (L.) Willd. and their effects on the growth kinetics and cellular integrity of Mycobacterium tuberculosis H37Rv. BMC Complement Altern Med 18(1):5PubMedPubMedCentralCrossRefGoogle Scholar
  134. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19(14):1639–1662CrossRefGoogle Scholar
  135. Mukhopadhyay S, Nair S, Ghosh S (2012) Pathogenesis in tuberculosis: transcriptomic approaches to unraveling virulence mechanisms and finding new drug targets. FEMS Microbiol Rev 36(2):463–485PubMedCrossRefPubMedCentralGoogle Scholar
  136. Namasivayam V, Gunther R (2007) PSO@AUTODOCK: a fast flexible molecular docking program based on swarm intelligence. Chem Biol Drug Des 70(6):475–484PubMedCrossRefGoogle Scholar
  137. Naz S, Farooq U, Ali S, Sarwar R, Khan S, Abagyan R (2018) Identification of new benzamide inhibitor against α-subunit of tryptophan synthase from Mycobacterium tuberculosis through structure-based virtual screening, anti-tuberculosis activity and molecular dynamics simulations. J Biomol Struct Dyn.  https://doi.org/10.1080/07391102.2018.1448303PubMedCrossRefPubMedCentralGoogle Scholar
  138. Nazzaro F, Fratianni F, De Martino L, Coppola R, De-Feo V (2013) Effect of essential oils on pathogenic bacteria. Pharmaceuticals (Basel) 6(12):1451–1474CrossRefGoogle Scholar
  139. Njire M, Tan Y, Mugweru J, Wang C, Guo J, Yew W, Tan S, Zhang T (2016) Pyrazinamide resistance in Mycobacterium tuberculosis: review and update. Adv Med Sci 61(1):63–71PubMedCrossRefGoogle Scholar
  140. Oprea TI (2000) Property distribution of drug-related chemical databases. J Comput Aided Mol Des 14(3):251–264PubMedCrossRefPubMedCentralGoogle Scholar
  141. Pagadala NS, Syed K, Tuszynski J (2017) Software for molecular docking: a review. Biophys Rev 9(2):91–102PubMedPubMedCentralCrossRefGoogle Scholar
  142. Pandey B, Grover S, Tyagi C, Goyal S, Jamal S, Singh A, Kaur J, Grover A (2018) Dynamics of fluoroquinolones induced resistance in DNA gyrase of Mycobacterium tuberculosis. J Biomol Struct Dyn 36(2):362–375PubMedCrossRefGoogle Scholar
  143. Pang YP, Perola E, Xu K, Prendergast FG (2001) EUDOC: a computer program for identification of drug interaction sites in macromolecules and drug leads from chemical databases. J Comput Chem 22(15):1750–1771PubMedCrossRefPubMedCentralGoogle Scholar
  144. Paul DS, Gautham N (2016) MOLS 2.0: software package for peptide modeling and protein-ligand docking. J Mol Model 22(10):239PubMedCrossRefGoogle Scholar
  145. Pei JF, Wang Q, Liu ZM, Li QL, Yang K, Lai LH (2006) PSIDOCK: towards highly efficient and accurate flexible ligand docking. Proteins 62(4):934–946PubMedCrossRefGoogle Scholar
  146. Peng J, Xu J (2011) RaptorX: exploiting structure information for protein alignment by statistical inference. Proteins 10:161–171CrossRefGoogle Scholar
  147. 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(16):1781–1802PubMedPubMedCentralCrossRefGoogle Scholar
  148. Pippel M, Scharfe M, Meier R, Sippl W (2012) ParaDockS – an open source framework for molecular docking. J Cheminform.  https://doi.org/10.1186/1758-2946-4-S1-F3
  149. Plewczynski D, Łaźniewski M, von Grotthuss M, Rychlewski L, Ginalski K (2011) VoteDock: consensus docking method for prediction of protein-ligand interactions. J Comput Chem 32(4):568–581PubMedCrossRefGoogle Scholar
  150. Putri DU, Rintiswati N, Soesatyo MH, Haryana SM (2018) Immune modulation properties of herbal plant leaves: Phyllanthus niruri aqueous extract on immune cells of tuberculosis patient – in vitro study. Nat Prod Res 32(4):463–467PubMedCrossRefGoogle Scholar
  151. Pyrkov TV, Chugunov AO, Krylov NA, Nolde DE, Efremov RG (2009) PLATINUM: a web tool for analysis of hydrophobic/hydrophilic organization of biomolecular complexes. Bioinformatics 25(9):1201–1202CrossRefGoogle Scholar
  152. Qiu J, Zang S, Ma Y, Owusu L, Zhou L, Jiang T, Xin Y (2017) Homology modeling and identification of amino acids involved in the catalytic process of Mycobacterium tuberculosis serine acetyltransferase. Mol Med Rep 15(3):1343–1347PubMedCrossRefGoogle Scholar
  153. Quan D, Nagalingam G, Payne R, Triccas JA (2017) New tuberculosis drug leads from naturally occurring compounds. Int J Infect Dis 56:212–220PubMedCrossRefPubMedCentralGoogle Scholar
  154. Rajendran V, Sethumadhavan R (2014) Drug resistance mechanism of PncA in Mycobacterium tuberculosis. J Biomol Struct Dyn 32(2):209–221PubMedCrossRefPubMedCentralGoogle Scholar
  155. Raman K, Chandra N (2008) Mycobacterium tuberculosis interactome analysis unravels potential pathways to drug resistance. BMC Microbiol 8:234PubMedPubMedCentralCrossRefGoogle Scholar
  156. Raman K, Yeturu K, Chandra N (2008) targetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis. BMC Syst Biol 2:109PubMedPubMedCentralCrossRefGoogle Scholar
  157. Ramesh KV, Purohit M, Mekhala K, Krishnan M, Wagle K, Deshmukh S (2008) Modeling the interactions of herbal drugs to β-ketoacyl ACP synthase of Mycobacterium tuberculosis H37Rv. J Biomol Struct Dyn 25(5):481–493PubMedCrossRefPubMedCentralGoogle Scholar
  158. Reddy AS, Pati SP, Kumar PP, Pradeep HN, Sastry GN (2007) Virtual screening in drug discovery -- a computational perspective. Curr Protein Pept Sci 8(4):329–351PubMedCrossRefPubMedCentralGoogle Scholar
  159. Ruiz-Carmona S, Alvarez-Garcia D, Foloppe N, Garmendia-Doval AB, Juhos S, Schmidtke P et al (2014) rDock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids. PLoS Comput Biol 2014(10):e1003571CrossRefGoogle Scholar
  160. Saini DK, Tyagi JS (2005) High-throughput microplate phosphorylation assays based on DevR-DevS/Rv2027c 2-component signal transduction pathway to screen for novel antitubercular compounds. J Biomol Screen 10(3):215–224PubMedCrossRefPubMedCentralGoogle Scholar
  161. Sambandamurthy VK, Wang X, Chen B, Russell RG, Derrick S, Collins FM, Morris SL, Jacobs WR Jr (2002) A pantothenate auxotroph of Mycobacterium tuberculosis is highly attenuated and protects mice against tuberculosis. Nat Med 8(10):1171–1174PubMedCrossRefPubMedCentralGoogle Scholar
  162. Sanusi SB, Abu-Bakar MF, Mohamed M, Sabran SF, Mainasara MM (2017) Southeast Asian medicinal plants as a potential source of antituberculosis agent. Evid Based Complement Alternat Med 2017:7185649PubMedPubMedCentralCrossRefGoogle Scholar
  163. Sauton N, Lagorce D, Villoutreix BO, Miteva MA (2008) MSDOCK: accurate multiple conformation generator and rigid docking protocol for multi-step virtual ligand screening. BMC Bioinformatics 2008:9Google Scholar
  164. Schmidtke P, Bidon-Chanal A, Luque FJ, Barril X (2011) MDpocket: open-source cavity detection and characterization on molecular dynamics trajectories. Bioinformatics 27(23):3276–3285CrossRefPubMedGoogle Scholar
  165. Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ (2005) PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res 33(Web Server issue):W363–W367PubMedPubMedCentralCrossRefGoogle Scholar
  166. Schwede T, Kopp J, Guex N, Peitsch MC (2003) SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res 31(13):3381–3385PubMedPubMedCentralCrossRefGoogle Scholar
  167. Seidel M, Alderwick LJ, Birch HL, Sahm H, Eggeling L, Besra GS (2007) Identification of a novel arabinofuranosyltransferase AftB involved in a terminal step of cell wall arabinan biosynthesis in Corynebacterianeae, such as Corynebacterium glutamicum and Mycobacterium tuberculosis. J Biol Chem 282(20):14729–14740PubMedCrossRefGoogle Scholar
  168. Seifert M, Catanzaro D, Catanzaro A, Rodwell TC (2015) Genetic mutations associated with isoniazid resistance in Mycobacterium tuberculosis: a systematic review. PLoS One 10(3):e0119628PubMedPubMedCentralCrossRefGoogle Scholar
  169. Sengupta S, Roy D, Bandyopadhyay S (2015) Structural insight into Mycobacterium tuberculosis maltosyl transferase inhibitors: pharmacophore-based virtual screening, docking, and molecular dynamics simulations. J Biomol Struct Dyn 33(12):2655–2666PubMedCrossRefGoogle Scholar
  170. Shin WH, Heo L, Lee J, Ko J, Seok C, Lee J (2011) LigDock-CSA: protein-ligand docking using conformational space annealing. J Comput Chem 32(15):3226–3232PubMedCrossRefGoogle Scholar
  171. Shoichet BK (2004) Virtual screening of chemical libraries. Nature 432(7019):862–865PubMedPubMedCentralCrossRefGoogle Scholar
  172. Shukla R, Shukla H, Sonkar A, Pandey T, Tripathi T (2017) Structure-based screening and molecular dynamics simulations offer novel natural compounds as potential inhibitors of Mycobacterium tuberculosis isocitrate lyase. J Biomol Struct Dyn.  https://doi.org/10.1080/07391102.2017.1341337PubMedCrossRefGoogle Scholar
  173. Silva JRA, Bishai WR, Govender T, Lamichhane G, Maguire GEM, Kruger HG, Lameira J, Alves CN (2016) Targeting the cell wall of Mycobacterium tuberculosis: a molecular modeling investigation of the interaction of imipenem and meropenem with L,D-transpeptidase 2. J Biomol Struct Dyn 34(2):304–317PubMedCrossRefPubMedCentralGoogle Scholar
  174. Singh RK, Kefala G, Janowski R, Mueller-Dieckmann C, von Kries JP, Weiss MS (2005) The high-resolution structure of LeuB (Rv2995c) from Mycobacterium tuberculosis. J Mol Biol 346(1):1–11PubMedCrossRefGoogle Scholar
  175. Singh T, Biswas D, Jayaram B (2011) AADS – an automated active site identification, docking, and scoring protocol for protein targets based on physicochemical descriptors. J Chem Inf Model 51(10):2515–2527PubMedCrossRefGoogle Scholar
  176. Skariyachan S, Manjunath M, Bachappanavar N (2018) Screening of potential lead molecules against prioritised targets of multi-drug-resistant-Acinetobacter baumannii – insights from molecular docking, molecular dynamic simulations and in vitro assays. J Biomol Struct Dyn.  https://doi.org/10.1080/07391102.2018.1451387PubMedCrossRefGoogle Scholar
  177. Sneader W (1990) Chronology of drug introductions. Comp Med Chem 1:7–80Google Scholar
  178. Sobolev V, Wade RC, Vriend G, Edelman M (1996) Molecular docking using surface complementarity. Proteins 25(1):120–129PubMedCrossRefGoogle Scholar
  179. Söding J, Biegert A, Lupas AN (2005) The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res 33(Web Server issue):W244–W248PubMedPubMedCentralCrossRefGoogle Scholar
  180. Sopitthummakhun K, Thongpanchang C, Vilaivan T, Yuthavong Y, Chaiyen P, Leartsakulpanich U (2012) Plasmodium serine hydroxymethyltransferase as a potential anti-malarial target: inhibition studies using improved methods for enzyme production and assay. Malar J 11:194PubMedPubMedCentralCrossRefGoogle Scholar
  181. Spitzer R, Jain AN (2012) Surflex-Dock: docking benchmarks and real-world application. J Comput Aided Mol Des 26(6):687–699PubMedPubMedCentralCrossRefGoogle Scholar
  182. Stroganov OV, Novikov FN, Stroylov VS, Kulkov V, Chilov GG (2008) Lead finder: an approach to improve accuracy of protein-ligand docking, binding energy estimation, and virtual screening. J Chem Inf Model 48(12):2371–2385PubMedCrossRefGoogle Scholar
  183. Sun H, Zhang C, Xiang L, Pi R, Guo Z, Zheng C, Li S, Zhao Y, Tang K, Luo M, Rastogi N, Li Y, Sun Q (2016) Characterization of mutations in streptomycin-resistant Mycobacterium tuberculosis isolates in Sichuan, China and the association between Beijing-lineage and dual-mutation in gidB. Tuberculosis (Edinb) 96:102–106CrossRefGoogle Scholar
  184. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, Jensen LJ, von-Mering C (2017) The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res 45(D1):D362–D368PubMedCrossRefGoogle Scholar
  185. Tan KP, Nguyen TB, Patel S, Varadarajan R, Madhusudhan MS (2013) Depth: a web server to compute depth, cavity sizes, detect potential small-molecule ligand-binding cavities and predict the pKa of ionizable residues in proteins. Nucleic Acids Res 41(Web Server issue):W314–W321PubMedPubMedCentralCrossRefGoogle Scholar
  186. Tan Y, Su B, Zheng H, Song Y, Wang Y, Pang Y (2017) Molecular characterization of prothionamide-resistant Mycobacterium tuberculosis isolates in southern China. Front Microbiol 8:2358PubMedPubMedCentralCrossRefGoogle Scholar
  187. Taylor JS, Burnett RM (2000) DARWIN: a program for docking flexible molecules. Proteins 41(2):173–191PubMedCrossRefGoogle Scholar
  188. Taylor RD, Jewsbury PJ, Essex JW (2003) FDS: flexible ligand and receptor docking with a continuum solvent model and soft-core energy function. J Comput Chem 24(13):1637–1656PubMedCrossRefGoogle Scholar
  189. Thomsen R, Christensen MH (2006) MolDock: a new technique for high-accuracy molecular docking. J Med Chem 49(11):3315–3321CrossRefPubMedGoogle Scholar
  190. Totrov M, Abagyan R (1997) Flexible protein-ligand docking by global energy optimization in internal coordinates. Proteins 1:215–220PubMedCrossRefGoogle Scholar
  191. Trosset JY, Scheraga HA (1999) Prodock: software package for protein modeling and docking. J Comput Chem 20(4):412–427CrossRefGoogle Scholar
  192. 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):455–461PubMedPubMedCentralGoogle Scholar
  193. Tsai TY, Chang KW, Chen CY (2011) iScreen: world’s first cloud-computing web server for virtual screening and de novo drug design based on TCM database@Taiwan. J Comput Aided Mol Des 25(6):525–531PubMedCrossRefGoogle Scholar
  194. Usha T, Shanmugarajan D, Goyal AK, Kumar CS, Middha SK (2017) Recent updates on computer-aided drug discovery: time for a paradigm shift. Curr Top Med Chem 17(30):3296–3307PubMedCrossRefGoogle Scholar
  195. Valvano MA, Messner P, Kosma P (2002) Novel pathways for biosynthesis of nucleotide-activated glycero-manno-heptose precursors of bacterial glycoproteins and cell surface polysaccharides. Microbiology 148(Pt 7):1979–1989PubMedCrossRefGoogle Scholar
  196. Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45(12):2615–2623CrossRefGoogle Scholar
  197. Venkatachalam CM, Jiang X, Oldfield T, Waldman M (2003) LigandFit: a novel method for the shape directed rapid docking of ligands to protein active-sites. J Mol Graph Model 21(4):289–307PubMedCrossRefGoogle Scholar
  198. Vidyaraj CK, Chitra A, Smita S, Muthuraj M, Govindarajan S, Usharani B, Anbazhagi S (2017) Prevalence of rifampicin-resistant Mycobacterium tuberculosis among human-immunodeficiency-virus-seropositive patients and their treatment outcomes. J Epidemiol Glob Health 7(4):289–294PubMedCrossRefGoogle Scholar
  199. Vilar S, Cozza G, Moro S (2008) Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery. Curr Top Med Chem 8(18):1555–1572PubMedCrossRefGoogle Scholar
  200. Vilchèze C, Jacobs WR Jr (2014) Resistance to isoniazid and ethionamide in Mycobacterium tuberculosis: genes, mutations, and causalities. Microbiol Spectr 2(4):MGM2-0014-2013PubMedPubMedCentralCrossRefGoogle Scholar
  201. Vyas V, Jain A, Jain A, Gupta A (2008) Virtual screening: a fast tool for drug design. Sci Pharm 76(3):333–360CrossRefGoogle Scholar
  202. Vyas VK, Ukawala RD, Ghate M, Chintha C (2012) Homology modeling a fast tool for drug discovery: current perspectives. Indian J Pharm Sci 74(1):1–17PubMedPubMedCentralCrossRefGoogle Scholar
  203. Wagener M, Jd V, Nabuurs SB (2012) Flexible protein-ligand docking using the Fleksy protocol. J Comput Chem 33(12):1215–1217PubMedPubMedCentralGoogle Scholar
  204. Wang JC, Chu PY, Chen CM, Lin JH (2012) idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach. Nucleic Acids Res 40(Web Server issue):W393–W399PubMedPubMedCentralCrossRefGoogle Scholar
  205. Webb B, Sali A (2017) Protein structure modeling with MODELLER. Methods Mol Biol 1654:39–54PubMedCrossRefGoogle Scholar
  206. Welch W, Ruppert J, Jain AN (1996) Hammerhead: fast, fully automated docking of flexible ligands to protein binding sites. Chem Biol 3(6):449–462PubMedCrossRefGoogle Scholar
  207. Wink M (2015) Modes of action of herbal medicines and plant secondary metabolites. Medicines (Basel) 2(3):251–286PubMedCentralCrossRefPubMedGoogle Scholar
  208. World Health Organization (WHO). (2018) http://www.who.int/tb/en/. Accessed 10 Apr 2018
  209. Wu S, Zhang Y (2008) MUSTER: improving protein sequence profile-profile alignments by using multiple sources of structure information. Proteins 72(2):547–556PubMedPubMedCentralCrossRefGoogle Scholar
  210. Xu D, Zhang Y (2012) Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field. Proteins 80(7):1715–1735PubMedPubMedCentralGoogle Scholar
  211. Yan RX, Si JN, Wang C, Zhang Z (2009) DescFold: a web server for protein fold recognition. BMC Bioinformatics 10:416PubMedPubMedCentralCrossRefGoogle Scholar
  212. Yang JM, Chen CC (2004) GEMDOCK: a generic evolutionary method for molecular docking. Proteins 55(2):288–304PubMedCrossRefGoogle Scholar
  213. Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y (2015) The I-TASSER suite: protein structure and function prediction. Nat Methods 12(1):7–8PubMedPubMedCentralCrossRefGoogle Scholar
  214. Zhao Y, Sanner MF (2007) FLIPDock: docking flexible ligands into flexible receptors. Proteins 68(3):726–737PubMedCrossRefGoogle Scholar
  215. Zhao LL, Sun Q, Liu HC, Wu XC, Xiao TY, Zhao XQ, Li GL, Jiang Y, Zeng CY, Wan KL (2015) Analysis of embCAB mutations associated with ethambutol resistance in multidrug-resistant Mycobacterium tuberculosis isolates from China. Antimicrob Agents Chemother 59(4):2045–2050PubMedPubMedCentralCrossRefGoogle Scholar
  216. Zheng J, Rubin EJ, Bifani P, Mathys V, Lim V, Au M, Jang J, Nam J, Dick T, Walker JR, Pethe K, Camacho LR (2013) Para-Aminosalicylic acid is a prodrug targeting dihydrofolate reductase in Mycobacterium tuberculosis. J Biol Chem 288(32):23447–23456PubMedPubMedCentralCrossRefGoogle Scholar
  217. Zsoldos Z, Reid D, Simon A, Sadjad SB, Johnson AP (2007) eHiTS: a new fast, exhaustive flexible ligand docking system. J Mol Graph Model 26(1):198–212PubMedCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of BiotechnologyDayananda Sagar College of Engineering, Dayananda Sagar InstitutionsBengaluruIndia

Personalised recommendations