Advertisement

Molecular Modeling and Drug Design Techniques in Microbial Drug Discovery

  • Chandrabose Selvaraj
Chapter
First Online:
  • 781 Downloads

Abstract

Bacterial infection and its resistance have become a major human health concern worldwide, and the number of resistant bacteria is increasing daily. Hence, antibacterial agents should possess the capability of treating resistant infections and have novel mode of action. Conventional drug development approaches are time-consuming and involve huge investments, and they frequently result in failure at the clinical trial phase due side effects. Modern computational approaches are an alternative to conventional modes and are the most effective, greatly improving on the former drug target identification and optimization of the lead compound. In this chapter, we focus on the advent of a few such computational approaches and the classical methods they aid in the identification of potential lead molecules. These techniques may be used as a resource to complement drug discovery programs for novel antibiotic discovery.

Keywords

Microbial resistance Drug Target Multi Drug Resistance Molecular Dynamics Molecular Modeling Ligand Based Screening High Throughput Screening 

Abbreviations

AMR

Antimicrobial resistance

BLAST

Basic Local Alignment Search Tool

CADD

Computer aided drug designing

CG

Coarse-grained

CG-MD

Coarse-grained molecular dynamics

CHARMM

Chemistry at Harvard Macromolecular Mechanics

CoMFA

Comparative molecular field analysis

CoMSIA

Comparative molecular similarity indices analysis

DADA

D-alanyl-D-alanine

DFT

Density functional theory

DPD

Dissipative particle dynamics

ESBLs

Extended spectrum β-lactamases

FDA

Food and Drug Administration

FEP

Free-energy perturbation method

GA

Genetic algorithms

GISA

Glycopeptides-intermediately-resistant S. aureus

GPU

Graphical processor unit

HTS

High throughput screening

IUPAC

International Union of Pure and Applied Chemistry

LB

Ligand-based

LBDD

Ligand-based drug design

LBVS

Ligand-based virtual screening

MD

Molecular dynamics

MDR

Multi-drug resistance

MRSA

Staphylococcus aureus resistant to methicillin

NCBI

National Center for Biotechnology Information

NMR

Nuclear magnetic resonance

PCA

Principle component analysis

PCR

Polymerase chain reaction

PK

Pharmacokinetic

PLS

Partial least squares

QM/MM

Quantum mechanics/molecular mechanics

QSAR

Quantitative structure-activity relationship

SB

Structure-based

SBDD

Structure-based drug design

SBVS

Structure-based virtual screening

SI

Sequence identity

TEIC

Teicoplanin

VANC

Vancomycin

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

References

  1. Abel R, Young T, Farid R, Berne BJ, Friesner RA (2008) Role of the active-site solvent in the thermodynamics of factor Xa ligand binding. J Am Chem Soc 130:2817–2831PubMedPubMedCentralCrossRefGoogle Scholar
  2. Abrams C, Bussi G (2014) Enhanced sampling in molecular dynamics using metadynamics, replica-exchange, and temperature-acceleration. Entropy 16:163–199CrossRefGoogle Scholar
  3. Accelrys Available Chemicals Directory (ACD) (2012) Accelrys, Inc., San Diego, CAGoogle Scholar
  4. Accelrys. TOPKAT (TOxicity Prediction by Komputer Assisted Technology) (2015). Available at: http://accelrys.com/products/discovery-studio/admet.html
  5. Acharya C, Coop A, Polli JE, MacKerell AD (2011) Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach. Curr Comput Aided Drug Des 7:10–22PubMedPubMedCentralCrossRefGoogle Scholar
  6. Adcock SA, McCammon JA (2006) Molecular dynamics: survey of methods for simulating the activity of proteins. Chem Rev 106:1589–1615PubMedPubMedCentralCrossRefGoogle Scholar
  7. Agrafiotis DK, Gibbs AC, Zhu F, Izrailev S, Martin E (2007) Conformational sampling of bioactive molecules: a comparative study. J Chem Inf Model 47:1067–1086PubMedCrossRefPubMedCentralGoogle Scholar
  8. Akamatsu M (2002) Current state and perspectives of 3D-QSAR. Curr Top Med Chem 2:1381–1394PubMedCrossRefPubMedCentralGoogle Scholar
  9. Albrich WC, Monnet DL, Harnarth S (2004) Antibiotic selection pressure and resistance in Streptococcus pneumoniae and Streptococcus pyogenes. Emerg Infect Dis 10:514–517PubMedPubMedCentralCrossRefGoogle Scholar
  10. Allen TD, Eby LT, Poteet ML, Lentz E, Lima L (2004) Career benefits associated with mentoring for protégeé: a meta-analysis. J Appl Psychol 89:127–136PubMedCrossRefPubMedCentralGoogle Scholar
  11. Allen GD, Breshears DD, McDowell NG (2015) On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6:1–55CrossRefGoogle Scholar
  12. AltTox. Toxicity testing overview. Available at: http://alttox.org/mapp/toxicity-testing-overview/
  13. An J, Totrov M, Abagyan R (2005) Pocketome via comprehensive identification and classification of ligand binding envelopes. Mol Cell Proteomics 4:752–761PubMedCrossRefGoogle Scholar
  14. Anandakrishnan R, Aguilar B, Onufriev AV (2012) H++ 3.0: automating pK prediction and the preparation of biomolecular structures for atomistic molecular modeling and simulation. Nucleic Acids Res 40:W537–41–W541PubMedPubMedCentralCrossRefGoogle Scholar
  15. Arias CA, Murray BE (2009) Antibiotic-resistant bugs in the 21 st century: a super-clinical challenge. N Engl J Med 360:439–443PubMedCrossRefGoogle Scholar
  16. Ashrafuzzaman M (2014) Aptamers as both drugs and drug-carriers. Biomed Res Int 2014:697923PubMedPubMedCentralCrossRefGoogle Scholar
  17. Atkins P, de Paula J. (2006) Atkins’ Physical Chemistry. 8th ed. New York: Macmillan.Google Scholar
  18. Baker D, Sali A (2001) Protein structure prediction and structural genomics. Science 294(5540):93–96PubMedCrossRefGoogle Scholar
  19. Ballester PJ, Mitchell JBO (2010) A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics (Oxford, England) 26:1169–1175CrossRefGoogle Scholar
  20. Barry AL, Fuchs PC, Brown SD (2001) In vitro activities of daptomycin against 2,789 clinical isolates from 11 North American medical centers. Antimicrob Agents Chemother 45:1919–1922PubMedPubMedCentralCrossRefGoogle Scholar
  21. Benigni R, Battistelli CL, Bossa C, Colafranceschi M, Tcheremenskaia O (2013) Mutagenicity, carcinogenicity, and other end points. In: Reisfeld B, Mayeno AN (eds) Computational toxicology, vol 930. Humana Press, New York, pp 67–98CrossRefGoogle Scholar
  22. Beno B, Mason J (2001) The design of combinatorail libraries using properties and 3D-pharmacophore fingerprinits. Drug Discov Today 6:251–258PubMedCrossRefGoogle Scholar
  23. Berendsen HJC, Grigera JR, Straatsma TP (1987) The missing term in effective pair potentials. J Phys Chem 91(24):6269–6271CrossRefGoogle Scholar
  24. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242PubMedPubMedCentralCrossRefGoogle Scholar
  25. Bernard D, Coop A, MacKerell AD (2005) Conformationally sampled pharmacophore for peptidic delta opioid ligands. J Med Chem 48:7773–7780PubMedCrossRefGoogle Scholar
  26. Binkowski T, Naghibzadeh S, Liang J (2003) CASTp: computed atlas of surface topography of proteins. Nucleic Acids Res 31:3352–3355PubMedPubMedCentralCrossRefGoogle Scholar
  27. Blanco E, Shen H, Ferrari M (2015) Principles of nanoparticle design for overcoming biological barriers to drug delivery. Nat Biotechnol 33:941–951PubMedPubMedCentralCrossRefGoogle Scholar
  28. Bleicher KH, Bohm HJ, Muller K, Alanine AI (2003) Hit and lead generation: beyond high-throughput screening. Nat Rev Drug Discov 2:369–378PubMedCrossRefGoogle Scholar
  29. Bolhuis PG, Chandler D, Dellago C, Geissler PL (2002) Transition path sampling: throwing ropes over rough mountain passes, in the dark. Annu Rev Phys Chem 53:291–318PubMedCrossRefGoogle Scholar
  30. Borhani DW, Shaw DE (2012) The future of molecular dynamics simulations in drug discovery. J Comput Aided Mol Des 26:15–26PubMedCrossRefGoogle Scholar
  31. Brady GP, Stouten PFW (2000) Fast prediction and visualization of protein binding pockets with PASS. J Comput Aided Mol Des 14:383–401PubMedCrossRefPubMedCentralGoogle Scholar
  32. Briggs JM, Marrone TJ, McCammon JA (1996) Computational science new horizons and relevance to pharmaceutical design. Trends Cardiovasc Med 6:198–206PubMedCrossRefPubMedCentralGoogle Scholar
  33. Bronzwaer SL, Bronzwaer SL, Cars O, Buchholz U, Mölstad S, Goettsch W, Veldhuijzen IK, Kool JL, Sprenger MJ, Degener JE (2002) A European study on the relationship between antimicrobial use and antimicrobial resistance. Emerg Infect Dis 8:278–282PubMedPubMedCentralCrossRefGoogle Scholar
  34. 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 4:187–217CrossRefGoogle Scholar
  35. Bryant SH, Lawrence CE (1993) An empirical energy function for threading protein sequence through the folding motif. Proteins 16(1):92–112PubMedCrossRefPubMedCentralGoogle Scholar
  36. Burger A, Abraham DJ (2006) Burger’s medicinal chemistry and drug discovery, drug discovery and drug development; Wiley, 2003. In: Langer T, Hoffmann RD (eds) Pharmacophores and Pharmacophore Searches. Wiley-VCH Verlag GmbH & Co, WeinheimGoogle Scholar
  37. Cammarata SK, Timm JA, Hempsall KA, Todd WM, Oliphant TH, Hafkin B (2000) Efficacy of linezolid in pneumonia due to penicillin intermediate and resistant Streptococcus pneumoniae. [abstract SO.OlO]. In: Programs and abstracts of the 9th International Congress on Infectious Diseases; Buenos Aires, Argentina, April 10–13. International Society of Infectious Diseases, BostonGoogle Scholar
  38. Carlson HA (2002) Protein flexibility and drug design: how to hit a moving target. Curr Opin Chem Biol 6:447–452PubMedCrossRefGoogle Scholar
  39. Case DA, Cheatham TE 3rd, Darden T, Gohlke H, Luo R (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688PubMedPubMedCentralCrossRefGoogle Scholar
  40. CASP (1999). Proteins, Structure, Function, and Genetics. CASP3 Proceeding 37:1–237Google Scholar
  41. Cato S (2000) Pharmacophore perception, development, and use in drug design. International University Line, La Jolla, CA, Chapter Exploring Pharmacophores with Chem-X,, pp 107–125Google Scholar
  42. Chang C, Swaan PW (2006) Computational approaches to modeling drug transporters. Eur J Pharm Sci 27:411–424PubMedCrossRefPubMedCentralGoogle Scholar
  43. Chen J, Swamidass SJ, Dou Y, Bruand J, Baldi P (2005) ChemDB: a public database of small molecules and related chemoinformatics resources. Bioinformatics 21(22):4133–4139PubMedCrossRefPubMedCentralGoogle Scholar
  44. Chen JH, Linstead E, Swamidass SJ, Wang D, Baldi P (2007) ChemDB update-full-text search and virtual chemical space. Bioinformatics 23:2348–2351PubMedCrossRefGoogle Scholar
  45. Chen VB, Arendall WB, Headd JJ, Keedy DA, Immormino RM, Kapral GJ, Murray LW, Richardson SJ, Richardson DC (2010) MolProbity: allatom structure validation for macromolecular crystallography. Acta Crystallogr D Biol Crystallogr D66:12–21CrossRefGoogle Scholar
  46. Clark DE (2006) What has computer-aided molecular design ever done for drug discovery? Expert Opin Drug Discovery 1:103–110CrossRefGoogle Scholar
  47. Clark DE (2008) What has virtual screening ever done for drug discovery? Expert Opin Drug Discovery 3:841–851CrossRefGoogle Scholar
  48. Claussen H, Buning C, Rarey M, Lengauer T (2001) FlexE: efficient molecular docking considering protein structure variations. J Mol Biol 308:377PubMedCrossRefPubMedCentralGoogle Scholar
  49. Cohen AJ, Mori-Sánchez P, Yang W (2012) Challenges for density functional theory. Chem Rev 112:289–320PubMedCrossRefPubMedCentralGoogle Scholar
  50. Collignon P, Powers JH, Chiller TM, Aidara-Kane A, Aarestrup FM (2009) World Health Organization ranking of antimicrobials according to their importance in human medicine: a critical step for developing risk management strategies for the use of antimicrobials in food production animals. Clin Infect Dis 49:132–141PubMedCrossRefPubMedCentralGoogle Scholar
  51. Corbeil CR, Williams CI, Labute P (2012) Variability in docking success rates due to dataset preparation. J Comput Aided Mol Des 26:775–786PubMedPubMedCentralCrossRefGoogle Scholar
  52. Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz KM, Ferguson DM, Spellmeyer DC, Fox T, Caldwell JW, Kollman PA (1995) A 2nd generation force-field for the simulation of proteins, nucleic acids and organic molecules. J Am Chem Soc 117:5179–5197CrossRefGoogle Scholar
  53. Cronin MTD (2011) In silico tools for toxicity prediction. In: Wilson AGE (ed) New horizons in predictive toxicology: current status and application. Royal Society of Chemistry, CambridgeGoogle Scholar
  54. D’Alfonso G, Tramontano A, Lahm A (2001) Structural conservation in single-domain proteins: implications for homology modeling. J Struct Biol 134:246–256PubMedCrossRefPubMedCentralGoogle Scholar
  55. Davis IW, Leaver-Fay A, Chen VB, Block JN, Kapral GJ, Wang X, Murray LW, Arendall WB, Snoeyink J, Richardson JS, Richardson DC (2007) MolProbity: all-atom contacts and structure validation for proteins and nucleic acids. Nucleic Acids Res 35:W375–W383PubMedPubMedCentralCrossRefGoogle Scholar
  56. Deeb O, Goodarzi M (2012) In silico quantitative structure toxicity relationship of chemical compounds: some case studies. Curr Drug Saf 7:289–297PubMedCrossRefPubMedCentralGoogle Scholar
  57. Devillers J (2013) Methods for building QSARs. In: Reisfeld B, Mayeno AN (eds) Computational toxicology, vol 930. Humana Press, New York, pp 3–27CrossRefGoogle Scholar
  58. Dias R, de Azevedo WF Jr (2008) Molecular docking algorithms. Curr Drug Targets 9(12):1040–1047PubMedCrossRefPubMedCentralGoogle Scholar
  59. DiMasi JA, Hansen RW, Grabowski HG (2003) The price of innovation: new estimates of drug developmentcosts. J Health Econ 22:151–185PubMedCrossRefPubMedCentralGoogle Scholar
  60. Dimitropoulos D, Ionides J, and Henrick K (2006) Using PDBeChem to Search the PDB Ligand Dictionary, in Current Protocols in Bioinformatics; John Wiley & Sons, 14.13.11–14.13.13.Google Scholar
  61. Dimitrov S, Mekenyan O (2010) An introduction to readacross for the prediction of the effects of chemicals. In: Cronin MTD, Madden JC (eds) In silico toxicology: principles and applications. The Royal Society of Chemistry, Cambridge, pp 372–384CrossRefGoogle Scholar
  62. Doernberg SB, Lodise TP, Thaden JT, Munita JM, Cosgrove SE, Arias CA, Boucher HW, Corey GR, Lowy FD, Murray B, Miller LG, Holland TL (2017) Gram-positive bacterial infections: research priorities, accomplishments, and future directions of the antibacterial resistance leadership group. Clin Infect Dis 64:S24PubMedPubMedCentralCrossRefGoogle Scholar
  63. Dolinsky TJ, Czodrowski P, Li H, Nielsen JE, Jensen JH, Klebe G, Baker NA (2007) PDB2PQR: expanding and upgrading automated preparation of biomolecular structures for molecular simulations. Nucleic Acids Res 35:W522–W525PubMedPubMedCentralCrossRefGoogle Scholar
  64. Duncan R (2003) The dawning era of polymer therapeutics. Nat Rev Drug Discov 2:347–360PubMedPubMedCentralCrossRefGoogle Scholar
  65. Durrant J, McCammon JA (2011) Molecular dynamics simulations and drug discovery. BMC Biol 9:71PubMedPubMedCentralCrossRefGoogle Scholar
  66. Ekins S, Mestres J, Testa B (2007) In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol 152:9–20PubMedPubMedCentralCrossRefGoogle Scholar
  67. El-Masri H (2013) Modeling for regulatory purposes (risk and safety assessment). In: Reisfeld B, Mayeno AN (eds) Computational toxicology, vol 930. Humana Press, New York, pp 297–303CrossRefGoogle Scholar
  68. Enoch SJ (2009) Chemical category formation and readacross for the prediction of toxicity. In: Puzyn T, Leszczynski J, Cronin MT (eds) Recent advances in QSAR studies, vol 8. Springer, Dordrecht, pp 209–219CrossRefGoogle Scholar
  69. Enyedy IJ, Sakamuri S, Zaman WA, Johnson KM, Wang S (2003) Pharmacophore-based discovery of substituted pyridines as novel dopamine transporter inhibitors. Bioorg Med Chem Lett 13:513–517PubMedCrossRefPubMedCentralGoogle Scholar
  70. Epik v2.2 (2011) Portland, OR: Schrödinger, IncGoogle Scholar
  71. Ernst A, Obrecht D (2008) Case study of parallel synthesis in hit identification, hit exploration, hit-to-lead, and lead-optimization programs in high-throughput lead optimization in drug discovery. In: Kshirsagar T (ed) CRC Press, Boca Raton, USA, pp 99–116Google Scholar
  72. Fanelli F, Gohlke H, Kuhn LA, Morris GM, Orozco M, Pertinhez TA, Rizzi M, Sotriffer C (2008) Target flexibility: an emerging consideration in drug discovery and design. J Med Chem 51:6237–6255PubMedPubMedCentralCrossRefGoogle Scholar
  73. Fang Y (2012) Ligand-receptor interaction platforms and their applications for drug discovery. Expert Opin Drug Discovery 7:969–988CrossRefGoogle Scholar
  74. Faradjian AK, Elber R (2004) Computing time scales from reaction coordinates by milestoning. J Chem Phys 120:10880–10889PubMedCrossRefPubMedCentralGoogle Scholar
  75. Faraggi E, Xue B, Zhou Y (2009) Improving the prediction accuracy of residue solvent accessibility and real-value backbone torsion angles of proteins by guided-learning through a two-layer neural network. Proteins 74:847–856PubMedPubMedCentralCrossRefGoogle Scholar
  76. Fiser A (2010) Template-based protein structure modeling. Methods Mol Biol (Clifton, NJ) 673:73–94CrossRefGoogle Scholar
  77. Floudas CA (2007) Computational methods in protein structure prediction. Biotechnol Bioeng 97:207–213PubMedCrossRefPubMedCentralGoogle Scholar
  78. Floudas C, Fung H, McAllister S, Moennigmann M, Rajgaria R (2006) Advances in protein structure prediction and de novo protein design: a review. Chem Eng Sci 61:966CrossRefGoogle Scholar
  79. Flower DR (2002) Drug design: cutting edge approaches. Royal Society of Chemistry, CambridgeCrossRefGoogle Scholar
  80. Frenkel D, Smit B (2001) Understanding molecular simulation. Academic Press, Inc, San Diego, p 638Google Scholar
  81. Frenkel D, Smit B (2002) Understanding molecular simulation: from algorithms to applications. Academic Press, San DiegoGoogle Scholar
  82. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749CrossRefPubMedGoogle Scholar
  83. Gao JL, Truhlar DG (2002) Quantum mechanical methods for enzyme kinetics. Annu Rev Phys Chem 53:467–505PubMedCrossRefPubMedCentralGoogle Scholar
  84. Gehlhaar DK, Verkhivker GM, Rejto PA, Sherman CJ, Fogel DB, Freer ST (1995) Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming. Chem Biol 2:317–324PubMedCrossRefPubMedCentralGoogle Scholar
  85. Gehlhaar DK, Bouzida D, Rejto PA (1999) In: Parrill L, Reddy MR (eds) In rational drug design: novel methodology and practical applications, vol 719. American Chemical Society, Washington, DC, pp 292–311CrossRefGoogle Scholar
  86. Geppert H, Vogt M, Bajorath J (2010) Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation. J Chem Inf Model 50:205–216PubMedCrossRefPubMedCentralGoogle Scholar
  87. Gilson MK, Zhou HX (2007) Calculation of protein-ligand binding affinities. Annu Rev Biophys Biomol Struct 36:21–42PubMedCrossRefPubMedCentralGoogle Scholar
  88. Giske CG, Monnet DL, Cars O, Carmeli Y (2008) Clinical and economic impact of common multidrug-resistant Gram-negative bacilli. Antimicrob Agents Chemother 52:813–821PubMedCrossRefPubMedCentralGoogle Scholar
  89. Göbl C, Madl T, Simon B, Sattler M (2014) NMR approaches for structural analysis of multidomain proteins and complexes in solution. Prog Nucl Magn Reson Spectrosc 80:26–63PubMedCrossRefPubMedCentralGoogle Scholar
  90. Gohda K, Mori I, Ohta D, Kikuchi T (2000) A CoMFA analysis with conformational propensity: an attempt to analyze the SAR of a set of molecules with different conformational flexibility using a 3D-QSAR method. J Comput Aided Mol Des 14:265–275PubMedCrossRefPubMedCentralGoogle Scholar
  91. Gohlke H, Klebe G (2001) Statistical potentials and scoring functions applied to protein-ligand binding. Curr Opin Struct Biol 11:231–235PubMedCrossRefPubMedCentralGoogle Scholar
  92. González PM, Acharya C, MacKerell AD Jr, Polli JE (2009) Inhibition requirements of the human apical sodium-dependent bile acid transporter (hASBT) using aminopiperidine conjugates of glutamyl-bile Acids. Pharm Res 26:1665–1678PubMedPubMedCentralCrossRefGoogle Scholar
  93. Goto E, Yagi Y, Matsumoto Y, Tsubota K (2002) Impaired functional visual acuity of dry eye patients. Am J Ophthalmol 133:181–186PubMedCrossRefPubMedCentralGoogle Scholar
  94. Guedes IA, de Magalhães CS, Dardenne LE (2014) Receptor-ligand molecular docking. Biophys Rev 6:75–87PubMedCrossRefPubMedCentralGoogle Scholar
  95. Guimaraes CRW, Cardozo M (2008) MM-GB/SA rescoring of docking poses in structure-based lead optimization. J Chem Inf Model 48:958–970PubMedCrossRefPubMedCentralGoogle Scholar
  96. Hajduk PJ, Huth JR, Fesik SW (2005) Druggability indices for protein targets derived from NMR-based screening data. J Med Chem 48:2518–2525PubMedCrossRefPubMedCentralGoogle Scholar
  97. Halgren T (2007) New method for fast and accurate binding-site identification and analysis. Chem Biol Drug Des 69:146–148PubMedCrossRefPubMedCentralGoogle Scholar
  98. Halgren TA (2009) Identifying and characterizing binding sites and assessing druggability. J Chem Inf Model 49:377–389PubMedCrossRefPubMedCentralGoogle Scholar
  99. 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 47:1750–1759CrossRefPubMedGoogle Scholar
  100. Hamelberg D, Mongan J, McCammon JA (2004) Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. J Chem Phys 120:11919–11929PubMedCrossRefPubMedCentralGoogle Scholar
  101. Hanson SM, Newstead S, Swartz KJ, Sansom MS (2015) Capsaicin interaction with TRPV1 channels in a lipid bilayer: molecular dynamics simulation. Biophys J 108:1425–1434PubMedPubMedCentralCrossRefGoogle Scholar
  102. Harvey MJ, De Fabritiis G (2012) High-throughput molecular dynamics: the powerful new tool for drug discovery. Drug Discov Today 17:1059–1062PubMedCrossRefPubMedCentralGoogle Scholar
  103. Hawkey PM, Jones AM (2009) The changing epidemiology of resistance. J Antimicrob Chemother 64:i3–i10PubMedCrossRefPubMedCentralGoogle Scholar
  104. Hendlich M, Rippmann F, Barnickel G (1997) LIGSITE: automatic and efficient detection of potential small moleculebinding sites in proteins. J Mol Graph Model 15:359–363CrossRefPubMedPubMedCentralGoogle Scholar
  105. Herrling PL (2005) The drug discovery process. Prog Drug Res 62:1–14PubMedCrossRefPubMedCentralGoogle Scholar
  106. Hileman B (2006) Accounting for R&D, Many doubt the $800 million pharmaceutical price tag. Chemical Eng News 84:50–51Google Scholar
  107. Honarparvar B, Kruger HG, Maguire GE, Govender T, Soliman ME (2014) Integrated approach to structure-based enzymatic drug design: molecular modeling, spectroscopy, and experimental bioactivity. Chem Rev 114:493–537PubMedCrossRefGoogle Scholar
  108. Hoofnagle AN, Resing KA, Ahn NG (2003) Protein analysis by hydrogen exchange mass spectrometry. Annu Rev Biophys Biomol Struct 32:1–25PubMedCrossRefGoogle Scholar
  109. Hooft R, Vriend WG, Sander C, Abola EE (1996) Errors in protein structures. Nature 381:272PubMedCrossRefGoogle Scholar
  110. Hristozov D, Oprea TI, Gasteiger J (2007) Ligand-based virtual screening by novelty detection with self-organizing maps. J Chem Inf Model 47(6):2044–2062PubMedCrossRefPubMedCentralGoogle Scholar
  111. Huang SY, Zou X (2006a) An iterative knowledge-based scoring function to predict protein-ligand interactions: I. Derivation of interaction potentials. J Comput Chem 27:1866–1875PubMedCrossRefGoogle Scholar
  112. Huang SY, Zou X (2006b) An iterative knowledge-based scoring function to predict protein-ligand interactions: II. Validation of the scoring function. J Comput Chem 27:1876–1882PubMedCrossRefGoogle Scholar
  113. Huang X, Lin J, Demner-Fushman D (2006) Evaluation of PICO as a knowledge representation for clinical questions. AMIA Annual Symposium proceedings. AMIA Symposium 2006:359–363Google Scholar
  114. Irwin JJ, Shoichet BK (2005) ZINC - a free database of commercially available compounds for virtual screening. J Chem Inf Model 45:177–182PubMedPubMedCentralCrossRefGoogle Scholar
  115. Isralewitz B, Gao M, Schulten K (2001) Steered molecular dynamics and mechanical functions of proteins. Curr Opin Struct Biol 11:224–230PubMedCrossRefGoogle Scholar
  116. Jack J, Wambaugh J, Shah I (2013) Systems toxicology from genes to organs. In: Reisfeld B, Mayeno AN (eds) Computational toxicology, vol 930. Humana Press, New York, pp 375–397CrossRefGoogle Scholar
  117. Jain AN (2003) Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J Med Chem 46:499–511PubMedCrossRefGoogle Scholar
  118. Jain AN (2006) Scoring functions for protein-ligand docking. Curr Protein Pept Sci 7:407–420PubMedCrossRefGoogle Scholar
  119. Jones DT, Taylor WR, Thornton JM (1992a) The rapid generation of mutation data matrices from protein sequences. Comput Appl Biosci 3:275–282Google Scholar
  120. Jones DT, Taylor WR, Thornton JM (1992b) A new approach to protein fold recognition. Nature 358:86–89PubMedCrossRefPubMedCentralGoogle Scholar
  121. Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748Google Scholar
  122. Jorgensen WL, Thomas LL (2008) Perspective on free-energy perturbation calculations for chemical equilibria. J Chem Theory Comput 4:869–876PubMedPubMedCentralCrossRefGoogle Scholar
  123. Jorgensen WL, Tirado-Rives J (2005) Molecular modeling of organic and biomolecular systems using BOSS and MCPRO. J Comput Chem 26:1689–1700PubMedCrossRefGoogle Scholar
  124. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935CrossRefGoogle Scholar
  125. Jorgensen WL, Maxwell DS, Tirado-Rives J (1996) Development and testing of the OPLS all atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc 118:11225–11236CrossRefGoogle Scholar
  126. Jubb H, Blundell TL, Ascher DB (2015) Flexibility and small pockets at protein–protein interfaces: new insights into druggability. Prog Biophys Mol Biol 119:2–9PubMedPubMedCentralCrossRefGoogle Scholar
  127. Kahsai AW, Xiao K, Rajagopal S, Ahn S, Shukla AK, Sun J, Oas TG, Lefkowitz RJ (2011) Multiple ligand-specific conformations of the β2-adrenergic receptor. Nat Chem Biol 7:692–700PubMedPubMedCentralCrossRefGoogle Scholar
  128. Kalyaanamoorthy S, Chen YP (2011) Structure-based drug design to augment hit discovery. Drug Discov Today 16:831–839PubMedCrossRefGoogle Scholar
  129. Karchin R, Cline M, Mandel-Gutfreund Y, Karplus K (2003) Hidden Markov models that use predicted local structure for fold recognition: alphabets of backbone geometry. Proteins 51:504–514PubMedCrossRefGoogle Scholar
  130. Karnachi P, Kulkarni A (2006) Application of pharmacophore fingerprints to structure based design and data mining. In: Langer T, Hoffmann RD (eds) Pharmacophores and pharmacophore searches. Wiley-VCH, WeinheimGoogle Scholar
  131. Karplus K, Karchin R, Draper J, Casper J, Mandel-Gutfreund Y, Diekhans M, Hughey R (2003) Combining local-structure, fold-recognition and new fold methods for protein structure prediction. Proteins 53:491–496PubMedCrossRefGoogle Scholar
  132. Kaserer T, Beck KR, Akram M, Odermatt A, Schuster D (2015) Pharmacophore models and pharmacophore-based virtual screening: concepts and applications exemplified on hydroxysteroid dehydrogenases. Molecules 20:22799–22832PubMedPubMedCentralCrossRefGoogle Scholar
  133. Kaveti S, Engen JR (2006) Protein interactions probed with mass spectrometry. Methods Mol Biol 316:179–197PubMedGoogle Scholar
  134. Kelley LA, MacCallum RM, Sternberg MJ (2000) Enhanced genome annotation using structural profiles in the program 3d-pssm. J Mol Biol 299:499–520PubMedCrossRefGoogle Scholar
  135. Khougaz K, Clas SD (2000) Crystallization inhibition in solid dispersions of MK-0591 and poly(vinylpyrrolidone) polymers. J Pharm Sci 89:1325–1334PubMedCrossRefGoogle Scholar
  136. Kindermann C, Matthee K, Strohmeyer J, Sievert F, Breitkreutz J (2011) Tailor-made release triggering from hot-melt extruded complexes of basic polyelectrolyte and poorly watersoluble drugs. Eur J Pharm Biopharm 79:372–381PubMedCrossRefGoogle Scholar
  137. King A, Phillips I (2001) The in vitro activity of daptomycin against 514 Gram-positive aerobic clinical isolates. J Antimicrob Chemother 48:219–223PubMedCrossRefGoogle Scholar
  138. Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery:methods and application. Nat Rev Drug Discov 3:935–949CrossRefGoogle Scholar
  139. Klauda JB, Venable RM, Freites JA, O’Connor JW, Tobias DJ, Mondragon-Ramirez C, Vorobyov I, MacKerell AD, Pastor RW (2010) Update of the CHARMM all-atom additive force field for lipids: validation on six lipid types. J Phys Chem B 114:7830–7843PubMedPubMedCentralCrossRefGoogle Scholar
  140. Klebe G (2006) Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today 11:580–594PubMedCrossRefGoogle Scholar
  141. Klebe G, Abraham U, Mietzner T (1994) Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J Med Chem 37:4130–4146PubMedCrossRefGoogle Scholar
  142. Kollman PA (1993) Free energy calculations: applications to chemical and biochemical phenomena. Chem Rev 93:2395–2417CrossRefGoogle Scholar
  143. Kountouris P, Hirst JD (2009) Prediction of backbone dihedral angles and protein secondary structure using support vector machines. BMC Bioinformatics 10:437PubMedPubMedCentralCrossRefGoogle Scholar
  144. Kovalenko A (2003) Three-dimensional RISM theory for molecular liquids and solid-liquid interfaces. In: Hirata F, Mezey PG (eds) 360, vol 24. Kluwer Academic Publishers, Dordrecht, pp 169–275Google Scholar
  145. Kramer MS, Cutler N, Feighner J, Shrivastava R, Carman J, Sramek JJ, Reines SA, Liu G, Snavely D, Wyatt-Knowles E, Hale JJ, Mills SG, MacCoss M, Swain CJ, Harrison T, Hill RG, Hefti F, Scolnick EM, Cascieri MA, Chicchi GG, Sadowski S, Williams AR, Hewson L, Smith D, Carlson EJ, Hargreaves RJ, Rupniak NM (1998) Distinct mechanism for antidepressant activity by blockade of central substance P receptors. Science 281:1640–1645PubMedCrossRefPubMedCentralGoogle Scholar
  146. Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161:269–288PubMedCrossRefGoogle Scholar
  147. Laio A, Parrinello M (2002) Escaping free-energy minima. Proc Natl Acad Sci U S A 99:12562–12566PubMedPubMedCentralCrossRefGoogle Scholar
  148. Langer T, Hoffmann RD (2006) Pharmacophore modeling: application in drug discovery. Expert Opin Drug Discovery 1:261–267CrossRefGoogle Scholar
  149. Laskowski RA (1995 Oct) SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions. J Mol Graph 13(5):323–330, 307–308PubMedCrossRefGoogle Scholar
  150. Laskowski RA, Moss DS, Thornton JM (1993) Main-chain bond lengths and bond angles in protein structures. J Mol Biol 231:1049–1067PubMedCrossRefGoogle Scholar
  151. Leach AR, Hann MM (2000) The in silica world of virtual libraries. Drug Discov Today 5:326–336PubMedCrossRefGoogle Scholar
  152. Levitt DG, Banaszak LJ (1992 Dec) POCKET: a computer graphics method for identifying and displaying protein cavities and their surrounding amino acids. J Mol Graph 10(4):229–234PubMedCrossRefGoogle Scholar
  153. Lewis K (2013) Platforms for antibiotic discovery. Nat Rev Drug Discov 12:371–387PubMedPubMedCentralCrossRefGoogle Scholar
  154. Lewis RA, Pickett SD, Clark DE (2000) Computer-aided molecular diversity analysis and combinatorial library design. Rev Comput Chem 16:1–51Google Scholar
  155. Lhasa Limited. Meteor Nexus (2014). Available at: http://www.lhasalimited.org
  156. Li H, Robertson AD, Jensen JH (2005) Very fast empirical prediction and rationalization of protein pKa values. Proteins 61:704–721PubMedCrossRefGoogle Scholar
  157. Liechty WB, Kryscio DR, Brandon VS, Peppas NA (2010) Polymers for drug delivery. Annu Rev Chem Biomol Eng 1:149–173PubMedPubMedCentralCrossRefGoogle Scholar
  158. LigPrep v2.5 (2011) Portland, OR: Schrödinger, IncGoogle Scholar
  159. Lin H, Truhlar DG (2007) QM/MM: what have we learned, where are we, and where do we go from here? Theor Chem Acc 117:185–199CrossRefGoogle Scholar
  160. Liu HY, Zou X (2006) Electrostatics of ligand binding: parametrization of the generalized born model and comparison with the Poisson-Boltzmann approach. J Phys Chem B 110:9304–9313PubMedPubMedCentralCrossRefGoogle Scholar
  161. Livermore DM (2009) Has the era of untreatable infections arrived? J Antimicrob Chemother 64:i29–i36PubMedCrossRefGoogle Scholar
  162. Lo Conte L, Brenner SE, Hubbard TJ, Chothia C, Murzin AG (2002) SCOP database in 2002: refinements accommodate structural genomics. Nucleic Acids Res 30:264–267PubMedPubMedCentralCrossRefGoogle Scholar
  163. Lonsdale R, Mulholland AJ (2014) QM/MM modelling of drugmetabolizing enzymes. Curr Top Med Chem 14:1339–1347PubMedCrossRefGoogle Scholar
  164. Lyne PD, Lamb ML, Saeh JC (2006) Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring. J Med Chem 49:4805–4808PubMedCrossRefGoogle Scholar
  165. MacKerell AD, Bashford D, Bellott M, Dunbrack RL, Evanseck JD, Field MJ (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 102:3586–3616CrossRefGoogle Scholar
  166. Madan AK, Bajaj S, Dureja H (2013) Classification models for safe drug molecules. In: Reisfeld B, Mayeno AN (eds) Computational toxicology, vol 930. Humana Press, New York, pp 99–124CrossRefGoogle Scholar
  167. Mahoney MW, Jorgensen WL (2001) A five-site model for liquid water and the reproduction of the density anomaly by rigid, nonpolarizable potential functions. J Chem Phys 112:8910–8922CrossRefGoogle Scholar
  168. Malabarba A, Goldstein BP (2005) Origin, structure, and activity in vitro and in vivo of dalbavancin. J Antimicrob Chemother 55:15–20CrossRefGoogle Scholar
  169. Mandal S, Moudgil M, Mandal SK (2009) Rational drug design. Eur J Pharmacol 625:90–100PubMedCrossRefPubMedCentralGoogle Scholar
  170. Marsac PJ, Li T, Taylor LS (2009) Estimation of drugpolymer miscibility and solubility in amorphous solid dispersions using experimentally determined interaction parameters. Pharm Res 26:139–151PubMedCrossRefGoogle Scholar
  171. Martin EJ, Hoeffel TJ (2000) Oriented Substituent Pharmacophore PRopErtY Space (OSPPREYS): a substituent-based calculation that describes combinatorial library products better than the corresponding product-based calculation. J Mol Graph Model 18:383–403PubMedCrossRefGoogle Scholar
  172. Mason JS, Beno BR (2000) Library design using BCUT chemistry-space descriptors and multiple four-point pharmacophore fingerprints: simultaneous optimization and structure-based diversity. J Mol Graph Model 18:438–451PubMedCrossRefGoogle Scholar
  173. Mason JS, Cheney DL (2000) Library design and virtual screening using multiple 4-point pharmacophore fingerprints. Pac Symp Biocomput 5:576–587Google Scholar
  174. McGann MR, Almond HR, Nicholls A, Grant JA, Brown FK (2003) Gaussian docking functions. Biopolymers 68:76–90CrossRefGoogle Scholar
  175. Meller J, Elber R (2001) Linear programming optimization and a double statistical filter for protein threading protocols. Proteins: Struct Funct Bioinf 45:241CrossRefGoogle Scholar
  176. Meng X-Y, Zhang H-X, 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
  177. Menichetti F (2005) Current and emerging serious Gram-positive infections. Clin Microbiol Infect 11(s3):22–28PubMedCrossRefGoogle Scholar
  178. Menikarachchi LC, Gascon JA (2010) QM/MM approaches in medicinal chemistry research. Curr Top Med Chem 10:46–54PubMedCrossRefGoogle Scholar
  179. Miao X, Waddell PJ, Valafar H (2008) TALI: local alignment of protein structures using backbone torsion angles. J Bioinform Comput Biol 6:163–181PubMedCrossRefGoogle Scholar
  180. Michel J, Verdonk ML, Essex JW (2006) Protein-ligand binding affinity predictions by implicit solvent simulations: a tool for lead optimization? J Med Chem 49:7427–7439PubMedCrossRefGoogle Scholar
  181. Michel J, Tirado-Rives J, Jorgensen WL (2009) Prediction of the water content in protein binding sites. J Phys Chem B 113:13337–13346PubMedPubMedCentralCrossRefGoogle Scholar
  182. Milburn D, Laskowski RA, Thornton JM (1998) Sequence annotated by structure: a tool facilitate the use of structural information in sequence analysis. Protein Eng 11:855–859PubMedCrossRefGoogle Scholar
  183. Miller MS, Lay WK, Li S, Hacker WC, An J, Ren J, Elcock AH (2017) Reparameterization of protein force field nonbonded interactions guided by osmotic coefficient measurements from molecular dynamics simulations. J Chem Theory Comput 13:1812–1826PubMedPubMedCentralCrossRefGoogle Scholar
  184. Miyazaki T, Aso Y, Yoshioka S, Kawanishi T (2011) Differences in crystallization rate of nitrendipine enantiomers in amorphous solid dispersions with HPMC and HPMCP. Int J Pharm 407:111–118PubMedCrossRefGoogle Scholar
  185. Modi S, Hughes M, Garrow A, White A (2012) The value of in silico chemistry in the safety assessment of chemicals in the consumer goods and pharmaceutical industries. Drug Discov Today 17:134–142CrossRefGoogle Scholar
  186. Moellering RC, Linden PK, Reinhardt J, Blumberg EA, Bompart F (1999) The efficacy and safty of quinupristin/dalfopristin for the treatment of infection caused by vancomycin-resistant Enterococcus faecium. J Antimicrob Chemother 44:251–261PubMedCrossRefGoogle Scholar
  187. 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 19:1639–1662CrossRefGoogle Scholar
  188. Muegge I (2006) PMF scoring revisited. J Med Chem 49:5895–5902PubMedCrossRefGoogle Scholar
  189. Mustata G, Follis AV, Hammoudeh DI, Metallo SJ, Wang H, Prochownik EV, Lazo JS, Bahar I (2009) Discovery of novel Myc-Max heterodimer disruptors with a three-dimensional pharmacophore model. J Med Chem 52:1247–1250PubMedPubMedCentralCrossRefGoogle Scholar
  190. Myrianthopoulos V, Gaboriaud-Kolar N, Tallant C, Hall ML, Grigoriou S, Brownlee PM, Fedorov O, Rogers C, Heidenreich D, Wainer M, Drosos N, Mexia N, Savitsky P, Bagratuni T, Kastritis E, Terpos E, Mikros E (2016) Discovery and optimization of a selective ligand for the switch/sucrose nonfermenting-related bromodomains of polybromo protein-1 by the use of virtual screening and hydration analysis. J Med Chem 59(19):8787–8803PubMedPubMedCentralCrossRefGoogle Scholar
  191. Nelson DL, Lehninger AL, Cox MM (2008) Lehninger principles of biochemistry. W.H. Freeman, New YorkGoogle Scholar
  192. Neumann D, Lehr C-M, Lenhof H-P et al (2004) Computational modeling of the sugar-lectin interaction. Adv Drug Deliv Rev 56:437–457PubMedCrossRefPubMedCentralGoogle Scholar
  193. Nichols RL, Graham DR, Barriere SL, Rodgers A, Wilson SE, Zervos M, Dunn DL, Kreter B (1999) Treatment of hospitalized patients with complicated Gram-positive skin and skin structure infections: two randomized, multicentre studies of quinupristin/dalfopristin versus cefazolin, oxacillin or vancomycin. Synercid Skin and Skin Structure Infection Group. J Antimicrob Chemother 44:263–273PubMedCrossRefPubMedCentralGoogle Scholar
  194. Nikaido H (2003) Molecular basis of bacterial outer membrane permeability revisited. Microbiol Mol Biol Rev 67:593–656PubMedPubMedCentralCrossRefGoogle Scholar
  195. OECD (2015) The OECD QSAR toolbox. Available at: http://www.oecd.org/chemicalsafety/riskassessment/theoecdqsartoolbox.htm
  196. Orengo CA, Bray JE, Buchan DW, Harrison A, Lee D (2002) The CATH protein family database: a resource for structural and functional annotation of genomes. Proteomics 2:11–21PubMedCrossRefPubMedCentralGoogle Scholar
  197. Österberg F, Morris GM, Sanner MF, Olson AJ, Goodsell DS (2002) Automated docking to multiple target structures: incorporation of protein mobility and structural water heterogeneity in AutoDock. Proteins 46:34–40PubMedCrossRefPubMedCentralGoogle Scholar
  198. Pallen MJ, Loman NJ, Penn CW (2010) High-throughput sequencing and clinical microbiology: progress, opportunities and challenges. Curr Opin Microbiol 13:625–631PubMedCrossRefPubMedCentralGoogle Scholar
  199. Palumbi SR (2001) Humans as the world’s greatest evolutionary force. Science 293:1786–1790PubMedCrossRefPubMedCentralGoogle Scholar
  200. Peitsch MC, Schwede T, Guex N (2000) Automated protein modelling—the proteome in 3D. Pharmacogenomics 1:257–266PubMedCrossRefPubMedCentralGoogle Scholar
  201. Pertel PE, Bernardo P, Fogarty C, Matthews P, Northland R, Benvenuto M, Thorne GM, Luperchio SA, Arbeit RD, Alder J (2008) Effects of prior effective therapy on the efficacy of daptomycin and ceftriaxone for the treatment of community-acquired pneumonia. Clin Infect Dis 46(8):1142–1151PubMedCrossRefPubMedCentralGoogle Scholar
  202. Peters JM, King RW, Höög C, Kirschner MW (1996 Nov 15) Identification of BIME as a subunit of the anaphase-promoting complex. Science 274(5290):1199–1201PubMedCrossRefPubMedCentralGoogle Scholar
  203. Peters KN, Dixon DM, Holland SM, Fauci AS (2008) The research agenda of the National Institute of Allergy and Infectious Diseases for antimicrobial resistance. J Infect Dis 197:1087–1093PubMedCrossRefPubMedCentralGoogle Scholar
  204. Petersen RK, Christensen KB, Assimopoulou AN, Fretté X, Papageorgiou VP, Kristiansen K, Kouskoumvekaki I (2011) Pharmacophore-driven identification of PPARγ agonists from natural sources. J Comput Aided Mol Des 25:107–116PubMedCrossRefPubMedCentralGoogle Scholar
  205. Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, Chipot C, Skeel RD, Kalé L, Schulten K (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26:1781–1802PubMedPubMedCentralCrossRefGoogle Scholar
  206. Pieper U, Eswar N, Ilyin VA, Stuart A, Sali A (2002) ModBase, a database of annotated comparative protein structure models. Nucleic Acids Res 30:255–259PubMedPubMedCentralCrossRefGoogle Scholar
  207. Pitt WR, Calmiano MD, Kroeplien B, Taylor RD, Turner JP, King MA (2013) Structure-based virtual screening for novel ligands. Methods Mol Biol 1008:501–519PubMedCrossRefPubMedCentralGoogle Scholar
  208. Poddar RK, Rakha P, Singh SK, Mishra DN (2010) Bioadhesive polymers as a platform for drug delivery: possibilities and future trends. Res J Pharm Dosage Forms Technol 1:40Google Scholar
  209. Pronk S, Pall S, Schulz R, Larsson P, Bjelkmar P (2013) GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29:845–854PubMedPubMedCentralCrossRefGoogle Scholar
  210. Schrödinger Suite 2014-1 Protein Preparation Wizard, Epik, version 2.7; Schrödinger, LLC: New York, NY, USA, 2013Google Scholar
  211. Raies AB, Bajic VB (2016) In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdiscip Rev Comput Mol Sci 6:147–172PubMedPubMedCentralCrossRefGoogle Scholar
  212. Raizada A, Bandari A, Kumar B (2010) Polymers in drug delivery: a review. Int J Pharm Res Dev 2:9–20Google Scholar
  213. Rajamani R, Good AC (2007) Ranking poses in structure-based lead discovery and optimization: current trends in scoring function development. Curr Opin Drug Discov Devel 10:308–315PubMedPubMedCentralGoogle Scholar
  214. Ramachandran GN, Ramakrishnan C, Sasisekharan V (1963) Stereochemistry of polypeptide chain configurations. J Mol Biol 7:95–99PubMedCrossRefPubMedCentralGoogle Scholar
  215. Rarey M, Kramer B, Lengauer T, Klebe GA (1996) Fast flexible docking method using an incremental construction algorithm. J Mol Biol 261:470–489PubMedCrossRefPubMedCentralGoogle Scholar
  216. Rask-Andersen M, Almén MS, Schiöth HB (2011) Trends in the exploitation of novel drug targets. Nat Rev Drug Discov 10:579–590CrossRefGoogle Scholar
  217. Reddy PD, Swarnalatha D (2010) Recent advances in novel drug delivery systems. Int J PharmTech Res 2:2025–2027Google Scholar
  218. Retief JD (2000) Phylogenetic analysis using PHYLIP. Methods Mol Evol 132:143–158Google Scholar
  219. Ripphausen P, Nisius B, Peltason L, Bajorath J (2010) Quo vadis, virtual screening? A comprehensive survey of prospective applications. J Med Chem 53:8461–8467PubMedCrossRefPubMedCentralGoogle Scholar
  220. Rocchia W, Sridharan S, Nicholls A, Alexov E, Chiabrera A, Honig B (2002) Rapid grid-based construction of the molecular surface and the use of induced surface charge to calculate reaction field energies: applications to the molecular systems and geometric objects. J Comput Chem 23:128–137PubMedCrossRefPubMedCentralGoogle Scholar
  221. Rohs R, Bloch I, Sklenar H, Shakked Z (2005) Molecular flexibility in ab-initio drug docking to DNA: binding-site and binding-mode transitions in all-atom Monte Carlo simulations. Nucleic Acids Res 33:7048–7057PubMedPubMedCentralCrossRefGoogle Scholar
  222. Rost B (2001) Review: protein secondary structure prediction continues to rise. J Struct Biol 134:204–218PubMedCrossRefGoogle Scholar
  223. Rost B, Sander C (1995) Progress of 1D protein structure prediction at last. Proteins 23(3):295–300PubMedCrossRefPubMedCentralGoogle Scholar
  224. Rowe PH (2010) Statistical methods for continuous measured endpoints in in silico toxicology. In silico toxicology: Principles and applications (pp. 228–251). Cambridge, UK: The Royal Society of ChemistryGoogle Scholar
  225. Roy H, Brahma CK, Kumar R, Nandi S (2013) Formulation of saquinavir mesylate loaded microparticle by counterion induced aggregation method: approach by hyperosmotic technique. Drug Invention Today 5:259–266CrossRefGoogle Scholar
  226. Ruiz-Carmona S, Alvarez-Garcia D, Foloppe N (2014) rDock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids. PLoS Comput Biol 10:e1003571PubMedPubMedCentralCrossRefGoogle Scholar
  227. Rybak MJ, Hershberger E, Moldovan T, Grucz RG (2000) In vitro activities of daptomycin, vancomycin, linezolid, and quinupristin-dalfopristin against Staphylococci and Enterococci, including vancomycinintermediate and – resistant strains. Antimicrob Agents Chemother 44:1062–1066PubMedPubMedCentralCrossRefGoogle Scholar
  228. Rychlewski L, Jaroszewski L, Li W, Godzik A (2000) Comparison of sequence profiles: strategies for structural predictions using sequence information. Protein Sci 9:232–241PubMedPubMedCentralCrossRefGoogle Scholar
  229. Salum L, Polikarpov I, Andricopulo AD (2008) Structure-based approach for the study of estrogen receptor binding affinity and subtype selectivity. J Chem Inf Model 48:2243–2253PubMedCrossRefGoogle Scholar
  230. Santos Filho OA, Alencastro RB (2003) Modelagem de proteinas por homologia. Quím Nova 26:253–259CrossRefGoogle Scholar
  231. Sastry GM, Adzhigirey M, Day T, Annabhimoju R, Sherman W (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27:221–234PubMedCrossRefGoogle Scholar
  232. Schäffer AA, Aravind L, Madden TL, Shavirin S, Spouge JL, Wolf YI, Koonin EV, Altschul SF (2001) Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements. Nucleic Acids Res 29:2994–3005PubMedPubMedCentralCrossRefGoogle Scholar
  233. Schapira M, Abagyan R, Totrov M (2003) Nuclear hormone receptor targeted virtual screening. J Med Chem 46:3045–3059PubMedCrossRefGoogle Scholar
  234. Schmaljohann D (2006) Thermo- and pH-responsive polymers in drug delivery. Adv Drug Deliv Rev 58:1655–1670PubMedCrossRefGoogle Scholar
  235. Schulz-Gasch T, Stahl M (2004) Scoring functions for protein-ligand interactions: a critical perspective. Drug Discov Today Technol 1:231–239PubMedCrossRefGoogle Scholar
  236. Schwartz R, Istrail S, King J (2001) Frequencies of amino acid strings in globular protein sequences indicate suppression of blocks of consecutive hydrophobic residues. Protein Sci 10:1023–1031PubMedPubMedCentralCrossRefGoogle Scholar
  237. Schwede T, Kopp J, Guex N, Peitsch MC (2003) SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res 31:3381–3385PubMedPubMedCentralCrossRefGoogle Scholar
  238. Sen S, Farooqui NA, Easwari TS, Roy B (2012) CoMFA-3D QSAR approach in drug design. Int. J. Res. Dev. Pharm. L. Sci 1:167–175Google Scholar
  239. Shaik S, Cohen S, Wang Y (2010) P450 enzymes: their structure, reactivity, and selectivity-modeled by QM/MM calculations. Chem Rev 110:949–1017PubMedCrossRefGoogle Scholar
  240. Shan Y, Wang G, Zhou H (2001) Fold recognition and accurate query-template alignment by a combination of PSI-BLAST and threading. Proteins: Struct Funct Bioinf 42:23–37CrossRefGoogle Scholar
  241. Shelley JC, Cholleti A, Frye LL, Greenwood JR, Timlin MR, Uchimaya M (2007) Epik: a software program for pK (a) prediction and protonation state generation for drug-like molecules. J Comput Aided Mol Des 21:681–691PubMedCrossRefGoogle Scholar
  242. Shi Y (2014) A glimpse of structural biology through X-ray crystallography. Cell 159:995–1014PubMedCrossRefGoogle Scholar
  243. Shoichet BK, Stroud RM, Santi DV, Kuntz ID, Perry KM (1993) Structure-based discovery of inhibitors of thymidylate synthase. Science 259:1445–1450PubMedCrossRefGoogle Scholar
  244. Silverman JA, Perlmutter NG, Shapiro HM (2003) Correlation of daptomycin bactericidal activity and membrane depolarization in Staphylococcus aureus. Antimicrob Agents Chemother 47:2538–2544PubMedPubMedCentralCrossRefGoogle Scholar
  245. Sippl MJ (1993) Recognition of errors in three-dimensional structures of proteins. Proteins 14:355–362CrossRefGoogle Scholar
  246. Sippl MJ, Weitckus S (1992) Detection of native‐like models for amino acid sequences of unknown three‐dimensional structure in a data base of known protein conformations. Proteins: Struct Funct Bioinf 13:258–271CrossRefGoogle Scholar
  247. Skolnick J, Kihara D, Zhang Y (2004) Development and large scale benchmark testing of the PROSPECTOR 3 threading algorithm. Proteins 56:502–518PubMedCrossRefGoogle Scholar
  248. Sliwoski G, Kothiwale S, Meiler J, Lowe EW (2013) Computational methods in drug discovery. Pharmacol Rev 66:334–395PubMedCrossRefGoogle Scholar
  249. Spellberg B, Powers JH, Brass EP, Miller LG, Edwards JE Jr (2004) Trends in antimicrobial drug development: implications for the future. Clin Infect Dis 38:1279–1286PubMedCrossRefGoogle Scholar
  250. Springer J, Safley M, Huber S, Troxclair D, Craver R, Newman WP (2010) Histopathological findings in fatal novel H1N1: an autopsy case series from September-November 2009 in New Orleans. Louisiana J La State Med Soc 162:88–91PubMedGoogle Scholar
  251. Srivastava S, Srivastava SK, Singh RB, Mishra S (2009) Computational haracterization of proteins involved in banana (Musa acuminata) ripening. Open Nutraceuticals J 2:122–126CrossRefGoogle Scholar
  252. Steenbergen JN, Alder J, Thorne GM, Tally FP (2005) Daptomycin: a lipopeptide antibiotic for the treatment of serious Gram-positive infections. J Antimicrob Chemother 55:283–288PubMedCrossRefGoogle Scholar
  253. Sung JH, Srinivasan B, Esch MB, McLamb WT, Bernabini C, Shuler ML, Hickman JJ (2014) Using PBPK guided “Body-on-a-Chip” systems to predict mammalian response to drug and chemical exposure. Exp Biol Med 239:1225–1239CrossRefGoogle Scholar
  254. Talele TT, Khedkar SA, Rigby AC (2010) Successful application of computer aided drug discovery: moving drugs from concept to the clinic. Curr Top Med Chem 10:127–141PubMedCrossRefGoogle Scholar
  255. Tannor DJ (2008) Introduction to quantum mechanics: a time-dependent perspective. University Science Books, SausalitoGoogle Scholar
  256. ten Brink T, Exner TE (2010) pK(a) based protonation states and microspecies for protein-ligand docking. J Comput Aided Mol Des 24:935–942PubMedCrossRefGoogle Scholar
  257. Teodorescu O, Galor T, Pillardy J, Elber R (2004) Enriching the sequence substitution matrix by structural information. Proteins: Struct Funct Bioinf 54:41CrossRefGoogle Scholar
  258. Thomas CM, Nielsen KM (2005) Mechanisms of, and barriers to, horizontal gene transfer between bacteria. Nat Rev Microbiol 3:711–721PubMedCrossRefGoogle Scholar
  259. Thompson DC, Humblet C, Joseph-McCarthy D (2008) Investigation of MM-PBSA rescoring of docking poses. J Chem Inf Model 48:1081–1091PubMedCrossRefGoogle Scholar
  260. Tobi D, Elber R (2000) Distance-dependent, pair potential for protein folding: results from linear optimization. Proteins: Struct Funct Bioinf 41:40CrossRefGoogle Scholar
  261. Tollman PA (2001) Revolution in R&D: how genomics and genetics are transforming the biopharmaceutical industry. Boston Consulting Group, BostonGoogle Scholar
  262. 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 31:455–461PubMedPubMedCentralGoogle Scholar
  263. Umasankar V, Gurunathan S (2015) Drug discovery: an Appraisal. Int J Parm Pharm Sci 4:59–66Google Scholar
  264. Valerio LG Jr (2009) In silico toxicology for the pharmaceutical sciences. Toxicol Appl Pharmacol 241:356–370PubMedCrossRefGoogle Scholar
  265. van der Kamp MW, Mulholland AJ (2013) Combined quantum mechanics/molecular mechanics (QM/MM) methods in computational enzymology. Biochemistry 52:2708–2728PubMedCrossRefGoogle Scholar
  266. Van der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, HJC B (2005) GROMACS: fast, flexible, and free. J Comput Chem 26:1701–1718CrossRefGoogle Scholar
  267. 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 21:289–307CrossRefGoogle Scholar
  268. Venkatapathy R, Wang NCY (2013) Developmental toxicity prediction. In: Reisfeld B, Mayeno AN (eds) Computational toxicology, vol 930. Humana Press, New York, pp 305–340CrossRefGoogle Scholar
  269. Verma RP, Hansch C (2009) Camptothecins: a SAR/QSAR study. Chem Rev 109:213–235PubMedCrossRefGoogle Scholar
  270. Vyas V, Jain A, Jain A, Gupta A (2008) Virtual screening: a fast tool for drug design. Sci Pharm 76:333–360CrossRefGoogle Scholar
  271. 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
  272. Walsh C (2003) Antibiotics: actions, origins, resistance. American Society for Microbiology (ASM) Press, Washington, DCCrossRefGoogle Scholar
  273. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25:1157–1174PubMedCrossRefGoogle Scholar
  274. Wang Y, Shaikh SA, Tajkhorshid E (2010) Exploring transmembrane diffusion pathways with molecular dynamics. Physiology 25:142–154PubMedCrossRefGoogle Scholar
  275. WaterMap (2014) New York, NY: Schrödinger, LLCGoogle Scholar
  276. Westbrook J, Feng Z, Jain S, Bhat TN, Thanki N, Veerasamy R, Gilliland GL, Bluhm WF, Weissing H, Greer DS, Bourne PE, Berman HM (2002) The protein data bank: unifying the archive. Nucleic Acids Res 30:245–248PubMedPubMedCentralCrossRefGoogle Scholar
  277. Wheeler SR, Kearney JB, Guardiola AR, Crews ST (2006) Single-cell mapping of neural and glial gene expression in the developing Drosophila CNS midline cells. Dev Biol 294:509–524PubMedPubMedCentralCrossRefGoogle Scholar
  278. Willett P (2006) Similarity-based virtual screening using 2D fingerprints. Drug Discov Today 11(23–24):1046–1053PubMedCrossRefGoogle Scholar
  279. Wilson GL, Lill MA (2011) Integrating structure-based and ligand-based approaches for computational drug design. Future Med Chem 3:735–750PubMedCrossRefGoogle Scholar
  280. Winkler DA (2002) The role of quantitative structure-activity relationships (QSAR) in biomolecular discovery. Brief Bioinform 3:73–86PubMedCrossRefGoogle Scholar
  281. Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J (2006) DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 34(Database issue):D668–D672PubMedCrossRefPubMedCentralGoogle Scholar
  282. Wolber G, Langer T (2005) LigandScout: 3-D pharmacophores derived from protein bound ligands and their use as virtual screening filters. J Chem Inf Model 45:160–169PubMedCrossRefGoogle Scholar
  283. Wood MJ, Hirst JD (2005) Protein secondary structure prediction with dihedral angles. Proteins 59:476–481PubMedCrossRefGoogle Scholar
  284. Woodfor N, Livermore DM (2009) Infections caused by Gram-positive bacteria: a review of the global challenge. J Infect 59:S4–S16CrossRefGoogle Scholar
  285. Wootton M, Howe RA, Walsh TR, Bennett PM, Macgowan AI (2000) In-vitro activity of a range of old and new antimicrobials to hetero-vancomycin-intermediate Staphylococcus aureus (hVISA) [abstract 23131. In: Program and abstract of the 40th Interscience Conference on Antimicrobial agents and chemotherapy; September 17–20, Toronto, CanadaGoogle Scholar
  286. Wu S, Skolnick J, Zhang Y (2007) Ab initio modeling of small proteins by iterative TASSER simulations. BMC Biol 5:17PubMedPubMedCentralCrossRefGoogle Scholar
  287. Xiang Z (2006) Advances in homology protein structure modeling. Curr Protein Pept Sci 7:217–227PubMedPubMedCentralCrossRefGoogle Scholar
  288. Xu Y, Xu D, Uberbacher EC (1998) An efficient computational method for globally optimal threading. J Comput Biol 5:597–614PubMedCrossRefGoogle Scholar
  289. Yan R, Xu D, Yang J, Walker S, Zhang Y (2013) A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure prediction. Sci Rep 3:2619PubMedPubMedCentralCrossRefGoogle Scholar
  290. Yang WW, Pierstorff E (2012) Reservoir-based polymer drug delivery systems. J Lab Autom 17:50–58PubMedCrossRefGoogle Scholar
  291. Yang L, Wang W, Sun Q, Xu F, Niu Y, Wang C, Liang L, Xu P (2016) Development of novel proteasome inhibitors based on phthalazinone scaffold. Bioorg Med Chem Lett 26:2801–2805PubMedCrossRefGoogle Scholar
  292. Yasuo K, Yamaotsu N, Gouda H, Tsujishita H, Hirono S (2009) Structure-based CoMFA as a predictive model – CYP2C9 inhibitors as a test case. J Chem Inf Model 49:853–864PubMedCrossRefGoogle Scholar
  293. Yona G, Levitt M (2002) Within the twilight zone: a sensitive profile-profile comparison tool based on information theory. J Mol Biol 315:1257–1275PubMedCrossRefGoogle Scholar
  294. Yoo SU, Krill SL, Wang Z, Telang C (2009) Miscibility/stability considerations in binary solid dispersion systems composed of functional excipients towards the design of multi-component amorphous systems. J Pharm Sci 98:4711–4723PubMedCrossRefGoogle Scholar
  295. Young T, Abel R, Kim B, Berne BJ, Friesner RA (2007) Motifs for molecular recognition exploiting hydrophobic enclosure in protein-ligand binding. Proc Natl Acad Sci U S A 104:808–813PubMedPubMedCentralCrossRefGoogle Scholar
  296. Yuriev E, Agostino M, Ramsland PA (2011) Challenges and advances in computational docking: 2009 in review. J Mol Recognit 24:149–164PubMedCrossRefGoogle Scholar
  297. Zhang Y, Skolnick J (2004) Automated structure prediction of weakly homologous proteins on a genomic scale. Proc Natl Acad Sci U S A 101:7594–7599PubMedPubMedCentralCrossRefGoogle Scholar
  298. Zhang Y, Arakaki A, Skolnick J (2005) TASSER: an automated method for the prediction of protein tertiary structures in CASP6. Proteins 61:91–98PubMedCrossRefGoogle Scholar
  299. Zhang W, Liu S, Zhou Y (2008) SP5: improving protein fold recognition by using torsion angle profiles and profile-based gap penalty model. PLoS One 3:e2325PubMedPubMedCentralCrossRefGoogle Scholar
  300. Zhang M, Li H, Lang B, O’Donnell K, Zhang H, Wang Z, Dong Y, Wu C, Williams RO (2012) Formulation and delivery of improved amorphous fenofibrate solid dispersions prepared by thin film freezing. Eur J Pharm Biopharm 82:534–544PubMedCrossRefGoogle Scholar
  301. Zhao H, Caflisch A (2013) Discovery of ZAP70 inhibitors by high-throughput docking into a confirmation of its kinase domain generated by molecular dynamics. Bioorg Med Chem Lett 23:5721–5726PubMedCrossRefGoogle Scholar
  302. Zhou H, Skolnick J (2007) Ab initio protein structure prediction using chunk-TASSER. Biophys J 93:1510–1518PubMedPubMedCentralCrossRefGoogle Scholar
  303. Zhou H, Zhou Y (2005) Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments. Proteins 58:6321–6328Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Chandrabose Selvaraj
    • 1
  1. 1.School of Basic Sciences, Indian Institute of Technology, MandiKamandIndia

Personalised recommendations

Cite chapter

Buy options