Skip to main content
SpringerLink
Log in

Molecular Docking to Flexible Targets

  • Protocol
  • First Online:
Molecular Modeling of Proteins

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1215))

Abstract

It is widely accepted that protein receptors exist as an ensemble of conformations in solution. How best to incorporate receptor flexibility into virtual screening protocols used for drug discovery remains a significant challenge. Here, stepwise methodologies are described to generate and select relevant protein conformations for virtual screening in the context of the relaxed complex scheme (RCS), to design small molecule libraries for docking, and to perform statistical analyses on the virtual screening results. Methods include equidistant spacing, RMSD-based clustering, and QR factorization protocols for ensemble generation and ROC analysis for ensemble selection.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Frauenfelder H, Sligar SG, Wolynes PG (1991) The energy landscapes and motions of proteins. Science 254(5038):1598–1603

    Article  PubMed  CAS  Google Scholar 

  2. Boehr DD, Nussinov R, Wright PE (2009) The role of dynamic conformational ensembles in biomolecular recognition. Nat Chem Biol 5(11):789–796. doi:10.1038/nchembio.232

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  3. Forman-Kay JD (1999) The “dynamics” in the thermodynamics of binding. Nat Struct Biol 6(12):1086–1087. doi:10.1038/70008

    Article  PubMed  CAS  Google Scholar 

  4. Cross JB, Thompson DC, Rai BK, Baber JC, Fan KY, Hu Y, Humblet C (2009) Comparison of several molecular docking programs: pose prediction and virtual screening accuracy. J Chem Inf Model 49(6):1455–1474. doi:10.1021/ci900056c

    Article  PubMed  CAS  Google Scholar 

  5. Cheng T, Li X, Li Y, Liu Z, Wang R (2009) Comparative assessment of scoring functions on a diverse test set. J Chem Inf Model 49(4):1079–1093. doi:10.1021/ci9000053

    Article  PubMed  CAS  Google Scholar 

  6. Armen RS, Chen J, Brooks CL 3rd (2009) An evaluation of explicit receptor flexibility in molecular docking using molecular dynamics and torsion angle molecular dynamics. J Chem Theory Comput 5(10):2909–2923. doi:10.1021/ct900262t

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  7. Sutherland JJ, Nandigam RK, Erickson JA, Vieth M (2007) Lessons in molecular recognition. 2. Assessing and improving cross-docking accuracy. J Chem Inf Model 47(6):2293–2302. doi:10.1021/ci700253h

    Article  PubMed  CAS  Google Scholar 

  8. Verkhivker GM, Bouzida D, Gehlhaar DK, Rejto PA, Freer ST, Rose PW (2002) Complexity and simplicity of ligand-macromolecule interactions: the energy landscape perspective. Curr Opin Struct Biol 12(2):197–203

    Article  PubMed  CAS  Google Scholar 

  9. Lin J-H, Perryman AL, Schames JR, McCammon JA (2002) Computational drug design accommodating receptor flexibility: the relaxed complex scheme. J Am Chem Soc 124(20):5632–5633. doi:10.1021/ja0260162

    Article  PubMed  CAS  Google Scholar 

  10. Teague SJ (2003) Implications of protein flexibility for drug discovery. Nat Rev Drug Discov 2(7):527–541. doi:10.1038/nrd1129

    Article  PubMed  CAS  Google Scholar 

  11. Cozzini P, Kellogg GE, Spyrakis F, Abraham DJ, Costantino G, Emerson A, Fanelli F, Gohlke H, Kuhn LA, Morris GM, Orozco M, Pertinhez TA, Rizzi M, Sotriffer CA (2008) Target flexibility: an emerging consideration in drug discovery and design. J Med Chem 51(20):6237–6255. doi:10.1021/jm800562d

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  12. Schames JR, Henchman RH, Siegel JS, Sotriffer CA, Ni H, McCammon JA (2004) Discovery of a novel binding trench in HIV integrase. J Med Chem 47(8):1879–1881. doi:10.1021/jm0341913

    Article  PubMed  CAS  Google Scholar 

  13. Gorfe AA, Caflisch A (2005) Functional plasticity in the substrate binding site of beta-secretase. Structure 13(10):1487–1498. doi:10.1016/j.str.2005.06.015

    Article  PubMed  CAS  Google Scholar 

  14. Cheng LS, Amaro RE, Xu D, Li WW, Arzberger PW, McCammon JA (2008) Ensemble-based virtual screening reveals potential novel antiviral compounds for avian influenza neuraminidase. J Med Chem 51(13):3878–3894. doi:10.1021/jm8001197

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  15. Baron R, McCammon JA (2007) Dynamics, hydration, and motional averaging of a loop-gated artificial protein cavity: the W191G mutant of cytochrome c peroxidase in water as revealed by molecular dynamics simulations. Biochemistry 46(37):10629–10642. doi:10.1021/bi700866x

    Article  PubMed  CAS  Google Scholar 

  16. Yuriev E, Agostino M, Ramsland PA (2011) Challenges and advances in computational docking: 2009 in review. J Mol Recognit 24(2):149–164. doi:10.1002/jmr.1077

    Article  PubMed  CAS  Google Scholar 

  17. Lavecchia A, Di Giovanni C (2013) Virtual screening strategies in drug discovery: a critical review. Curr Med Chem 20(23):2839–2860

    Article  PubMed  CAS  Google Scholar 

  18. B-Rao C, Subramanian J, Sharma SD (2009) Managing protein flexibility in docking and its applications. Drug Discov Today 14(7–8):394–400. doi:10.1016/j.drudis.2009.01.003

    Article  PubMed  CAS  Google Scholar 

  19. Sinko W, Lindert S, McCammon JA (2013) Accounting for receptor flexibility and enhanced sampling methods in computer-aided drug design. Chem Biol Drug Des 81(1):41–49. doi:10.1111/cbdd.12051

    Article  PubMed  CAS  Google Scholar 

  20. Jiang F, Kim SH (1991) “Soft docking”: matching of molecular surface cubes. J Mol Biol 219(1):79–102

    Article  PubMed  CAS  Google Scholar 

  21. Cerqueira NM, Bras NF, Fernandes PA, Ramos MJ (2009) MADAMM: a multistaged docking with an automated molecular modeling protocol. Proteins 74(1):192–206. doi:10.1002/prot.22146

    Article  PubMed  CAS  Google Scholar 

  22. Sherman W, Day T, Jacobson MP, Friesner RA, Farid R (2006) Novel procedure for modeling ligand/receptor induced fit effects. J Med Chem 49(2):534–553. doi:10.1021/jm050540c

    Article  PubMed  CAS  Google Scholar 

  23. Sherman W, Beard HS, Farid R (2006) Use of an induced fit receptor structure in virtual screening. Chem Biol Drug Des 67(1):83–84. doi:10.1111/j.1747-0285.2005.00327.x

    Article  PubMed  CAS  Google Scholar 

  24. Jain AN (2009) Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation. J Comput Aided Mol Des 23(6):355–374. doi:10.1007/s10822-009-9266-3

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  25. Davis IW, Baker D (2009) RosettaLigand docking with full ligand and receptor flexibility. J Mol Biol 385(2):381–392. doi:10.1016/j.jmb.2008.11.010

    Article  PubMed  CAS  Google Scholar 

  26. Lemmon G, Meiler J (2012) Rosetta ligand docking with flexible XML protocols. Methods Mol Biol 819:143–155. doi:10.1007/978-1-61779-465-0_10

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  27. Abagyan R, Totrov M, Kuznetsov D (1994) ICM – a new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation. J Comput Chem 15(5):488–506. doi:10.1002/jcc.540150503

    Article  CAS  Google Scholar 

  28. Corbeil CR, Moitessier N (2009) Docking ligands into flexible and solvated macromolecules. 3. Impact of input ligand conformation, protein flexibility, and water molecules on the accuracy of docking programs. J Chem Inf Model 49(4):997–1009. doi:10.1021/ci8004176

    Article  PubMed  CAS  Google Scholar 

  29. Cavasotto CN, Kovacs JA, Abagyan RA (2005) Representing receptor flexibility in ligand docking through relevant normal modes. J Am Chem Soc 127(26):9632–9640. doi:10.1021/ja042260c

    Article  PubMed  CAS  Google Scholar 

  30. Cavasotto CN (2012) Normal mode-based approaches in receptor ensemble docking. Methods Mol Biol 819:157–168. doi:10.1007/978-1-61779-465-0_11

    Article  PubMed  CAS  Google Scholar 

  31. Nichols SE, Baron R, Ivetac A, McCammon JA (2011) Predictive power of molecular dynamics receptor structures in virtual screening. J Chem Inf Model 51(6):1439–1446. doi:10.1021/ci200117n

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  32. Amaro RE, Baron R, McCammon JA (2008) An improved relaxed complex scheme for receptor flexibility in computer-aided drug design. J Comput Aided Mol Des 22(9):693–705. doi:10.1007/s10822-007-9159-2

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  33. Lin J-H, Perryman AL, Schames JR, McCammon JA (2003) The relaxed complex method: accommodating receptor flexibility for drug design with an improved scoring scheme. Biopolymers 68(1):47–62. doi:10.1002/bip.10218

    Article  PubMed  CAS  Google Scholar 

  34. Schnaufer A, Ernst NL, Palazzo SS, O'Rear J, Salavati R, Stuart K (2003) Separate insertion and deletion subcomplexes of the Trypanosoma brucei RNA editing complex. Mol Cell 12(2):307–319

    Article  PubMed  CAS  Google Scholar 

  35. Landon MR, Amaro RE, Baron R, Ngan CH, Ozonoff D, Andrew McCammon J, Vajda S (2008) Novel druggable hot spots in avian influenza neuraminidase H5N1 revealed by computational solvent mapping of a reduced and representative receptor ensemble. Chem Biol Drug Des 71(2):106–116. doi:10.1111/j.1747-0285.2007.00614.x

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  36. Babakhani A, Talley TT, Taylor P, McCammon JA (2009) A virtual screening study of the acetylcholine binding protein using a relaxed-complex approach. Comput Biol Chem 33(2):160–170. doi:10.1016/j.compbiolchem.2008.12.002

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  37. Durrant JD, de Oliveira CAF, McCammon JA (2010) Including receptor flexibility and induced fit effects into the design of MMP-2 inhibitors. J Mol Recognit 23(2):173–182. doi:10.1002/jmr.989

    PubMed  CAS  PubMed Central  Google Scholar 

  38. Demir Ö, Baronio R, Salehi F, Wassman CD, Hall L, Hatfield GW, Chamberlin R, Kaiser P, Lathrop RH, Amaro RE (2011) Ensemble-based computational approach discriminates functional activity of p53 cancer and rescue mutants. PLoS Comput Biol 7(10):e1002238. doi:10.1371/journal.pcbi.1002238

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  39. Amaro RE, Schnaufer A, Interthal H, Hol W, Stuart KD, McCammon JA (2008) Discovery of drug-like inhibitors of an essential RNA-editing ligase in Trypanosoma brucei. Proc Natl Acad Sci U S A 105(45):17278–17283. doi:10.1073/pnas.0805820105

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  40. Durrant JD, Hall L, Swift RV, Landon M, Schnaufer A, Amaro RE (2010) Novel naphthalene-based inhibitors of Trypanosoma brucei RNA editing ligase 1. PLoS Negl Trop Dis 4(8):e803. doi:10.1371/journal.pntd.0000803

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  42. Wang Y, Harrison CB, Schulten K, McCammon JA (2011) Implementation of accelerated molecular dynamics in NAMD. Comput Sci Discov 4(1):015002

    Article  PubMed  PubMed Central  Google Scholar 

  43. Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C (2006) Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 65(3):712–725. doi:10.1002/prot.21123

    Article  PubMed  CAS  Google Scholar 

  44. 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): 1750–1759. doi:10.1021/jm030644s

    Article  PubMed  CAS  Google Scholar 

  45. 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(7):1739–1749. doi:10.1021/jm0306430

    Article  PubMed  CAS  Google Scholar 

  46. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461. doi:10.1002/jcc.21334

    PubMed  CAS  PubMed Central  Google Scholar 

  47. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14(1):33–38, 27–38

    Article  PubMed  CAS  Google Scholar 

  48. Deng J, Schnaufer A, Salavati R, Stuart KD, Hol WG (2004) High resolution crystal structure of a key editosome enzyme from Trypanosoma brucei: RNA editing ligase 1. J Mol Biol 343(3):601–613. doi:10.1016/j.jmb.2004.08.041

    Article  PubMed  CAS  Google Scholar 

  49. Drug Discovery Unit UoD DDU Library Collections (2013) http://www.drugdiscovery.dundee.ac.uk/libraries.html

  50. Mysinger MM, Carchia M, Irwin JJ, Shoichet BK (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55(14):6582–6594. doi:10.1021/jm300687e

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  51. Moshiri H, Acoca S, Kala S, Najafabadi HS, Hogues H, Purisima E, Salavati R (2011) Naphthalene-based RNA editing inhibitor blocks RNA editing activities and editosome assembly in Trypanosoma brucei. J Biol Chem 286(16):14178–14189. doi:10.1074/jbc.M110.199646

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  52. Salavati R, Moshiri H, Kala S, Shateri Najafabadi H (2012) Inhibitors of RNA editing as potential chemotherapeutics against trypanosomatid pathogens. Int J Parasitol Drugs Drug Resist 2:36–46. doi:10.1016/j.ijpddr.2011.10.003

    Article  PubMed  PubMed Central  Google Scholar 

  53. Sørensen J, Palmer DS, Qvist KB, Schiøtt B (2011) Initial stage of cheese production: a molecular modeling study of bovine and camel chymosin complexed with peptides from the chymosin-sensitive region of kappa-casein. J Agric Food Chem 59(10):5636–5647. doi:10.1021/jf104898w

    Article  PubMed  Google Scholar 

  54. Feher VA, Lawson JD (2009) Approaches to kinase homology modeling: successes and considerations for the structural kinome. In: Rongshi L, Stafford JA (eds) Kinase inhibitor drugs. Wiley, Hoboken, NJ, pp 433–460. doi: 10.1002/9780470524961.ch17

  55. Marti-Renom MA, Stuart AC, Fiser A, Sanchez R, Melo F, Sali A (2000) Comparative protein structure modeling of genes and genomes. Annu Rev Biophys Biomol Struct 29:291–325. doi:10.1146/annurev.biophys.29.1.291

    Article  PubMed  CAS  Google Scholar 

  56. Cavasotto CN, Phatak SS (2009) Homology modeling in drug discovery: current trends and applications. Drug Discov Today 14(13–14):676–683. doi:10.1016/j.drudis.2009.04.006

    Article  PubMed  CAS  Google Scholar 

  57. Damm-Ganamet KL, Smith RD, Dunbar JB Jr, Stuckey JA, Carlson HA (2013) CSAR benchmark exercise 2011–2012: evaluation of results from docking and relative ranking of blinded congeneric series. J Chem Inf Model. doi:10.1021/ci400025f

    Google Scholar 

  58. Hamelberg D, Mongan J, McCammon JA (2004) Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. J Chem Phys 120(24): 11919–11929. doi:10.1063/1.1755656

    Article  PubMed  CAS  Google Scholar 

  59. Pierce LCT, Salomon-Ferrer R, Augusto F, de Oliveira C, McCammon JA, Walker RC (2012) Routine access to millisecond time scale events with accelerated molecular dynamics. J Chem Theory Comput 8(9):2997–3002. doi:10.1021/ct300284c

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  60. Swift RV, Durrant J, Amaro RE, McCammon JA (2009) Toward understanding the conformational dynamics of RNA ligation. Biochemistry 48(4):709–719. doi:10.1021/bi8018114

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  61. Allnér O, Nilsson L, Villa A (2012) Magnesium ion–water coordination and exchange in biomolecular simulations. J Chem Theory Comput 8(4):1493–1502. doi:10.1021/ct3000734

    Article  Google Scholar 

  62. Horn HW, Swope WC, Pitera JW, Madura JD, Dick TJ, Hura GL, Head-Gordon T (2004) Development of an improved four-site water model for biomolecular simulations: TIP4P-Ew. J Chem Phys 120(20):9665–9678. doi:10.1063/1.1683075

    Article  PubMed  CAS  Google Scholar 

  63. Demir O, Amaro RE (2013) Designing novel inhibitors of Trypanosoma brucei. In: Kortagere S (ed) Methods in molecular biology: in silico models for drug discovery, vol 993. Humana Press, Totowa, NJ, pp 231–243, doi: 10.1007/978-1-62703-342-8_15

    Google Scholar 

  64. Amaro RE, Swift RV, McCammon JA (2007) Functional and structural insights revealed by molecular dynamics simulations of an essential RNA editing ligase in Trypanosoma brucei. PLoS Negl Trop Dis 1(2):e68. doi:10.1371/journal.pntd.0000068

    Article  PubMed  PubMed Central  Google Scholar 

  65. Shang Y, Simmerling C (2012) Molecular dynamics applied in drug discovery: the case of HIV-1 protease. Methods Mol Biol 819:527–549. doi:10.1007/978-1-61779-465-0_31

    Article  PubMed  CAS  Google Scholar 

  66. Nichols S, Baron R, McCammon JA (2012) On the use of molecular dynamics receptor conformations for virtual screening. In: Baron R (ed) Computational drug discovery and design, vol 819, Methods in molecular biology. Springer, New York, pp 93–103

    Chapter  Google Scholar 

  67. O’Donoghue P, Luthey-Schulten Z (2003) On the evolution of structure in aminoacyl-tRNA synthetases. Microbiol Mol Biol Rev 67(4):550–573. doi:10.1128/MMBR.67.4.550-573.2003

    Article  PubMed  PubMed Central  Google Scholar 

  68. Baron R, McCammon JA (2008) (Thermo)dynamic role of receptor flexibility, entropy, and motional correlation in protein–ligand binding. Chem Phys Chem 9(7):983–988. doi:10.1002/cphc.200700857

    PubMed  CAS  Google Scholar 

  69. Shao J, Tanner SW, Thompson N, Cheatham TE (2007) Clustering molecular dynamics trajectories: 1. Characterizing the performance of different clustering algorithms. J Chem Theory Comput 3(6):2312–2334. doi:10.1021/ct700119m

    Article  CAS  Google Scholar 

  70. Osguthorpe DJ, Sherman W, Hagler AT (2012) Generation of receptor structural ensembles for virtual screening using binding site shape analysis and clustering. Chem Biol Drug Des 80(2):182–193. doi:10.1111/j.1747-0285.2012.01396.x

    Article  PubMed  CAS  Google Scholar 

  71. Osguthorpe DJ, Sherman W, Hagler AT (2012) Exploring protein flexibility: incorporating structural ensembles from crystal structures and simulation into virtual screening protocols. J Phys Chem B 116(23):6952–6959. doi:10.1021/jp3003992

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  72. Schmidtke P, Barril X (2010) Understanding and predicting druggability. A high-throughput method for detection of drug binding sites. J Med Chem 53(15):5858–5867. doi:10.1021/jm100574m

    Article  PubMed  CAS  Google Scholar 

  73. Le Guilloux V, Schmidtke P, Tuffery P (2009) Fpocket: an open source platform for ligand pocket detection. BMC Bioinformatics 10:168. doi:10.1186/1471-2105-10-168

    Article  PubMed  PubMed Central  Google Scholar 

  74. Durrant JD, de Oliveira CAF, McCammon JA (2011) POVME: an algorithm for measuring binding-pocket volumes. J Mol Graph Model 29(5):773–776. doi:10.1016/j.jmgm.2010.10.007

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  75. Roe DR, Cheatham TE (2013) PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput 9(7):3084–3095. doi:10.1021/ct400341p

    Article  CAS  Google Scholar 

  76. Salomon-Ferrer R, Case DA, Walker RC (2013) An overview of the Amber biomolecular simulation package. Wiley Interdiscip Rev Comput Mol Sci 3(2):198–210. doi:10.1002/wcms.1121

    Article  CAS  Google Scholar 

  77. Case DA, Darden TA, Cheatham III TE, Simmerling CL, Wang J, Duke RE, Luo R, Walker RC, Zhang W, Merz KM, Roberts B, Hayik S, Roitberg A, Seabra G, Swails J, Goetz AW, Kolossváry I, Wong KF, Paesani F, Vanicek J, Wolf RM, Liu J, Wu X, Brozell SR, Steinbrecher T, Gohlke H, Cai Q, Ye X, Wang J, Hsieh M-J, Cui G, Roe DR, Mathews DH, Seetin MG, Salomon-Ferrer R, Sagui C, Babin V, Luchko T, Gusarov S, Kovalenko A, Kollman PA (2012) AMBER. 12 edn. University of California, San Francisco, CA, USA

    Google Scholar 

  78. Case DA, Cheatham TE, Darden T, Gohlke H, Luo R, Merz KM, Onufriev A, Simmerling C, Wang B, Woods RJ (2005) The Amber biomolecular simulation programs. J Comput Chem 26(16):1668–1688. doi:10.1002/jcc.20290

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  79. Kozakov D, Hall DR, Chuang G-Y, Cencic R, Brenke R, Grove LE, Beglov D, Pelletier J, Whitty A, Vajda S (2011) Structural conservation of druggable hot spots in protein–protein interfaces. Proc Natl Acad Sci 108(33):13528–13533. doi:10.1073/pnas.1101835108

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  80. Brenke R, Kozakov D, Chuang G-Y, Beglov D, Hall D, Landon MR, Mattos C, Vajda S (2009) Fragment-based identification of druggable “hot spots” of proteins using Fourier domain correlation techniques. Bioinformatics 25(5):621–627. doi:10.1093/bioinformatics/btp036

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  81. Brenk R, Schipani A, James D, Krasowski A, Gilbert IH, Frearson J, Wyatt PG (2008) Lessons learnt from assembling screening libraries for drug discovery for neglected diseases. Chem Med Chem 3(3):435–444. doi:10.1002/cmdc.200700139

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  82. Rishton GM (2003) Nonleadlikeness and leadlikeness in biochemical screening. Drug Discov Today 8(2):86–96

    Article  PubMed  CAS  Google Scholar 

  83. Seidler J, McGovern SL, Doman TN, Shoichet BK (2003) Identification and prediction of promiscuous aggregating inhibitors among known drugs. J Med Chem 46(21):4477–4486. doi:10.1021/jm030191r

    Article  PubMed  CAS  Google Scholar 

  84. McGovern SL, Caselli E, Grigorieff N, Shoichet BK (2002) A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening. J Med Chem 45(8):1712–1722

    Article  PubMed  CAS  Google Scholar 

  85. Baell JB, Holloway GA (2010) New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem 53(7):2719–2740. doi:10.1021/jm901137j

    Article  PubMed  CAS  Google Scholar 

  86. Walters WP, Stahl MT, Murcko MA (1998) Virtual screening – an overview. Drug Discov Today 3(4):160–178

    Article  CAS  Google Scholar 

  87. Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52(7):1757–1768. doi:10.1021/ci3001277

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  88. Warren GL, Andrews CW, Capelli AM, Clarke B, LaLonde J, Lambert MH, Lindvall M, Nevins N, Semus SF, Senger S, Tedesco G, Wall ID, Woolven JM, Peishoff CE, Head MS (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49(20):5912–5931. doi:10.1021/jm050362n

    Article  PubMed  CAS  Google Scholar 

  89. Nicholls A (2011) What do we know?: simple statistical techniques that help. In: Bajorath J (ed) Chemoinformatics and computational chemical biology, vol 672, Methods in molecular biology. Humana Press, Totowa, NJ, pp 531–581. doi:10.1007/978-1-60761-839-3_22

    Chapter  Google Scholar 

  90. Jain AN (2008) Bias, reporting, and sharing: computational evaluations of docking methods. J Comput Aided Mol Des 22(3–4):201–212. doi:10.1007/s10822-007-9151-x

    Article  PubMed  CAS  Google Scholar 

  91. Nichols SE, Swift RV, Amaro RE (2012) Rational prediction with molecular dynamics for hit identification. Curr Top Med Chem 12(18):2002–2012. doi:10.2174/156802612804910313

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  92. du Prel JB, Hommel G, Rohrig B, Blettner M (2009) Confidence interval or p-value?: part 4 of a series on evaluation of scientific publications. Dtsch Arztebl Int 106(19):335–339. doi: D – NLM: PMC2689604 OTO – NOTNLM

    Google Scholar 

  93. Craig IR, Essex JW, Spiegel K (2010) Ensemble docking into multiple crystallographically derived protein structures: an evaluation based on the statistical analysis of enrichments. J Chem Inf Model 50(4):511–524. doi:10.1021/ci900407c

    Article  PubMed  CAS  Google Scholar 

  94. Bucher D, Grant BJ, Markwick PR, McCammon JA (2011) Accessing a hidden conformation of the maltose binding protein using accelerated molecular dynamics. PLoS Comput Biol 7(4):e1002034. doi:10.1371/journal.pcbi.1002034

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  95. Votapka L, Amaro RE (2013) Multistructural hot spot characterization with FTProd. Bioinformatics 29(3):393–394. doi:10.1093/bioinformatics/bts689

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  96. 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(9):2817–2831. doi:10.1021/ja0771033

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  97. Beuming T, Farid R, Sherman W (2009) High-energy water sites determine peptide binding affinity and specificity of PDZ domains. Protein Sci 18(8):1609–1619. doi:10.1002/pro.177

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  98. Thomsen R, Christensen MH (2006) MolDock: a new technique for high-accuracy molecular docking. J Med Chem 49(11):3315–3321. doi:10.1021/jm051197e

    Article  PubMed  CAS  Google Scholar 

  99. Lie MA, Thomsen R, Pedersen CNS, Schiøtt B, Christensen MH (2011) Molecular docking with ligand attached water molecules. J Chem Inf Model 51(4):909–917. doi:10.1021/ci100510m

    Article  PubMed  CAS  Google Scholar 

  100. Truchon J-F, Bayly CI (2007) Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem. J Chem Inf Model 47(2):488–508. doi:10.1021/ci600426e

    Article  PubMed  CAS  Google Scholar 

  101. Sheridan RP, Singh SB, Fluder EM, Kearsley SK (2001) Protocols for bridging the peptide to nonpeptide gap in topological similarity searches. J Chem Inf Comput Sci 41(5):1395–1406. doi:10.1021/ci0100144

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

This work was funded in part by through the NIH Director’s New Innovator Award Program DP2-OD007237 and the National Science Foundation’s XSEDE Supercomputer resources grant LRAC CHE060073N to R.E.A. Support from the National Biomedical Computation Resource (P41 GM103426), the Center for Theoretical Biophysics, and UCSD Drug Discovery Institute is gratefully acknowledged. J.S. thanks the Alfred Benzon Foundation for a generous postdoctoral fellowship.

Author information

Authors and Affiliations

  1. Department of Chemistry and Biochemistry, University of California, 3234 Urey Hall, MC-0340, San Diego, La Jolla, CA, 92093-0340, USA

    Jesper Sørensen, Özlem Demir, Robert V. Swift, Victoria A. Feher & Rommie E. Amaro

Authors
  1. Jesper Sørensen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Özlem Demir
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Robert V. Swift
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Victoria A. Feher
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rommie E. Amaro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rommie E. Amaro .

Editor information

Editors and Affiliations

  1. University of Hertfordshire, Hatfield, Hertfordshire, United Kingdom

    Andreas Kukol

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this protocol

Cite this protocol

Sørensen, J., Demir, Ö., Swift, R.V., Feher, V.A., Amaro, R.E. (2015). Molecular Docking to Flexible Targets. In: Kukol, A. (eds) Molecular Modeling of Proteins. Methods in Molecular Biology, vol 1215. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1465-4_20

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-1465-4_20

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-1464-7

  • Online ISBN: 978-1-4939-1465-4

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation