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Journal of Computer-Aided Molecular Design

, Volume 30, Issue 6, pp 457–469 | Cite as

A pose prediction approach based on ligand 3D shape similarity

  • Ashutosh Kumar
  • Kam Y. J. Zhang
Article

Abstract

Molecular docking predicts the best pose of a ligand in the target protein binding site by sampling and scoring numerous conformations and orientations of the ligand. Failures in pose prediction are often due to either insufficient sampling or scoring function errors. To improve the accuracy of pose prediction by tackling the sampling problem, we have developed a method of pose prediction using shape similarity. It first places a ligand conformation of the highest 3D shape similarity with known crystal structure ligands into protein binding site and then refines the pose by repacking the side-chains and performing energy minimization with a Monte Carlo algorithm. We have assessed our method utilizing CSARdock 2012 and 2014 benchmark exercise datasets consisting of co-crystal structures from eight proteins. Our results revealed that ligand 3D shape similarity could substitute conformational and orientational sampling if at least one suitable co-crystal structure is available. Our method identified poses within 2 Å RMSD as the top-ranking pose for 85.7 % of the test cases. The median RMSD for our pose prediction method was found to be 0.81 Å and was better than methods performing extensive conformational and orientational sampling within target protein binding sites. Furthermore, our method was better than similar methods utilizing ligand 3D shape similarity for pose prediction.

Keywords

Virtual screening Molecular docking Pose prediction Shape similarity 

Notes

Acknowledgments

We acknowledge the Hokusai Greatwave supercomputer at RIKEN for the supercomputing resources used for this study. We thank Toufik Salah for his help in the preparation of data used in some experiments. We thank members of our lab for help and discussions.

Supplementary material

10822_2016_9923_MOESM1_ESM.docx (3.1 mb)
Supplementary material 1 (DOCX 3154 kb)

References

  1. 1.
    Tanrikulu Y, Krüger B, Proschak E (2013) The holistic integration of virtual screening in drug discovery. Drug Discov Today 18:358–364CrossRefGoogle Scholar
  2. 2.
    Walters WP, Stahl MT, Murcko MA (1998) Virtual screening—an overview. Drug Discov Today 3:160–178CrossRefGoogle Scholar
  3. 3.
    Kumar A, Zhang KYJ (2015) Hierarchical virtual screening approaches in small molecule drug discovery. Methods 71:26–37CrossRefGoogle Scholar
  4. 4.
    Lavecchia A, Di Giovanni C (2013) Virtual screening strategies in drug discovery: a critical review. Curr Med Chem 20:2839–2860CrossRefGoogle Scholar
  5. 5.
    Muegge I (2008) Synergies of virtual screening approaches. Mini Rev Med Chem 8:927–933CrossRefGoogle Scholar
  6. 6.
    Muegge I, Oloff S (2006) Advances in virtual screening. Drug Discov Today Technol 3:405–411CrossRefGoogle Scholar
  7. 7.
    Drwal MN, Griffith R (2013) Combination of ligand- and structure-based methods in virtual screening. Drug Discov Today Technol 10:e395–e401CrossRefGoogle Scholar
  8. 8.
    Sliwoski G, Kothiwale S, Meiler J, Lowe EW (2014) Computational methods in drug discovery. Pharmacol Rev 66:334–395CrossRefGoogle Scholar
  9. 9.
    Sukumar N, Das S (2011) Current trends in virtual high throughput screening using ligand-based and structure-based methods. Comb Chem High Throughput Screen 14:872–888CrossRefGoogle Scholar
  10. 10.
    Fukunishi Y (2009) Structure-based drug screening and ligand-based drug screening with machine learning. Comb Chem High Throughput Screen 12:397–408CrossRefGoogle Scholar
  11. 11.
    Yuriev E, Agostino M, Ramsland PA (2011) Challenges and advances in computational docking: 2009 in review. J Mol Recognit 24:149–164CrossRefGoogle Scholar
  12. 12.
    Yuriev E, Holien J, Ramsland PA (2015) Improvements, trends, and new ideas in molecular docking: 2012–2013 in review. J Mol Recognit 28:581–604CrossRefGoogle Scholar
  13. 13.
    Yuriev E, Ramsland PA (2013) Latest developments in molecular docking: 2010–2011 in review. J Mol Recognit 26:215–239CrossRefGoogle Scholar
  14. 14.
    Kutchukian PS, Shakhnovich EI (2010) De novo design: balancing novelty and confined chemical space. Expert Opin Drug Discov 5:789–812CrossRefGoogle Scholar
  15. 15.
    Loving K, Alberts I, Sherman W (2010) Computational approaches for fragment-based and de novo design. Curr Top Med Chem 10:14–32CrossRefGoogle Scholar
  16. 16.
    Dror RO, Green HF, Valant C, Borhani DW, Valcourt JR, Pan AC, Arlow DH, Canals M, Lane JR, Rahmani R, Baell JB, Sexton PM, Christopoulos A, Shaw DE (2013) Structural basis for modulation of a G-protein-coupled receptor by allosteric drugs. Nature 503:295–299Google Scholar
  17. 17.
    Dror RO, Pan AC, Arlow DH, Borhani DW, Maragakis P, Shan Y, Xu H, Shaw DE (2011) Pathway and mechanism of drug binding to G-protein-coupled receptors. Proc Natl Acad Sci USA 108:13118–13123CrossRefGoogle Scholar
  18. 18.
    Buch I, Giorgino T, De Fabritiis G (2011) Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations. Proc Natl Acad Sci USA 108:10184–10189CrossRefGoogle Scholar
  19. 19.
    Huang S-Y, Grinter SZ, Zou X (2010) Scoring functions and their evaluation methods for protein–ligand docking: recent advances and future directions. Phys Chem Chem Phys 12:12899–12908CrossRefGoogle Scholar
  20. 20.
    Wang JC, Lin JH (2013) Scoring functions for prediction of protein–ligand interactions. Curr Pharm Des 19:2174–2182CrossRefGoogle Scholar
  21. 21.
    Muddana HS, Fenley AT, Mobley DL, Gilson MK (2014) The SAMPL4 host-guest blind prediction challenge: an overview. J Comput Aided Mol Des 28:305–317CrossRefGoogle Scholar
  22. 22.
    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 53:1853–1870CrossRefGoogle Scholar
  23. 23.
    Hu B, Lill MA (2013) Exploring the potential of protein-based pharmacophore models in ligand pose prediction and ranking. J Chem Inf Model 53:1179–1190CrossRefGoogle Scholar
  24. 24.
    Hu B, Lill MA (2014) PharmDock: a pharmacophore-based docking program. J Cheminform 6:14CrossRefGoogle Scholar
  25. 25.
    Deng Z, Chuaqui C, Singh J (2004) Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein–ligand binding interactions. J Med Chem 47:337–344CrossRefGoogle Scholar
  26. 26.
    Kelly MD, Mancera RL (2004) Expanded interaction fingerprint method for analyzing ligand binding modes in docking and structure-based drug design. J Chem Inf Comput Sci 44:1942–1951CrossRefGoogle Scholar
  27. 27.
    Marcou G, Rognan D (2007) Optimizing fragment and scaffold docking by use of molecular interaction fingerprints. J Chem Inf Model 47:195–207CrossRefGoogle Scholar
  28. 28.
    Mpamhanga CP, Chen B, McLay IM, Willett P (2006) Knowledge-based interaction fingerprint scoring: a simple method for improving the effectiveness of fast scoring functions. J Chem Inf Model 46:686–698CrossRefGoogle Scholar
  29. 29.
    Perez-Nueno VI, Rabal O, Borrell JI, Teixido J (2009) APIF: a new interaction fingerprint based on atom pairs and its application to virtual screening. J Chem Inf Model 49:1245–1260CrossRefGoogle Scholar
  30. 30.
    Tan L, Batista J, Bajorath J (2010) Computational methodologies for compound database searching that utilize experimental protein–ligand interaction information. Chem Biol Drug Des 76:191–200Google Scholar
  31. 31.
    Hu B, Zhu X, Monroe L, Bures MG, Kihara D (2014) PL-PatchSurfer: a novel molecular local surface-based method for exploring protein–ligand interactions. Int J Mol Sci 15:15122–15145CrossRefGoogle Scholar
  32. 32.
    Zhu X, Shin WH, Kim H, Kihara D (2016 ) Combined approach of patch-surfer and PL-PatchSurfer for protein–ligand binding prediction in CSAR 2013 and 2014. J Chem Inf Model 56:1088–1099CrossRefGoogle Scholar
  33. 33.
    Hare BJ, Walters WP, Caron PR, Bemis GW (2004) CORES: an automated method for generating three-dimensional models of protein/ligand complexes. J Med Chem 47:4731–4740CrossRefGoogle Scholar
  34. 34.
    Ripphausen P, Nisius B, Bajorath J (2011) State-of-the-art in ligand-based virtual screening. Drug Discov Today 16:372–376CrossRefGoogle Scholar
  35. 35.
    Maggiora G, Vogt M, Stumpfe D, Bajorath J (2014) Molecular similarity in medicinal chemistry. J Med Chem 57:3186–3204CrossRefGoogle Scholar
  36. 36.
    Cleves AE, Jain AN (2015) Knowledge-guided docking: accurate prospective prediction of bound configurations of novel ligands using Surflex-Dock. J Comput Aided Mol Des 29:485–509CrossRefGoogle Scholar
  37. 37.
    Fradera X, Knegtel RM, Mestres J (2000) Similarity-driven flexible ligand docking. Proteins 40:623–636CrossRefGoogle Scholar
  38. 38.
    Gao C, Thorsteinson N, Watson I, Wang J, Vieth M (2015) Knowledge-based strategy to improve ligand pose prediction accuracy for lead optimization. J Chem Inf Model 55:1460–1468CrossRefGoogle Scholar
  39. 39.
    Kumar A, Ito A, Takemoto M, Yoshida M, Zhang KYJ (2014) Identification of 1,2,5-oxadiazoles as a new class of SENP2 inhibitors using structure based virtual screening. J Chem Inf Model 54:870–880CrossRefGoogle Scholar
  40. 40.
    Hevener KE, Mehboob S, Su P-C, Truong K, Boci T, Deng J, Ghassemi M, Cook JL, Johnson ME (2012) Discovery of a novel and potent class of F. tularensis Enoyl-Reductase (FabI) inhibitors by molecular shape and electrostatic matching. J Med Chem 55:268–279CrossRefGoogle Scholar
  41. 41.
    Naylor E, Arredouani A, Vasudevan SR, Lewis AM, Parkesh R, Mizote A, Rosen D, Thomas JM, Izumi M, Ganesan A, Galione A, Churchill GC (2009) Identification of a chemical probe for NAADP by virtual screening. Nat Chem Biol 5:220–226CrossRefGoogle Scholar
  42. 42.
    Vasudevan SR, Singh N, Churchill GC (2014) Scaffold hopping with virtual screening from IP3 to a drug-like partial agonist of the inositol trisphosphate receptor. ChemBioChem 15:2774–2782CrossRefGoogle Scholar
  43. 43.
    Rush TS 3rd, Grant JA, Mosyak L, Nicholls A (2005) A shape-based 3-D scaffold hopping method and its application to a bacterial protein-protein interaction. J Med Chem 48:1489–1495CrossRefGoogle Scholar
  44. 44.
    Wu G, Vieth M (2004) SDOCKER: a method utilizing existing X-ray structures to improve docking accuracy. J Med Chem 47:3142–3148CrossRefGoogle Scholar
  45. 45.
    Fukunishi Y, Nakamura H (2008) Prediction of protein–ligand complex structure by docking software guided by other complex structures. J Mol Graph Model 26:1030–1033CrossRefGoogle Scholar
  46. 46.
    Fukunishi Y, Nakamura H (2012) Integration of ligand-based drug screening with structure-based drug screening by combining maximum volume overlapping score with ligand docking. Pharmaceuticals (Basel) 5:1332–1345CrossRefGoogle Scholar
  47. 47.
    Huang SY, Li M, Wang J, Pan Y (2016) HybridDock: a hybrid protein–ligand docking protocol integrating protein- and ligand-based approaches. J Chem Inf Model 56:1078–1087CrossRefGoogle Scholar
  48. 48.
    Liu X, Jiang H, Li H (2011) SHAFTS: a hybrid approach for 3D molecular similarity calculation. 1. Method and assessment of virtual screening. J Chem Inf Model 51:2372–2385CrossRefGoogle Scholar
  49. 49.
    Lu W, Liu X, Cao X, Xue M, Liu K, Zhao Z, Shen X, Jiang H, Xu Y, Huang J, Li H (2011) SHAFTS: a hybrid approach for 3D molecular similarity calculation. 2. Prospective case study in the discovery of diverse p90 ribosomal S6 protein kinase 2 inhibitors to suppress cell migration. J Med Chem 54:3564–3574CrossRefGoogle Scholar
  50. 50.
    McGann M (2012) FRED and HYBRID docking performance on standardized datasets. J Comput Aided Mol Des 26:897–906CrossRefGoogle Scholar
  51. 51.
    Kelley BP, Brown SP, Warren GL, Muchmore SW (2015) POSIT: Flexible Shape-Guided Docking For Pose Prediction. J Chem Inf Model 55:1771–1780CrossRefGoogle Scholar
  52. 52.
    Roy A, Srinivasan B, Skolnick J (2015) PoLi: a virtual screening pipeline based on template pocket and ligand similarity. J Chem Inf Model 55:1757–1770CrossRefGoogle Scholar
  53. 53.
    Kumar A, Zhang KYJ (2016) Application of shape similarity in pose selection and virtual screening in CSARdock2014 exercise. J Chem Inf Model 56:965–973CrossRefGoogle Scholar
  54. 54.
    Bower MJ, Cohen FE, Dunbrack RL Jr (1997) Prediction of protein side-chain rotamers from a backbone-dependent rotamer library: a new homology modeling tool. J Mol Biol 267:1268–1282CrossRefGoogle Scholar
  55. 55.
    Dunbrack RL Jr, Karplus M (1993) Backbone-dependent rotamer library for proteins. Application to side-chain prediction. J Mol Biol 230:543–574CrossRefGoogle Scholar
  56. 56.
    Li Z, Scheraga HA (1987) Monte Carlo-minimization approach to the multiple-minima problem in protein folding. Proc Natl Acad Sci USA 84:6611–6615CrossRefGoogle Scholar
  57. 57.
    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242CrossRefGoogle Scholar
  58. 58.
    Winn MD, Ballard CC, Cowtan KD, Dodson EJ, Emsley P, Evans PR, Keegan RM, Krissinel EB, Leslie AG, McCoy A, McNicholas SJ, Murshudov GN, Pannu NS, Potterton EA, Powell HR, Read RJ, Vagin A, Wilson KS (2011) Overview of the CCP4 suite and current developments. Acta Crystallogr D Biol Crystallogr 67:235–242CrossRefGoogle Scholar
  59. 59.
    Krissinel E, Henrick K (2004) Secondary-structure matching (SSM), a new tool for fast protein structure alignment in three dimensions. Acta Crystallogr D Biol Crystallogr 60:2256–2268CrossRefGoogle Scholar
  60. 60.
    Hawkins PC, Nicholls A (2012) Conformer generation with OMEGA: learning from the data set and the analysis of failures. J Chem Inf Model 52:2919–2936CrossRefGoogle Scholar
  61. 61.
    Hawkins PC, Skillman AG, Warren GL, Ellingson BA, Stahl MT (2010) Conformer generation with OMEGA: algorithm and validation using high quality structures from the protein databank and Cambridge structural database. J Chem Inf Model 50:572–584CrossRefGoogle Scholar
  62. 62.
    Hawkins PC, Skillman AG, Nicholls A (2007) Comparison of shape-matching and docking as virtual screening tools. J Med Chem 50:74–82CrossRefGoogle Scholar
  63. 63.
    Rogers DJ, Tanimoto TT (1960) A computer program for classifying plants. Science 132:1115–1118CrossRefGoogle Scholar
  64. 64.
    Fleishman SJ, Leaver-Fay A, Corn JE, Strauch EM, Khare SD, Koga N, Ashworth J, Murphy P, Richter F, Lemmon G, Meiler J, Baker D (2011) RosettaScripts: a scripting language interface to the Rosetta macromolecular modeling suite. PLoS ONE 6:e20161CrossRefGoogle Scholar
  65. 65.
    Davis IW, Baker D (2009) RosettaLigand docking with full ligand and receptor flexibility. J Mol Biol 385:381–392CrossRefGoogle Scholar
  66. 66.
    Davis IW, Raha K, Head MS, Baker D (2009) Blind docking of pharmaceutically relevant compounds using RosettaLigand. Protein Sci 18:1998–2002CrossRefGoogle Scholar
  67. 67.
    Meiler J, Baker D (2006) ROSETTALIGAND: protein-small molecule docking with full side-chain flexibility. Proteins 65:538–548CrossRefGoogle Scholar
  68. 68.
    DeLuca S, Khar K, Meiler J (2015) Fully flexible docking of medium sized ligand libraries with RosettaLigand. PLoS ONE 10:e0132508CrossRefGoogle Scholar
  69. 69.
    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 47:1739–1749CrossRefGoogle Scholar
  70. 70.
    Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein–ligand complexes. J Med Chem 49:6177–6196CrossRefGoogle Scholar
  71. 71.
    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 47:1750–1759CrossRefGoogle Scholar
  72. 72.
    FRED 3.0.1: OpenEye Scientific Software, Santa Fe, NM. http://www.eyesopen.com
  73. 73.
    Jakalian A, Jack DB, Bayly CI (2002) Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation. J Comput Chem 23:1623–1641CrossRefGoogle Scholar
  74. 74.
    McGann MR, Almond HR, Nicholls A, Grant JA, Brown FK (2003) Gaussian docking functions. Biopolymers 68:76–90CrossRefGoogle Scholar
  75. 75.
    Hawkins PC, Warren GL, Skillman AG, Nicholls A (2008) How to do an evaluation: pitfalls and traps. J Comput Aided Mol Des 22:179–190CrossRefGoogle Scholar
  76. 76.
    Hawkins PC, Kelley BP, Warren GL (2014) The application of statistical methods to cognate docking: a path forward? J Chem Inf Model 54:1339–1355CrossRefGoogle Scholar
  77. 77.
    Bender A, Mussa HY, Glen RC, Reiling S (2004) Similarity searching of chemical databases using atom environment descriptors (MOLPRINT 2D): evaluation of performance. J Chem Inf Comput Sci 44:1708–1718CrossRefGoogle Scholar
  78. 78.
    Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742–754CrossRefGoogle Scholar
  79. 79.
    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 49:5912–5931CrossRefGoogle Scholar
  80. 80.
    Plewczynski D, Lazniewski M, Augustyniak R, Ginalski K (2011) Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database. J Comput Chem 32:742–755CrossRefGoogle Scholar
  81. 81.
    Tuccinardi T, Botta M, Giordano A, Martinelli A (2010) Protein kinases: docking and homology modeling reliability. J Chem Inf Model 50:1432–1441CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Structural Bioinformatics Team, Center for Life Science TechnologiesRIKENYokohamaJapan

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