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Shape similarity guided pose prediction: lessons from D3R Grand Challenge 3

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Abstract

To extend the utility of ligand 3D shape similarity into pose prediction and virtual screening, we have previously developed CDVS and PoPSS methods. Both of them utilize ligand 3D shape similarity with the crystallographic ligands to improve pose prediction. While CDVS utilizes shape similarity to select suitable receptor structures for molecular docking, PoPSS places a ligand conformation of the highest shape similarity with crystal ligands into the target protein binding pocket which is then refined by side-chain repacking and Monte Carlo energy minimization. Analyses of PoPSS revealed some drawbacks in ligand conformation generation and the scoring scheme used. Moreover, as PoPSS does not sample the ligand conformation after placing it in the binding pocket, it relies solely on conformation generation methods to produce native like conformations. To address these limitations of PoPSS method, we report here a modified approach named as PoPSS-Lite, where side-chain repacking was replaced by a simple grid-based energy minimization. This modification also allowed the sampling of terminal functional groups while keeping the core scaffold fixed. Furthermore, shape similarity calculations were improved by increasing the number of ligand conformations and using a different similarity metric. The performance of PoPSS-Lite was prospectively evaluated in D3R GC3. Comparison of PoPSS-Lite demonstrated superior performance over PoPSS and CDVS with lower mean and median RMSDs. Furthermore, comparison with other D3R GC3 pose prediction submissions revealed top performance for PoPSS-Lite. Our D3R GC3 result extends our perspective that ligand 3D shape similarity with known crystallographic information can be successfully used to predict the binding pose of ligands with unknown binding modes. Our D3R GC3 results further highlight the necessity for improvement in conformer generation methods in order to improve shape similarity guided pose prediction.

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References

  1. Maggiora G, Vogt M, Stumpfe D, Bajorath J (2014) Molecular similarity in medicinal chemistry. J Med Chem 57(8):3186–3204. https://doi.org/10.1021/jm401411z

    Article  CAS  PubMed  Google Scholar 

  2. Zauhar RJ, Moyna G, Tian L, Li Z, Welsh WJ (2003) Shape signatures: a new approach to computer-aided ligand- and receptor-based drug design. J Med Chem 46(26):5674–5690. https://doi.org/10.1021/jm030242k

    Article  CAS  PubMed  Google Scholar 

  3. 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(5):1489–1495. https://doi.org/10.1021/jm040163o

    Article  CAS  PubMed  Google Scholar 

  4. Kortagere S, Krasowski MD, Ekins S (2009) The importance of discerning shape in molecular pharmacology. Trends Pharmacol Sci 30(3):138–147. https://doi.org/10.1016/j.tips.2008.12.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Schnecke V, Bostrom J (2006) Computational chemistry-driven decision making in lead generation. Drug Discov Today 11(1–2):43–50. https://doi.org/10.1016/S1359-6446(05)03703-7

    Article  CAS  PubMed  Google Scholar 

  6. Ballester PJ, Westwood I, Laurieri N, Sim E, Richards WG (2010) Prospective virtual screening with Ultrafast Shape Recognition: the identification of novel inhibitors of arylamine N-acetyltransferases. J R Soc Interface 7(43):335–342. https://doi.org/10.1098/rsif.2009.0170

    Article  CAS  PubMed  Google Scholar 

  7. Hoeger B, Diether M, Ballester PJ, Kohn M (2014) Biochemical evaluation of virtual screening methods reveals a cell-active inhibitor of the cancer-promoting phosphatases of regenerating liver. Eur J Med Chem 88:89–100. https://doi.org/10.1016/j.ejmech.2014.08.060

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Patil SP, Ballester PJ, Kerezsi CR (2014) Prospective virtual screening for novel p53-MDM2 inhibitors using ultrafast shape recognition. J Comput Aided Mol Des 28(2):89–97. https://doi.org/10.1007/s10822-014-9732-4

    Article  CAS  PubMed  Google Scholar 

  9. Boström J, Berggren K, Elebring T, Greasley PJ, Wilstermann M (2007) Scaffold hopping, synthesis and structure–activity relationships of 5,6-diaryl-pyrazine-2-amide derivatives: a novel series of CB1 receptor antagonists. Bioorg Med Chem 15(12):4077–4084. https://doi.org/10.1016/j.bmc.2007.03.075

    Article  CAS  PubMed  Google Scholar 

  10. Freitas RF, Oprea TI, Montanari CA (2008) 2D QSAR and similarity studies on cruzain inhibitors aimed at improving selectivity over cathepsin L. Bioorg Med Chem 16(2):838–853. https://doi.org/10.1016/j.bmc.2007.10.048

    Article  CAS  PubMed  Google Scholar 

  11. Temml V, Voss CV, Dirsch VM, Schuster D (2014) Discovery of new liver X receptor agonists by pharmacophore modeling and shape-based virtual screening. J Chem Inf Model 54(2):367–371. https://doi.org/10.1021/ci400682b

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kumar A, Parkesh R, Sznajder LJ, Childs-Disney JL, Sobczak K, Disney MD (2012) Chemical correction of pre-mRNA splicing defects associated with sequestration of muscleblind-like 1 protein by expanded r(CAG)-containing transcripts. ACS Chem Biol 7(3):496–505. https://doi.org/10.1021/cb200413a

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Vasudevan SR, Moore JB, Schymura Y, Churchill GC (2012) Shape-based reprofiling of FDA-approved drugs for the H1 histamine receptor. J Med Chem 55(16):7054–7060. https://doi.org/10.1021/jm300671m

    Article  CAS  PubMed  Google Scholar 

  14. Sun H, Xu X, Wu X, Zhang X, Liu F, Jia J, Guo X, Huang J, Jiang Z, Feng T, Chu H, Zhou Y, Zhang S, Liu Z, You Q (2013) Discovery and design of tricyclic scaffolds as protein kinase CK2 (CK2) inhibitors through a combination of shape-based virtual screening and structure-based molecular modification. J Chem Inf Model 53(8):2093–2102. https://doi.org/10.1021/ci400114f

    Article  CAS  PubMed  Google Scholar 

  15. Chen W-L, Wang Z-H, Feng T-T, Li D-D, Wang C-H, Xu X-L, Zhang X-J, You Q-D, Guo X-K (2016) Discovery, design and synthesis of 6H-anthra[1,9-cd]isoxazol-6-one scaffold as G9a inhibitor through a combination of shape-based virtual screening and structure-based molecular modification. Bioorg Med Chem 24(22):6102–6108. https://doi.org/10.1016/j.bmc.2016.09.071

    Article  CAS  PubMed  Google Scholar 

  16. Bassetto M, Leyssen P, Neyts J, Yerukhimovich MM, Frick DN, Brancale A (2017) Shape-based virtual screening, synthesis and evaluation of novel pyrrolone derivatives as antiviral agents against HCV. Bioorg Med Chem Lett 27(4):936–940. https://doi.org/10.1016/j.bmcl.2016.12.087

    Article  CAS  PubMed  Google Scholar 

  17. Hevener KE, Mehboob S, Su P-C, Truong K, Boci T, Deng J, Ghassemi M, Cook JL, Johnson ME (2011) Discovery of a novel and potent class of F. tularensis enoyl-reductase (FabI) inhibitors by molecular shape and electrostatic matching. J Med Chem 55(1):268–279. https://doi.org/10.1021/jm201168g

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Kaoud TS, Yan C, Mitra S, Tseng C-C, Jose J, Taliaferro JM, Tuohetahuntila M, Devkota A, Sammons R, Park J, Park H, Shi Y, Hong J, Ren P, Dalby KN (2012) From in silico discovery to intracellular activity: targeting JNK–protein interactions with small molecules. ACS Med Chem Lett 3(9):721–725. https://doi.org/10.1021/ml300129b

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 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(4):220–226. https://doi.org/10.1038/nchembio.150

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Bostrom J, Grant JA, Fjellstrom O, Thelin A, Gustafsson D (2013) Potent fibrinolysis inhibitor discovered by shape and electrostatic complementarity to the drug tranexamic acid. J Med Chem 56(8):3273–3280. https://doi.org/10.1021/jm301818g

    Article  CAS  PubMed  Google Scholar 

  21. Kumar A, Ito A, Hirohama M, Yoshida M, Zhang KYJ (2016) Identification of new SUMO activating enzyme 1 inhibitors using virtual screening and scaffold hopping. Bioorg Med Chem Lett 26(4):1218–1223. https://doi.org/10.1016/j.bmcl.2016.01.030

    Article  CAS  PubMed  Google Scholar 

  22. Kong Y, Bender A, Yan A (2018) Identification of novel aurora kinase A (AURKA) inhibitors via hierarchical ligand-based virtual screening. J Chem Inf Model 58(1):36–47. https://doi.org/10.1021/acs.jcim.7b00300

    Article  CAS  PubMed  Google Scholar 

  23. Wu G, Vieth M (2004) SDOCKER: a method utilizing existing X-ray structures to improve docking accuracy. J Med Chem 47(12):3142–3148. https://doi.org/10.1021/jm040015y

    Article  CAS  PubMed  Google Scholar 

  24. Fukunishi Y, Nakamura H (2008) Prediction of protein-ligand complex structure by docking software guided by other complex structures. J Mol Graph Model 26(6):1030–1033. https://doi.org/10.1016/j.jmgm.2007.07.001

    Article  CAS  PubMed  Google Scholar 

  25. 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 5(12):1332–1345. https://doi.org/10.3390/ph5121332

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Huang SY, Li M, Wang J, Pan Y (2015) HybridDock: a hybrid protein-ligand docking protocol integrating protein- and ligand-based approaches. J Chem Inf Model. https://doi.org/10.1021/acs.jcim.5b00275

    Article  PubMed  Google Scholar 

  27. 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(6):e20161. https://doi.org/10.1371/journal.pone.0020161

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. McGann M (2012) FRED and HYBRID docking performance on standardized datasets. J Comput Aided Mol Des 26(8):897–906. https://doi.org/10.1007/s10822-012-9584-8

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  30. Kelley BP, Brown SP, Warren GL, Muchmore SW (2015) POSIT: flexible shape-guided docking for pose prediction. J Chem Inf Model 55(8):1771–1780. https://doi.org/10.1021/acs.jcim.5b00142

    Article  CAS  PubMed  Google Scholar 

  31. Roy A, Srinivasan B, Skolnick J (2015) PoLi: a virtual screening pipeline based on template pocket and ligand similarity. J Chem Inf Model 55(8):1757–1770. https://doi.org/10.1021/acs.jcim.5b00232

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kumar A, Zhang KYJ (2015) Application of shape similarity in pose selection and virtual screening in CSARdock2014 exercise. J Chem Inf Model. https://doi.org/10.1021/acs.jcim.5b00279

    Article  PubMed  Google Scholar 

  33. Kumar A, Zhang KYJ (2018) A cross docking pipeline for improving pose prediction and virtual screening performance. J Comput Aided Mol Des 32(1):163–173. https://doi.org/10.1007/s10822-017-0048-z

    Article  CAS  PubMed  Google Scholar 

  34. Kumar A, Zhang KYJ (2016) A pose prediction approach based on ligand 3D shape similarity. J Comput Aided Mol Des 30(6):457–469. https://doi.org/10.1007/s10822-016-9923-2

    Article  CAS  PubMed  Google Scholar 

  35. Kumar A, Zhang KYJ (2016) Prospective evaluation of shape similarity based pose prediction method in D3R Grand Challenge 2015. J Comput Aided Mol Des 30(9):685–693. https://doi.org/10.1007/s10822-016-9931-2

    Article  CAS  PubMed  Google Scholar 

  36. 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(1):235–242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. 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(5):1268–1282. https://doi.org/10.1006/jmbi.1997.0926

    Article  CAS  PubMed  Google Scholar 

  38. Dunbrack RL Jr, Karplus M (1993) Backbone-dependent rotamer library for proteins. Application to side-chain prediction. J Mol Biol 230(2):543–574. https://doi.org/10.1006/jmbi.1993.1170

    Article  CAS  PubMed  Google Scholar 

  39. Li Z, Scheraga HA (1987) Monte Carlo-minimization approach to the multiple-minima problem in protein folding. Proc Natl Acad Sci USA 84(19):6611–6615

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Alford RF, Leaver-Fay A, Jeliazkov JR, O’Meara MJ, DiMaio FP, Park H, Shapovalov MV, Renfrew PD, Mulligan VK, Kappel K, Labonte JW, Pacella MS, Bonneau R, Bradley P, Dunbrack RL, Das R, Baker D, Kuhlman B, Kortemme T, Gray JJ (2017) The Rosetta all-atom energy function for macromolecular modeling and design. J Chem Theory Comput 13(6):3031–3048. https://doi.org/10.1021/acs.jctc.7b00125

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 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. https://doi.org/10.1021/jm0306430

    Article  CAS  PubMed  Google Scholar 

  42. 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(21):6177–6196. https://doi.org/10.1021/jm051256o

    Article  CAS  PubMed  Google Scholar 

  43. 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. https://doi.org/10.1021/jm030644s

    Article  CAS  PubMed  Google Scholar 

  44. Schrödinger Release 2015-3: LigPrep, version 3.5, Schrödinger, LLC, New York, NY, 2015

  45. Schrödinger Release 2015-3: Maestro, version 10.3, Schrödinger, LLC, New York, NY, 2015

  46. Hawkins PC, Nicholls A (2012) Conformer generation with OMEGA: learning from the data set and the analysis of failures. J Chem Inf Model 52(11):2919–2936. https://doi.org/10.1021/ci300314k

    Article  CAS  PubMed  Google Scholar 

  47. 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(4):572–584. https://doi.org/10.1021/ci100031x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. OMEGA 2.5.1.4: OpenEye Scientific Software, Santa Fe, NM. http://www.eyesopen.com/

  49. Hawkins PCD, Skillman AG, Nicholls A (2006) Comparison of shape-matching and docking as virtual screening tools. J Med Chem 50(1):74–82. https://doi.org/10.1021/jm0603365

    Article  CAS  Google Scholar 

  50. Krissinel E, Henrick K (2004) Secondary-structure matching (SSM), a new tool for fast protein structure alignment in three dimensions. Acta Crystallogr D 60(12-1):2256–2268. https://doi.org/10.1107/S0907444904026460

    Article  CAS  PubMed  Google Scholar 

  51. Winn MD, Ballard CC, Cowtan KD, Dodson EJ, Emsley P, Evans PR, Keegan RM, Krissinel EB, Leslie AGW, 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 67(4):235–242. https://doi.org/10.1107/S0907444910045749

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Tversky A (1977) Features of similarity. Psychol Rev 84(4):327–352. https://doi.org/10.1037/0033-295X.84.4.327

    Article  Google Scholar 

  53. Warren GL, Andrews CW, Capelli A-M, 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. https://doi.org/10.1021/jm050362n

    Article  CAS  PubMed  Google Scholar 

  54. Plewczynski D, Łaźniewski M, Augustyniak R, Ginalski K (2011) Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database. J Comput Chem 32(4):742–755. https://doi.org/10.1002/jcc.21643

    Article  CAS  PubMed  Google Scholar 

  55. Hawkins PCD (2017) Conformation generation: the state of the art. J Chem Inf Model 57(8):1747–1756. https://doi.org/10.1021/acs.jcim.7b00221

    Article  CAS  PubMed  Google Scholar 

  56. Watts KS, Dalal P, Murphy RB, Sherman W, Friesner RA, Shelley JC (2010) ConfGen: a conformational search method for efficient generation of bioactive conformers. J Chem Inf Model 50(4):534–546. https://doi.org/10.1021/ci100015j

    Article  CAS  PubMed  Google Scholar 

  57. RDKit: Open-source cheminformatics; http://www.rdkit.org/

  58. Vainio MJ, Johnson MS (2007) Generating conformer ensembles using a multiobjective genetic algorithm. J Chem Inf Model 47(6):2462–2474. https://doi.org/10.1021/ci6005646

    Article  CAS  PubMed  Google Scholar 

  59. Santeri PJ, VM J, S. JM (2010) Accurate conformation-dependent molecular electrostatic potentials for high-throughput in silico drug discovery. J Comput Chem 31(8):1722–1732. https://doi.org/10.1002/jcc.21460

    Article  CAS  Google Scholar 

  60. Rogers DJ, Tanimoto TT (1960) A computer program for classifying plants. Science 132(3434):1115–1118. https://doi.org/10.1126/science.132.3434.1115

    Article  CAS  PubMed  Google Scholar 

  61. O’Hagan S, Kell DB (2016) MetMaxStruct: a Tversky-similarity-based strategy for analysing the (sub)structural similarities of drugs and endogenous metabolites. Front Pharmacol. https://doi.org/10.3389/fphar.2016.00266

    Article  PubMed  PubMed Central  Google Scholar 

  62. Horvath D, Marcou G, Varnek A (2013) Do not hesitate to use Tversky—and other hints for successful active analogue searches with feature count descriptors. J Chem Inf Model 53(7):1543–1562. https://doi.org/10.1021/ci400106g

    Article  CAS  PubMed  Google Scholar 

  63. 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(5):1708–1718. https://doi.org/10.1021/ci0498719

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We acknowledge RIKEN ACCC for the supercomputing resources at the Hokusai BigWaterfall supercomputer used in this study. This research was supported by Platform Project for Supporting Drug Discovery and Life Science Research (Basis for Supporting Innovative Drug Discovery and Life Science Research (BINDS)) from AMED under Grant Number JP18am0101082. We thank members of our lab for help and discussions.

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  1. Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan

    Ashutosh Kumar & Kam Y. J. Zhang

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Kumar, A., Zhang, K.Y.J. Shape similarity guided pose prediction: lessons from D3R Grand Challenge 3. J Comput Aided Mol Des 33, 47–59 (2019). https://doi.org/10.1007/s10822-018-0142-x

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