Journal of Computer-Aided Molecular Design

, Volume 33, Issue 12, pp 1083–1094 | Cite as

Predicting binding poses and affinity ranking in D3R Grand Challenge using PL-PatchSurfer2.0

  • Woong-Hee Shin
  • Daisuke KiharaEmail author


Computational prediction of protein–ligand interactions is a useful approach that aids the drug discovery process. Two major tasks of computational approaches are to predict the docking pose of a compound in a known binding pocket and to rank compounds in a library according to their predicted binding affinities. There are many computational tools developed in the past decades both in academia and industry. To objectively assess the performance of existing tools, the community has held a blind assessment of computational predictions, the Drug Design Data Resource Grand Challenge. This round, Grand Challenge 4 (GC4), focused on two targets, protein beta-secretase 1 (BACE-1) and cathepsin S (CatS). We participated in GC4 in both BACE-1 and CatS challenges using our molecular surface-based virtual screening method, PL-PatchSurfer2.0. A unique feature of PL-PatchSurfer2.0 is that it uses the three-dimensional Zernike descriptor, a mathematical moment-based shape descriptor, to quantify local shape complementarity between a ligand and a receptor, which properly incorporates molecular flexibility and provides stable affinity assessment for a bound ligand–receptor complex. Since PL-PatchSurfer2.0 does not explicitly build a bound pose of a ligand, we used an external docking program, such as AutoDock Vina, to provide an ensemble of poses, which were then evaluated by PL-PatchSurfer2.0. Here, we provide an overview of our method and report the performance in GC4.


PL-PatchSurfer D3R Grand Challenge Protein–ligand interaction Virtual screening BACE-1 CatS 



We thank Charles Christoffer for proofreading the manuscript. This work was partly supported by the National Institutes of Health (R01GM123055), the National Science Foundation (DMS1614777, CMMI1825941), and the Purdue Institute of Drug Discovery.


  1. 1.
    Śledź P, Caflisch A (2018) Protein structure-based drug design: from docking to molecular dynamics. Curr Opin Str Biol 48:93–102Google Scholar
  2. 2.
    Sliwoski G, Kothiwale S, Meiler J, Lowe EW (2014) Computational methods in drug discovery. Pharmacol Rev 66:334–395PubMedPubMedCentralGoogle Scholar
  3. 3.
    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
  4. 4.
    Allen WJ, Balius TE, Mukherjee Sm Brozell SR, Moustakas DT, Lang PT, Case DA, Kuntz ID, Rizzo RC (2015) DOCK6: impact of new features and current docking performance. J Comput Chem 36:1132–1156PubMedPubMedCentralGoogle Scholar
  5. 5.
    Sauton M, Lagorce D, Villoutreix BO, Miteva MA (2008) MS-DOCK: accurate multiple conformation generator and rigid docking protocol for multi-step virtual ligand screening. BMC Bioinf 9:184Google Scholar
  6. 6.
    Craig IR, Essex JW, Speigel K (2010) Ensemble docking into multiple crystallographically derived protein structures: an evaluation based on the statistical analysis of enrichments. J Chem Inf Model 50:511–524PubMedGoogle Scholar
  7. 7.
    Shivakumar D, Williams J, Wu Y, Damm W, Shelly J, Sherman W (2010) Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field. J Chem Theory Comput 6:1509–1519PubMedGoogle Scholar
  8. 8.
    Wang L, Deng Y, Wu Y, Kim B, LeBard DN, Wandschneider D, Beachy M, Friesner RA, Abel R (2017) Accurate modeling of scaffold hopping transformations in drug discovery. J Chem Theory Comput 13:42–54Google Scholar
  9. 9.
    Dong X, Ebalunode JO, Yang SY, Zheng W (2011) Receptor-based pharmacophore and pharmacophore key descriptors for virtual screening and QSAR modeling. Curr Comput Aided Drug Des 7:181–189PubMedGoogle Scholar
  10. 10.
    Forli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ (2016) Computational protein–ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc 11:905–919PubMedPubMedCentralGoogle Scholar
  11. 11.
    Durant JL, Leland BA, Henry DR, Nourse JG (2002) Reoptimization of MDL key for use in drug discovery. J Chem Inf Model 42:1273–1280Google Scholar
  12. 12.
    Venkatraman V, Chakravarthy PR, Kihara D (2009) Application of 3D Zernike descriptors to shape-based ligand similarity searching. J Cheminform 1:19PubMedPubMedCentralGoogle Scholar
  13. 13.
    Nantasenamat C, Isarankura-Na-Ayudhya C, Prachayasittikul V (2010) Advances in computational methods to predict the biological activity of compounds. Expert Opin Drug Discov 5:633–654PubMedGoogle Scholar
  14. 14.
    Hattori M, Okuno Y, Goto S, Kanehisa M (2003) Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. J Am Chem Soc 125:11853–11865PubMedGoogle Scholar
  15. 15.
    Koes DR, Camacho CJ (2011) Pharmer: efficient and exact pharmacophore search. J Chem Inf Model 51:1307–1314PubMedPubMedCentralGoogle Scholar
  16. 16.
    Nguyen DD, Cang Z, Wu K, Wang M, Cao Y, Wei G-W (2019) Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges. J Comput-Aided Mol Des 33:71–82PubMedGoogle Scholar
  17. 17.
    Li H, Leung K-S, Wong M-H, Ballester PJ (2014) Substituting random forest for multiple linear regression improves binding affinity prediction of scoring function: cyscore as a case study. BMC Bioinf 15:1Google Scholar
  18. 18.
    Jimenez J, Skalic M, Martinez-Rosell G, De Fabritiis G (2018) KDEEP: protein–ligand absolute binding affinity prediction via 3D-convolutional neural networks. J Chem Info Model 58:287–296Google Scholar
  19. 19.
    Feinberg EN, Sur D, Wu Z, Husic BE, Mai H, Li Y, Sun S, Yang J, Ramsundar B, Pande VS (2018) PotentialNet for molecular property prediction. ACS Cent Sci 4:1520–1530PubMedPubMedCentralGoogle Scholar
  20. 20.
    Cang ZX, Wei G-W (2017) TopologyNet: topology based deep convolutional and multi-task neural networks for biomolecular property predictions. PLoS Comput Biol 13:e1005690PubMedPubMedCentralGoogle Scholar
  21. 21.
    Galeb Z, Parks CD, Chiu M, Yang H, Shao C, Walters WP, Lambert MH, Nevins N, Bembenek SD, Ameriks MK, Mirzadegan T, Burley SK, Amaro RE, Gilson MK (2019) D3R Grand Challenge 3: blind prediction of protein–ligand poses and affinity rankings. J Comput-Aided Mol Des 33:1–18Google Scholar
  22. 22.
    Shin W-H, Christoffer C, Wang J, Kihara D (2016) PL-PatchSurfer2: improved local surface matching-based virtual screening method that is tolerant to target and ligand structure variation. J Chem Inf Model 56:1676–1691PubMedPubMedCentralGoogle Scholar
  23. 23.
    Shin W, Kihara D (2018) Virtual ligand screening using PL-PatchSurfer2, a molecular surface-based protein–ligand docking method. Methods Mol Biol 1762:105–121PubMedGoogle Scholar
  24. 24.
    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 15:15122–15145PubMedPubMedCentralGoogle Scholar
  25. 25.
    Novotni M, Klein R (2003) 3D Zernike descriptors for content based shape retrieval. In: Proceedings of eighth ACM symposium on solid modeling and applications, Washington, pp 216–225Google Scholar
  26. 26.
    Han X, Sit A, Christoffer C, Chen S, Kihara D (2019) A global map of the protein shape universe. PLoS Comput Biol 15:e1006969PubMedPubMedCentralGoogle Scholar
  27. 27.
    Sael L, Li B, La D, Fang Y, Ramani K, Rustamov R, Kihara D (2008) Fast protein tertiary structure retrieval based on global surface shape similarity. Proteins 72:1259–1273PubMedGoogle Scholar
  28. 28.
    Esquivel-Rodríguez J, Xiong Y, Han X, Guang S, Christoffer C, Kihara D (2015) Navigating 3D electron microscopy maps with EM-SURFER. BMC Bioinf 16:181PubMedPubMedCentralGoogle Scholar
  29. 29.
    Venkatraman V, Yang YD, Sael L, Kihara D (2009) Protein–protein docking using region-based 3D Zernike descriptors. BMC Bioinf 10:407Google Scholar
  30. 30.
    Esquivel-Rodriguez J, Yang YD, Kihara D (2012) Multi-LZerD: multiple protein docking for asymmetric complexes. Proteins 80:1818–1833PubMedPubMedCentralGoogle Scholar
  31. 31.
    Shin W-H, Zhu X, Bures MG, Kihara D (2015) Three-dimensional compound comparison methods and their application in drug discovery. Molecules 20:12841–12862PubMedPubMedCentralGoogle Scholar
  32. 32.
    Zhu X, Xiong Y, Kihara D (2015) Large-scale binding ligand prediction by improved patch-based method Patch-Surfer2.0. Bioinformatics 31:707–713PubMedGoogle Scholar
  33. 33.
    Chikhi R, Sael L, Kihara D (2010) Real-time ligand binding pocket database search using local surface descriptors. Proteins 78:2007–2028PubMedPubMedCentralGoogle Scholar
  34. 34.
    Baker NA, Sept D, Joseph S, Holst MJ, McCammon JA (2001) Electrostatics of nano-systems: application to microtubules and the ribosome. Proc Natl Acad USA 98:10037–10041Google Scholar
  35. 35.
    Heiden W, Moeckel G, Brickmann J (1993) A new approach to analysis and display of local lipophilicity/hydrophilicity mapped on molecular surfaces. J Comput-Aided Mol Des 7:503–514PubMedGoogle Scholar
  36. 36.
    Cheng T, Zhao Y, Li X, Lin F, Xu Y, Zhang X, Li Y, Wang R (2007) Computation of octanol–water partition coefficients by guiding an additive model with knowledge. J Chem Inf Model 47:2140–2148PubMedGoogle Scholar
  37. 37.
    Prati F, Bottegoni G, Bolognesi ML, Cavalli A (2018) BACE-1 inhibitors: from recent single-target molecules to multitarget compounds for Alzheimer's disease. J Med Chem 61:619–637Google Scholar
  38. 38.
    Burley SK, Berman HM, Bhikadiya C, Bi C, Chen L, Costanzo LD, Christie C, Dalenberg K, Duarte JM, Dutta S, Feng Z, Ghosh S, Goodsell DS, Green RK, Guranović V, Guzenko D, Hudson DP, Kalro T, Liang Y, Lowe R, Namkoong H, Peisach E, Periskova I, Prlić A, Randle C, Rose A, Rose P, Sala R, Sekharan M, Shao C, Tan L, Tao Y-P, Valasatava Y, Voigt M, Westbrook J, Woo J, Yang H, Young J, Zhuravleva M, Zardeck C (2019) RCSB Protein Data Bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy. Nucleic Acids Res 47:D464–474Google Scholar
  39. 39.
    Ameriks MK, Bembenek SD, Burdett MT, Choong IC, Edwards JP, Gebauer D, Gu Y, Karlsson L, Purkey HE, Staker BL, Sun S, Thurmond RL, Zhu J (2010) Diazinones as P2 replacements for pyrazole-based cathepsin S inhibitors. Bioorg Med Chem Lett 20:4060–4064Google Scholar
  40. 40.
    O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open Babel: an open chemical toolbox. J Cheminf 3:33Google Scholar
  41. 41.
    Hawkins PCD, 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 50:572–584Google Scholar
  42. 42.
    Boratyn GM, Camacho C, Copper PS, Coulouris G, Fong A, Ma N, Madden TL, Matten WT, McGinnis SD, Merezhuk Y, Raytselis Y, Sayers EW, Tao T, Ye J, Zaretskaya I (2013) BLAST: a more efficient report with usability improvements. Nucleic Acids Res 41:W29–W33PubMedPubMedCentralGoogle Scholar
  43. 43.
    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:W522–525PubMedPubMedCentralGoogle Scholar
  44. 44.
    Zhang Y, Skolnick J (2005) TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res 33:2302–2309PubMedPubMedCentralGoogle Scholar
  45. 45.
    RDKit: Open-source cheminformatics.
  46. 46.
    Lee-Dutra A, Wiener DK, Sun S (2011) Cathepsin S inhibitors: 2004–2010. Expert Opin Ther Pat 21:311–337PubMedGoogle Scholar
  47. 47.
    Zhu X, Shin W-H, 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 56:1088–1099PubMedGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Biological SciencePurdue UniversityWest LafayetteUSA
  2. 2.Department of Chemistry EducationSunchon National UniversitySuncheonRepublic of Korea
  3. 3.Department of Computer SciencePurdue UniversityWest LafayetteUSA
  4. 4.Purdue University Center for Cancer ResearchPurdue UniversityWest LafayetteUSA
  5. 5.Department of PediatricsUniversity of CincinnatiCincinnatiUSA

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