Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

D3R Grand Challenge 2: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

The Drug Design Data Resource (D3R) ran Grand Challenge 2 (GC2) from September 2016 through February 2017. This challenge was based on a dataset of structures and affinities for the nuclear receptor farnesoid X receptor (FXR), contributed by F. Hoffmann-La Roche. The dataset contained 102 IC50 values, spanning six orders of magnitude, and 36 high-resolution co-crystal structures with representatives of four major ligand classes. Strong global participation was evident, with 49 participants submitting 262 prediction submission packages in total. Procedurally, GC2 mimicked Grand Challenge 2015 (GC2015), with a Stage 1 subchallenge testing ligand pose prediction methods and ranking and scoring methods, and a Stage 2 subchallenge testing only ligand ranking and scoring methods after the release of all blinded co-crystal structures. Two smaller curated sets of 18 and 15 ligands were developed to test alchemical free energy methods. This overview summarizes all aspects of GC2, including the dataset details, challenge procedures, and participant results. We also consider implications for progress in the field, while highlighting methodological areas that merit continued development. Similar to GC2015, the outcome of GC2 underscores the pressing need for methods development in pose prediction, particularly for ligand scaffolds not currently represented in the Protein Data Bank (http://www.pdb.org), and in affinity ranking and scoring of bound ligands.

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

Access this article

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Sliwoski G, Kothiwale S, Meiler J, Lowe EW (2014) Computational methods in drug discovery. Pharmacol Rev 66:334–395. https://doi.org/10.1124/pr.112.007336

    Article  Google Scholar 

  2. 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 22:693–705. https://doi.org/10.1007/s10822-007-9159-2

    Article  CAS  Google Scholar 

  3. Jorgensen WL (2004) The many roles of computation in drug discovery. Science 303:1813–1818. https://doi.org/10.1126/science.1096361

    Article  CAS  Google Scholar 

  4. Carlson HA (2016) Lessons learned over four benchmark exercises from the community structure–activity resource. J Chem Inf Model 56:951–954. https://doi.org/10.1021/acs.jcim.6b00182

    Article  Google Scholar 

  5. Carlson HA, Smith RD, Damm-Ganamet KL et al (2016) CSAR 2014: a benchmark exercise using unpublished data from pharma. J Chem Inf Model 56:1063–1077. https://doi.org/10.1021/acs.jcim.5b00523

    Article  CAS  Google Scholar 

  6. Smith RD, Damm-Ganamet KL, Dunbar JB et al (2016) CSAR benchmark exercise 2013: evaluation of results from a combined computational protein design, docking, and scoring/ranking challenge. J Chem Inf Model 56:1022–1031. https://doi.org/10.1021/acs.jcim.5b00387

    Article  CAS  Google Scholar 

  7. Damm-Ganamet KL, Smith RD, Dunbar JB et al (2013) CSAR benchmark exercise 2011–2012: evaluation of results from docking and relative ranking of blinded congeneric series. J Chem Inf Model 53:1853–1870. https://doi.org/10.1021/ci400025f

    Article  CAS  Google Scholar 

  8. Smith RD, Dunbar JB, Ung PM-U et al (2011) CSAR Benchmark Exercise of 2010: Combined Evaluation Across All Submitted Scoring Functions. J Chem Inf Model 51:2115–2131. https://doi.org/10.1021/ci200269q

    Article  CAS  Google Scholar 

  9. Gathiaka S, Liu S, Chiu M et al (2016) D3R grand challenge 2015: evaluation of protein–ligand pose and affinity predictions. J Comput Aided Mol Des 30:651–668. https://doi.org/10.1007/s10822-016-9946-8

    Article  CAS  Google Scholar 

  10. Makishima M, Okamoto AY, Repa JJ et al (1999) Identification of a nuclear receptor for bile acids. Science 284:1362–1365. https://doi.org/10.1126/science.284.5418.1362

    Article  CAS  Google Scholar 

  11. Parks DJ, Blanchard SG, Bledsoe RK et al (1999) Bile acids: natural ligands for an orphan nuclear receptor. Science 284:1365–1368.. https://doi.org/10.1126/science.284.5418.1365

    Article  CAS  Google Scholar 

  12. Wang H, Chen J, Hollister K et al (1999) Endogenous bile acids are ligands for the nuclear receptor FXR/BAR. Mol Cell 3:543–553. https://doi.org/10.1016/S1097-2765(00)80348-2

    Article  CAS  Google Scholar 

  13. Lu TT, Makishima M, Repa JJ et al (2000) Molecular basis for feedback regulation of bile acid synthesis by nuclear receptors. Mol Cell 6:507–515. https://doi.org/10.1016/S1097-2765(00)00050-2

    Article  CAS  Google Scholar 

  14. Gardès C, Blum D, Bleicher K et al (2011) Studies in mice, hamsters, and rats demonstrate that repression of hepatic apoA-I expression by taurocholic acid in mice is not mediated by the farnesoid-X-receptor. J Lipid Res 52:1188–1199. https://doi.org/10.1194/jlr.M012542

    Article  Google Scholar 

  15. Richter HGF, Benson GM, Bleicher KH et al (2011) Optimization of a novel class of benzimidazole-based farnesoid X receptor (FXR) agonists to improve physicochemical and ADME properties. Bioorg Med Chem Lett 21:1134–1140. https://doi.org/10.1016/j.bmcl.2010.12.123

    Article  CAS  Google Scholar 

  16. Richter HGF, Benson GM, Blum D et al (2011) Discovery of novel and orally active FXR agonists for the potential treatment of dyslipidemia & diabetes. Bioorg Med Chem Lett 21:191–194. https://doi.org/10.1016/j.bmcl.2010.11.039

    Article  CAS  Google Scholar 

  17. Feng S, Yang M, Zhang Z et al (2009) Identification of an N-oxide pyridine GW4064 analog as a potent FXR agonist. Bioorg Med Chem Lett 19:2595–2598. https://doi.org/10.1016/j.bmcl.2009.03.008

    Article  CAS  Google Scholar 

  18. Tembe BL, Mc Cammon JA (1984) Ligand-receptor interactions. Comput Chem 8:281–283. https://doi.org/10.1016/0097-8485(84)85020-2

    Article  Google Scholar 

  19. Nichols JS, Parks DJ, Consler TG, Blanchard SG (1998) Development of a scintillation proximity assay for peroxisome proliferator-activated receptor ligand binding domain. Anal Biochem 257:112–119. https://doi.org/10.1006/abio.1997.2557

    Article  CAS  Google Scholar 

  20. Warkentin M, Thorne RE (2007) A general method for hyperquenching protein crystals. J Struct Funct Genom 8:141–144. https://doi.org/10.1007/s10969-007-9029-0

    Article  CAS  Google Scholar 

  21. Otwinowski Z, Minor W (1997) Processing of X-ray diffraction data collected in oscillation mode. Methods Enzymol 276:307–326. https://doi.org/10.1016/S0076-6879(97)76066-X

    Article  CAS  Google Scholar 

  22. Kabsch W (2010) XDS. Acta Crystallogr D 66:125–132. https://doi.org/10.1107/S0907444909047337

    Article  CAS  Google Scholar 

  23. McCoy AJ, Grosse-Kunstleve RW, Adams PD et al (2007) Phaser crystallographic software. J Appl Crystallogr 40:658–674. https://doi.org/10.1107/S0021889807021206

    Article  CAS  Google Scholar 

  24. Winn MD, Murshudov GN, Papiz MZ (2003) Macromolecular TLS refinement in REFMAC at moderate resolutions. Methods Enzymol 374:300–321. https://doi.org/10.1016/S0076-6879(03)74014-2

    Article  CAS  Google Scholar 

  25. Blanc E, Roversi P, Vonrhein C et al (2004) Refinement of severely incomplete structures with maximum likelihood in BUSTER–TNT. Acta Crystallogr D 60:2210–2221. https://doi.org/10.1107/S0907444904016427

    Article  CAS  Google Scholar 

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

    CAS  Google Scholar 

  27. Small-molecule Drug Discovery Suite 2017-4 (2017) Schrödinger, LLC, New York, NY

  28. Jacobson MP, Pincus DL, Rapp CS et al (2004) A hierarchical approach to all-atom protein loop prediction. Proteins Struct Funct Bioinform 55:351–367. https://doi.org/10.1002/prot.10613

    Article  CAS  Google Scholar 

  29. Jacobson MP, Friesner RA, Xiang Z, Honig B (2002) On the role of the crystal environment in determining protein side-chain conformations. J Mol Biol 320:597–608. https://doi.org/10.1016/S0022-2836(02)00470-9

    Article  CAS  Google Scholar 

  30. Yung-Chi C, Prusoff WH (1973) Relationship between the inhibition constant (KI) and the concentration of inhibitor which causes 50 per cent inhibition (I50) of an enzymatic reaction. Biochem Pharmacol 22:3099–3108. https://doi.org/10.1016/0006-2952(73)90196-2

    Article  Google Scholar 

  31. Ekins S, Bunin B (2013) The collaborative drug discovery (CDD) database. In: Kortagere S (ed) In silico models for drug discovery. Humana Press, New York, pp 139–154

    Chapter  Google Scholar 

  32. Viswanadhan VN, Ghose AK, Revankar GR, Robins RK (1989) Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occurring nucleoside antibiotics. J Chem Inf Comput Sci 29:163–172. https://doi.org/10.1021/ci00063a006

    Article  CAS  Google Scholar 

  33. Duan J, Dixon SL, Lowrie JF, Sherman W (2010) Analysis and comparison of 2D fingerprints: insights into database screening performance using eight fingerprint methods. J Mol Graph Model 29:157–170. https://doi.org/10.1016/j.jmgm.2010.05.008

    Article  CAS  Google Scholar 

  34. Sastry M, Lowrie JF, Dixon SL, Sherman W (2010) Large-scale systematic analysis of 2D fingerprint methods and parameters to improve virtual screening enrichments. J Chem Inf Model 50:771–784. https://doi.org/10.1021/ci100062n

    Article  CAS  Google Scholar 

  35. Kuhn B, Tichý M, Wang L et al (2017) Prospective evaluation of free energy calculations for the prioritization of cathepsin L inhibitors. J Med Chem 60:2485–2497. https://doi.org/10.1021/acs.jmedchem.6b01881

    Article  CAS  Google Scholar 

  36. Keränen H, Pérez-Benito L, Ciordia M et al (2017) Acylguanidine beta secretase 1 inhibitors: a combined experimental and free energy perturbation study. J Chem Theory Comput 13:1439–1453. https://doi.org/10.1021/acs.jctc.6b01141

    Article  Google Scholar 

  37. Lenselink EB, Louvel J, Forti AF et al (2016) Predicting binding affinities for GPCR ligands using free-energy perturbation. ACS Omega 1:293–304. https://doi.org/10.1021/acsomega.6b00086

    Article  CAS  Google Scholar 

  38. Abel R, Mondal S, Masse C et al (2017) Accelerating drug discovery through tight integration of expert molecular design and predictive scoring. Curr Opin Struct Biol 43:38–44. https://doi.org/10.1016/j.sbi.2016.10.007

    Article  CAS  Google Scholar 

  39. Chipot C (2014) Frontiers in free-energy calculations of biological systems. Wiley Interdiscip Rev 4:71–89. https://doi.org/10.1002/wcms.1157

    CAS  Google Scholar 

  40. Aldeghi M, Heifetz A, Bodkin MJ et al (2015) Accurate calculation of the absolute free energy of binding for drug molecules. Chem Sci 7:207–218. https://doi.org/10.1039/C5SC02678D

    Article  Google Scholar 

  41. Aldeghi M, Heifetz A, Bodkin MJ et al (2017) Predictions of ligand selectivity from absolute binding free energy calculations. J Am Chem Soc 139:946–957. https://doi.org/10.1021/jacs.6b11467

    Article  CAS  Google Scholar 

  42. Mishra SK, Calabró G, Loeffler HH et al (2015) Evaluation of selected classical force fields for alchemical binding free energy calculations of protein-carbohydrate complexes. J Chem Theory Comput 11:3333–3345. https://doi.org/10.1021/acs.jctc.5b00159

    Article  CAS  Google Scholar 

  43. 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 31:455–461. https://doi.org/10.1002/jcc.21334

    CAS  Google Scholar 

  44. Bannan CC, Burley KH, Chiu M et al (2016) Blind prediction of cyclohexane–water distribution coefficients from the SAMPL5 challenge. J Comput Aided Mol Des 30:927–944. https://doi.org/10.1007/s10822-016-9954-8

    Article  CAS  Google Scholar 

  45. Yin J, Henriksen NM, Slochower DR et al (2017) Overview of the SAMPL5 host–guest challenge: are we doing better?. J Comput Aided Mol Des 31:1–19. https://doi.org/10.1007/s10822-016-9974-4

    Article  CAS  Google Scholar 

  46. Muddana HS, Fenley AT, Mobley DL, Gilson MK (2014) The SAMPL4 host–guest blind prediction challenge: an overview. J Comput Aided Mol Des 28:305–317. https://doi.org/10.1007/s10822-014-9735-1

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by National Institutes of Health (NIH) grant 1U01GM111528 for the Drug Design Data Resource (D3R). We also thank OpenEye Scientific Software for generously donating the use of their software. RCSB Protein Data Bank is supported by NSF, NIH, and DOE (NSF DBI-1338415). The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. MKG has an equity interest in, and is a co-founder and scientific advisor of, VeraChem LLC. REA has equity interest in and is a co-founder and scientific advisor of Actavalon, Inc.; VAF has equity interest in Actavalon, Inc. and PW has an equity interest in Relay Pharmaceuticals, Inc. In August 2016, VAF joined Schrodinger, Inc., and after that point was no longer involved in D3R activities. In June 2017, SL joined Silicon Therapeutics, Inc., and after that point was no longer involved in D3R activities.

Author information

Authors and Affiliations

  1. Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA

    Zied Gaieb, Shuai Liu, Michael Chiu, Victoria A. Feher, Michael K. Gilson & Rommie E. Amaro

  2. Merck & Co., Inc., Boston, MA, 02115, USA

    Symon Gathiaka

  3. RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA

    Huanwang Yang, Chenghua Shao & Stephen K. Burley

  4. Relay Therapeutics, Cambridge, MA, 20142, USA

    W. Patrick Walters

  5. Roche Pharmaceutical Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland

    Bernd Kuhn & Markus G. Rudolph

Authors
  1. Zied Gaieb
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuai Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Symon Gathiaka
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michael Chiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Huanwang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chenghua Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Victoria A. Feher
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. W. Patrick Walters
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Bernd Kuhn
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Markus G. Rudolph
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Stephen K. Burley
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Michael K. Gilson
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Rommie E. Amaro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael K. Gilson.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gaieb, Z., Liu, S., Gathiaka, S. et al. D3R Grand Challenge 2: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies. J Comput Aided Mol Des 32, 1–20 (2018). https://doi.org/10.1007/s10822-017-0088-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-017-0088-4

Keywords

Search

Navigation