Shedding light on important waters for drug design: simulations versus grid-based methods.
Abstract
Water molecules play an important role in the association of drugs with their pharmaceutical targets. For this reason, calculating the energetic contribution of water is essential to make accurate predictions of compounds' affinity and selectivity. Water molecules can also modify the binding mode of compounds by forming water bridges, or clusters, that stabilize a particular orientation of the ligand. Several computational methods have been developed for solvent mapping, but few studies have attempted to compare them in a drug design context. In this paper, four commercially available solvent mapping tools (SZMAP, WaterFLAP, 3D-RISM, and WaterMap) are evaluated on three different protein targets. The methods were compared by looking at their ability to predict the structure-activity relations of lead compounds. All methods were found to be useful to some degree and to improve the predictions from docking alone. However, the only simulation-based approach tested, WaterMap, was found in some cases to be more accurate than grid-based methods.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Many solvent mapping methods evaluated on real drug design studies showed that solvent mapping methods could be important to help ligand optimization and to correctly rank compounds to assist synthetic prioritization.
“Many solvent mapping methods were evaluated on real drug design studies in a recent paper (Bucher et al., 2018), showing that solvent mapping methods could be important to help ligand optimization and to correctly rank compounds to assist synthetic prioritization.”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Bucher et al., 2018
Cites
Sastry, G. M., Adzhigirey, M., Day, T., Annabhimoju, R., & Sherman, W. (2013) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-013-9644-8 · Cited by 6,130 · see citing works
OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins.
Harder, E. D., Damm, W., Maple, J., Wu, C., Reboul, M., Xiang, J.-Y., Wang, L.-L., Lupyan, D., Dahlgren, M. K., Knight, J. L., et al. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00864 · Cited by 2,826 · see citing works
Enhanced sampling techniques in molecular dynamics simulations of biological systems
Bernardi, R. C., Melo, M. C. R., & Schulten, K. (2015) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2014.10.019 · Cited by 757 · see citing works
Motifs for molecular recognition exploiting hydrophobic enclosure in protein–ligand binding
Young, T. K., Abel, R., Kim, B., Berne, B. J., & Friesner, R. A. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0610202104 · Cited by 697 · see citing works
Role of the Active-Site Solvent in the Thermodynamics of Factor Xa Ligand Binding
Abel, R., Young, T. K., Farid, R., Berne, B. J., & Friesner, R. A. (2008) · Journal of the American Chemical Society · doi:10.1021/ja0771033 · Cited by 682 · see citing works
The Entropic Cost of Bound Water in Crystals and Biomolecules
Dunitz, J. D. (1994) · Science · doi:10.1126/science.264.5159.670 · Cited by 577 · see citing works
Proceedings of the 2006 ACM/IEEE conference on Supercomputing - SC '06
(2006) · doi:10.1145/1188455 · Cited by 528 · see citing works
Ladbury, J. E. (1996) · Chemistry & Biology · doi:10.1016/s1074-5521(96)90164-7 · Cited by 521 · see citing works
An Integral Equation To Describe the Solvation of Polar Molecules in Liquid Water
Beglov, D. B., & Roux, B. (1997) · The Journal of Physical Chemistry B · doi:10.1021/jp971083h · Cited by 517 · see citing works
Snyder, P. W., Mecinović, J., Moustakas, D. T., Thomas, S. W., Harder, M., Mack, E. T., Lockett, M. R., Heroux, A., Sherman, W., & Whitesides, G. M. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1114107108 · Cited by 358 · see citing works
Inhomogeneous Fluid Approach to Solvation Thermodynamics. 1. Theory
Lazaridis, T. (1998) · The Journal of Physical Chemistry B · doi:10.1021/jp9723574 · Cited by 334 · see citing works
Nguyen, C., Young, T., & Gilson, M. (2012) · Journal of Chemical Physics · doi:10.1063/1.4733951 · Cited by 305 · see citing works
Michel, J., Tirado‐Rives, J., & Jorgensen, W. L. (2009) · Journal of the American Chemical Society · doi:10.1021/ja906058w · Cited by 258 · see citing works
How Can Hydrophobic Association Be Enthalpy Driven?
Setny, P., Baron, R., & McCammon, J. A. (2010) · Journal of Chemical Theory and Computation · doi:10.1021/ct1003077 · Cited by 234 · see citing works
Perspective: Alchemical free energy calculations for drug discovery.
Mobley, D., & Klimovich, P. V. (2012) · Journal of Chemical Physics · doi:10.1063/1.4769292 · Cited by 232 · see citing works
Murray, C. W., Carr, M. G., Callaghan, O., Chessari, G., Congreve, M., Cowan, S., Coyle, J. E., Downham, R., Figueroa, E., Frederickson, M., et al. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm100059d · Cited by 186 · see citing works
The Role of Water Molecules in Computational Drug Design
de Beer, S., Vermeulen, N., & Oostenbrink, C. (2010) · Current Topics in Medicinal Chemistry · doi:10.2174/156802610790232288 · Cited by 173 · see citing works
Lu, Y., Wang, R., Yang, C., & Wang, S. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci6003527 · Cited by 172 · see citing works
Lee, K. L., Ambler, C. M., Anderson, D. R., Boscoe, B. P., Bree, A. G., Brodfuehrer, J., Chang, J. S., Choi, C., Chung, S., Curran, K. J., et al. (2017) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.7b00231 · Cited by 162 · see citing works
Desroy, N., Housseman, C., Bock, X., Joncour, A., Bienvenu, N., Cherel, L., Labeguere, V., Rondet, E., Peixoto, C., Grassot, J., et al. (2017) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.7b00032 · Cited by 148 · see citing works
Self-citationA Cavity Corrected 3D-RISM Functional for Accurate Solvation Free Energies
Truchon, J., Pettitt, B. M., & Labute, P. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct4009359 · Cited by 145 · see citing works
Is it the shape of the cavity, or the shape of the water in the cavity?
Snyder, P. W., Lockett, M. R., Moustakas, D. T., & Whitesides, G. M. (2013) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2013-01818-y · Cited by 141 · see citing works
Thermodynamic Contributions of the Ordered Water Molecule in HIV-1 Protease
Li, Z., & Lazaridis, T. (2003) · Journal of the American Chemical Society · doi:10.1021/ja0299203 · Cited by 124 · see citing works
Water Sites, Networks, And Free Energies with Grand Canonical Monte Carlo
Ross, G. A., Bodnarchuk, M. S., & Essex, J. W. (2015) · Journal of the American Chemical Society · doi:10.1021/jacs.5b07940 · Cited by 122 · see citing works
Higgs, C. J., Beuming, T., & Sherman, W. (2010) · ACS Medicinal Chemistry Letters · doi:10.1021/ml100008s · Cited by 102 · see citing works
Mason, J. S., Bortolato, A., Weiss, D. R., et al. (2013) · In Silico Pharmacology · doi:10.1186/2193-9616-1-23 · Cited by 86 · see citing works
Pastor, M., Cruciani, G., & Watson, K. A. (1997) · Journal of Medicinal Chemistry · doi:10.1021/jm970273d · Cited by 85 · see citing works
Water, water, everywhere… It’s time to stop and think.
Bodnarchuk, M. S. (2016) · Drug Discovery Today · doi:10.1016/j.drudis.2016.05.009 · Cited by 81 · see citing works
Evaluating Free Energies of Binding and Conservation of Crystallographic Waters Using SZMAP
Bayden, A. S., Moustakas, D. T., Joseph‐McCarthy, D., & Lamb, M. L. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500746d · Cited by 80 · see citing works
Testing inhomogeneous solvation theory in structure-based ligand discovery
Balius, T. E., Fischer, M. M., Stein, R. M., Adler, T. B., Nguyen, C. N., Cruz, A., Gilson, M. K., Kurtzman, T., & Shoichet, B. K. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1703287114 · Cited by 80 · see citing works
García‐Sosa, A. T. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci3005786 · Cited by 79 · see citing works
Krimmer, S. G., Cramer, J., Betz, M., Fridh, V., Karlsson, R., Heine, A., & Klebe, G. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6b00998 · Cited by 79 · see citing works
Molecular fields in drug discovery: getting old or reaching maturity?
Cross, S., & Cruciani, G. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2008.12.006 · Cited by 70 · see citing works
Wade, R. C., Mazor, M. H., McCammon, J. A., et al. (1991) · Biopolymers · doi:10.1002/bip.360310802 · Cited by 69 · see citing works
Joncour, A., Desroy, N., Housseman, C., et al. (2017) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.7b00647 · Cited by 61 · see citing works
Huggins, D. J., & Payne, M. C. (2013) · The Journal of Physical Chemistry B · doi:10.1021/jp4042233 · Cited by 39 · see citing works
Reynolds, C. A., Wade, R. C., & Goodford, P. J. (1989) · Journal of Molecular Graphics · doi:10.1016/s0263-7855(89)80013-x · Cited by 37 · see citing works
Active-Site Hydration and Water Diffusion in Cytochrome P450cam: A Highly Dynamic Process
Miao, Y., & Baudry, J. Y. (2011) · Biophysical Journal · doi:10.1016/j.bpj.2011.08.020 · Cited by 23 · see citing works
A Perspective on Water Site Prediction Methods for Structure Based Drug Design
Graves, A. P., Wall, I. D., Edge, C., Woolven, J. M., Cui, G., Gall, A. L., Hong, X., Raha, K., & Manas, E. S. (2017) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026617666170427095035 · Cited by 14 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Guedes, I. A., Pereira, F. S. S., & Dardenne, L. E. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01089 · Cited by 331 · see citing works
- Authors
- Bucher, D., Stouten, P., & Triballeau, N.
- Affiliations
- Galapagos (France) (FR)
- Galapagos (Belgium) (BE)
- Corresponding author
- Bucher, Denis
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-02-28
- Volume
- 58
- Issue
- 3
- Pages
- 692-699
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2794400040
- Cited by
- 57
- References
- 39
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 5 · 2023: 7 · 2022: 3 · 2021: 11 · 2020: 5 · 2019: 10 · 2018: 8
- FWCI
- 6.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- water bridges, water clusters, structure-activity relationship, 3D-RISM, molecular dynamics simulation, drug design, binding affinity prediction
- MeSH terms
- Lysophospholipase D, Animals, Binding Sites, Humans, Ligands, Models, Molecular, Phosphoric Diester Hydrolases, Phosphotransferases, Protein Binding, Thermodynamics, Water, Drug Design, Mice, Drug Discovery
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review