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Role of the Active-Site Solvent in the Thermodynamics of Factor Xa Ligand Binding
Open accessAbstract
Understanding the underlying physics of the binding of small-molecule ligands to protein active sites is a key objective of computational chemistry and biology. It is widely believed that displacement of water molecules from the active site by the ligand is a principal (if not the dominant) source of binding free energy. Although continuum theories of hydration are routinely used to describe the contributions of the solvent to the binding affinity of the complex, it is still an unsettled question as to whether or not these continuum solvation theories describe the underlying molecular physics with sufficient accuracy to reliably rank the binding affinities of a set of ligands for a given protein. Here we develop a novel, computationally efficient descriptor of the contribution of the solvent to the binding free energy of a small molecule and its associated receptor that captures the effects of the ligand displacing the solvent from the protein active site with atomic detail. This descriptor quantitatively predicts (R(2) = 0.81) the binding free energy differences between congeneric ligand pairs for the test system factor Xa, elucidates physical properties of the active-site solvent that appear to be missing in most continuum theories of hydration, and identifies several features of the hydration of the factor Xa active site relevant to the structure-activity relationship of its inhibitors.
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Comparison of simple potential functions for simulating liquid water
Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., & Klein, M. L. (1983) · The Journal of Chemical Physics · doi:10.1063/1.445869 · Cited by 41,298
Molecular dynamics with coupling to an external bath
Berendsen, H. J. C., Postma, J. P. M., van Gunsteren, W. F., DiNola, A., & Haak, J. R. (1984) · The Journal of Chemical Physics · doi:10.1063/1.448118 · Cited by 30,700
Particle mesh Ewald: An N ⋅log( N ) method for Ewald sums in large systems
Darden, T., York, D., & Pedersen, L. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464397 · Cited by 30,546
A smooth particle mesh Ewald method
Essmann, U., Perera, L., Berkowitz, M. L., Darden, T., Lee, H., & Pedersen, L. G. (1995) · The Journal of Chemical Physics · doi:10.1063/1.470117 · Cited by 22,514
Polymorphic transitions in single crystals: A new molecular dynamics method
Parrinello, M., & Rahman, A. (1981) · Journal of Applied Physics · doi:10.1063/1.328693 · Cited by 19,790
Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., Repasky, M. P., Knoll, E. H., Shelley, M., Perry, J. K., et al. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm0306430 · Cited by 10,057
Self-citationGROMACS 3.0: a package for molecular simulation and trajectory analysis
Lindahl, E., Hess, B., & van der Spoel, D. (2001) · Journal of Molecular Modeling · doi:10.1007/s008940100045 · Cited by 6,977
Friesner, R. A., Murphy, R. B., Repasky, M. P., Frye, L. L., Greenwood, J. R., Halgren, T. A., Sanschagrin, P. C., & Mainz, D. T. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm051256o · Cited by 6,851
Self-citationMolecular dynamics simulations at constant pressure and/or temperature
Andersen, H. C. (1980) · The Journal of Chemical Physics · doi:10.1063/1.439486 · Cited by 5,758
Kaminski, G. A., Friesner, R. A., Tirado-Rives, J., & Jorgensen, W. L. (2001) · The Journal of Physical Chemistry B · doi:10.1021/jp003919d · Cited by 4,060
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Ferguson, F. M., & Gray, N. S. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2018.21 · Cited by 1,009
Role of computer-aided drug design in modern drug discovery
Macalino, S. J. Y., Gosu, V., Hong, S., & Choi, S. (2015) · Archives of Pharmacal Research · doi:10.1007/s12272-015-0640-5 · Cited by 762
The Art and Science of Molecular Docking
Paggi, J. M., Pandit, A., & Dror, R. O. (2024) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-030222-120000 · Cited by 459
Harriman, G., Greenwood, J., Bhat, S., Huang, X., Wang, R., Paul, D., Tong, L., Saha, A. K., Westlin, W. F., Kapeller, R., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1520686113 · Cited by 287
Huggins, D. J., Biggin, P. C., Dämgen, M. A., Essex, J. W., Harris, S. A., Henchman, R. H., Khalid, S., Kuzmanic, A., Laughton, C. A., Michel, J., et al. (2019) · WIREs Computational Molecular Science · doi:10.1002/wcms.1393 · Cited by 169
Czodrowski, P., Mallinger, A., Wienke, D., Esdar, C., Pöschke, O., Busch, M., Rohdich, F., Eccles, S. A., Ortiz-Ruiz, M.-J., Schneider, R., et al. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6b00597 · Cited by 123
AI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges
Schauperl, M., & Denny, R. A. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00026 · Cited by 122
WScore: a flexible and accurate treatment of explicit water molecules in ligand-receptor docking.
Murphy, R. B., Repasky, M. P., Greenwood, J. R., Tubert-Brohman, I., Jerome, S., Annabhimoju, R., Boyles, N. A., Schmitz, C. D., Abel, R., Farid, R., et al. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6b00131 · Cited by 89
Self-citationWater, 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
Yang, Y., Lightstone, F. C., & Wong, S. E. (2013) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2013.749853 · Cited by 65
Improving the Accuracy of Protein Thermostability Predictions for Single Point Mutations
Duan, J., Lupyan, D., & Wang, L. (2020) · Biophysical Journal · doi:10.1016/j.bpj.2020.05.020 · Cited by 63
Shedding light on important waters for drug design: simulations versus grid-based methods.
Bucher, D., Stouten, P., & Triballeau, N. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00642 · Cited by 57
Free enthalpies of replacing water molecules in protein binding pockets.
Riniker, S., Barandun, L. J., Diederich, F., Krämer, O., Steffen, A., & van Gunsteren, W. F. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-012-9620-8 · Cited by 23
Jogalekar, A. S., Reiling, S., & Vaz, R. J. (2010) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2010.09.019 · Cited by 23
Zhang, Y., Larraufie, M.-H., Musavi, L., Akkiraju, H., Brown, L. M., & Stockwell, B. R. (2018) · Biochemistry · doi:10.1021/acs.biochem.7b01113 · Cited by 16
Gao, Y.-D., Hu, Y., Crespo, A., Wang, D., Armacost, K. A., Fells, J. I., Fradera, X., Wang, H., Wang, H., Sherborne, B., et al. (2018) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-017-0072-z · Cited by 9
Ferraz, M. V. F., Neto, J. C. S., Lins, R. D., & Teixeira, E. S. (2023) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp05644e · Cited by 9
Metadata
- Authors
- Abel, R., Young, T., Farid, R., Berne, B. J., & Friesner, R. A.
- Venue
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Published
- 2008-02-12
- Volume
- 130
- Issue
- 9
- Pages
- 2817-2831
- ISSN
- 0002-7863 (ISSN-L)
- 1520-5126
- OpenAlex
- W1971652848
- Cited by
- 668
- References
- 37
- FWCI
- 37.88
- Type
- journal article
- Language
- en
- Open access
- green
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