The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Docking Covalent Inhibitors: A Parameter Free Approach To Pose Prediction and Scoring
Abstract
Although many popular docking programs include a facility to account for covalent ligands, large-scale systematic docking validation studies of covalent inhibitors have been sparse. In this paper, we present the development and validation of a novel approach for docking and scoring covalent inhibitors, which consists of conventional noncovalent docking, heuristic formation of the covalent attachment point, and structural refinement of the protein-ligand complex. This approach combines the strengths of the docking program Glide and the protein structure modeling program Prime and does not require any parameter fitting for the study of additional covalent reaction types. We first test this method by predicting the native binding geometry of 38 covalently bound complexes. The average RMSD of the predicted poses is 1.52 Å, and 76% of test set inhibitors have an RMSD of less than 2.0 Å. In addition, the apparent affinity score constructed herein is tested on a virtual screening study and the characterization of the SAR properties of two different series of congeneric compounds with satisfactory success.
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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
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-citationDevelopment and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen
Jones, G., Willett, P., Glen, R. C., Leach, A. R., & Taylor, R. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0897 · Cited by 6,763
Madhavi Sastry, G., Adzhigirey, M., Day, T., Annabhimoju, R., & Sherman, W. (2013) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-013-9644-8 · Cited by 6,006
Self-citationHalgren, T. A., Murphy, R. B., Friesner, R. A., Beard, H. S., Frye, L. L., Pollard, W. T., & Banks, J. L. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm030644s · Cited by 5,177
A hierarchical approach to all‐atom protein loop prediction
Jacobson, M. P., Pincus, D. L., Rapp, C. S., Day, T. J. F., Honig, B., Shaw, D. E., & Friesner, R. A. (2004) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.10613 · Cited by 2,504
Self-citationShelley, J. C., Cholleti, A., Frye, L. L., Greenwood, J. R., Timlin, M. R., & Uchimaya, M. (2007) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-007-9133-z · Cited by 2,045
Self-citationThe resurgence of covalent drugs
Singh, J., Petter, R. C., Baillie, T. A., & Whitty, A. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3410 · Cited by 1,873
Honigberg, L. A., Smith, A. M., Sirisawad, M., Verner, E., Loury, D., Chang, B., Li, S., Pan, Z., Thamm, D. H., Miller, R. A., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1004594107 · Cited by 1,561
Automated docking of substrates to proteins by simulated annealing
Goodsell, D. S., & Olson, A. J. (1990) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.340080302 · Cited by 1,285
Cited by
Ward, C. C., Kleinman, J. I., Brittain, S. M., Lee, P. S., Chung, C. Y. S., Kim, K., Petri, Y., Thomas, J. R., Tallarico, J. A., McKenna, J. M., et al. (2019) · ACS Chemical Biology · doi:10.1021/acschembio.8b01083 · Cited by 336
Guedes, I. A., Pereira, F. S. S., & Dardenne, L. E. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01089 · Cited by 331
Rapid Covalent-Probe Discovery by Electrophile-Fragment Screening
Resnick, E., Bradley, A., Gan, J., Douangamath, A., Krojer, T., Sethi, R., Geurink, P. P., Aimon, A., Amitai, G., Bellini, D., et al. (2019) · Journal of the American Chemical Society · doi:10.1021/jacs.9b02822 · Cited by 335
Covalent inhibitors design and discovery.
De Cesco, S., Kurian, J., Dufresne, C., Mittermaier, A. K., & Moitessier, N. (2017) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2017.06.019 · Cited by 315
Covalent docking of large libraries for the discovery of chemical probes
London, N., Miller, R. M., Krishnan, S., Uchida, K., Irwin, J. J., Eidam, O., Gibold, L., Cimermančič, P., Bonnet, R., Shoichet, B. K., et al. (2014) · Nature Chemical Biology · doi:10.1038/nchembio.1666 · Cited by 274
Covalent docking using autodock: two-point attractor and flexible side chain methods.
Bianco, G., Forli, S., Goodsell, D. S., & Olson, A. J. (2016) · Protein Science · doi:10.1002/pro.2733 · Cited by 246
Human GLP-1 receptor transmembrane domain structure in complex with allosteric modulators
Song, G., Yang, D., Wang, Y., de Graaf, C., Zhou, Q., Jiang, S., Liu, K., Cai, X., Dai, A., Lin, G., et al. (2017) · Nature · doi:10.1038/nature22378 · Cited by 241
Theory and Applications of Covalent Docking in Drug Discovery: Merits and Pitfalls
Kumalo, H., Bhakat, S., & Soliman, M. (2015) · Molecules · doi:10.3390/molecules20021984 · Cited by 158
CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed
Hwang, W., Austin, S. L., Blondel, A., Boittier, E. D., Boresch, S., Buck, M., Buckner, J., Caflisch, A., Chang, H.-T., Cheng, X., et al. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c04100 · Cited by 136
Self-citationFragment-based covalent ligand discovery
Lu, W., Kostic, M., Zhang, T., Che, J., Patricelli, M. P., Jones, L. H., Chouchani, E. T., & Gray, N. S. (2021) · RSC Chemical Biology · doi:10.1039/d0cb00222d · Cited by 136
Tricyclic Covalent Inhibitors Selectively Target Jak3 through an Active Site Thiol
Goedken, E. R., Argiriadi, M. A., Banach, D. L., Fiamengo, B. A., Foley, S. E., Frank, K. E., George, J. S., Harris, C. M., Hobson, A. D., Ihle, D. C., et al. (2015) · Journal of Biological Chemistry · doi:10.1074/jbc.m114.595181 · Cited by 101
Efficient targeted oncogenic KRASG12C degradation via first reversible-covalent PROTAC
Yang, F., Wen, Y., Wang, C., Zhou, Y., Zhou, Y., Zhang, Z.-M., Liu, T., & Lu, X. (2022) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2021.114088 · Cited by 109
Modeling covalent-modifier drugs.
Awoonor-Williams, E., Walsh, A. G., & Rowley, C. N. (2017) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2017.05.009 · Cited by 90
Development of covalent inhibitors: Principle, design, and application in cancer
Zheng, L., Li, Y., Wu, D., Xiao, H., Zheng, S., Wang, G., & Sun, Q. (2023) · MedComm – Oncology · doi:10.1002/mog2.56 · Cited by 71
Du, G., Rao, S., Gurbani, D., Henning, N. J., Jiang, J., Che, J., Yang, A., Ficarro, S. B., Marto, J. A., Aguirre, A. J., et al. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b01502 · Cited by 39
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
Integrated Covalent Drug Design Workflow Using Site Identification by Ligand Competitive Saturation
Yu, W., Weber, D. J., & MacKerell, A. D. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00232 · Cited by 16
Development of an Affinity-Based Probe to Profile Endogenous Human Adenosine A 3 Receptor Expression
Beerkens, B. L. H., Snijders, I. M., Snoeck, J., Liu, R., Tool, A. T. J., Le Dévédec, S. E., Jespers, W., Kuijpers, T. W., van Westen, G. J. P., Heitman, L. H., et al. (2023) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.3c00854 · Cited by 6
Metadata
- Authors
- Zhu, K., Borrelli, K. W., Greenwood, J. R., Day, T., Abel, R., Farid, R. S., & Harder, E.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-06-26
- Volume
- 54
- Issue
- 7
- Pages
- 1932-1940
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W1995770177
- Cited by
- 462
- References
- 21
- FWCI
- 19.76
- Type
- journal article
- Language
- en
- Open access
- closed
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