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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
On the Role of the Crystal Environment in Determining Protein Side-chain Conformations
Jacobson, M. P., Friesner, R. A., Xiang, Z., & Honig, B. (2002) · Journal of Molecular Biology · doi:10.1016/s0022-2836(02)00470-9 · Cited by 1,267
The VSGB 2.0 model: A next generation energy model for high resolution protein structure modeling
Li, J., Abel, R., Zhu, K., Cao, Y., Zhao, S., & Friesner, R. A. (2011) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.23106 · Cited by 1,159
Self-citationGreenwood, J. R., Calkins, D., Sullivan, A. P., & Shelley, J. C. (2010) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-010-9349-1 · Cited by 947
Self-citationConfGen: A Conformational Search Method for Efficient Generation of Bioactive Conformers
Watts, K. S., Dalal, P., Murphy, R. B., Sherman, W., Friesner, R. A., & Shelley, J. C. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100015j · Cited by 440
Njoroge, F. G., Chen, K. X., Shih, N.-Y., & Piwinski, J. J. (2008) · Accounts of Chemical Research · doi:10.1021/ar700109k · Cited by 266
Lin, C., Kwong, A., & Perni, R. (2006) · Infectious Disorders - Drug Targets · doi:10.2174/187152606776056706 · Cited by 243
Structure-guided development of affinity probes for tyrosine kinases using chemical genetics
Blair, J. A., Rauh, D., Kung, C. K.-H., Yun, C., Fan, Q.-W., Rode, H. B., Zhang, C., Eck, M. J., Weiss, W. A., & Shokat, K. M. (2007) · Nature Chemical Biology · doi:10.1038/nchembio866 · Cited by 204
Ouyang, X., Zhou, S., Su, C. T. T., Ge, Z., Li, R., & Kwoh, C. K. (2013) · Journal of Computational Chemistry · doi:10.1002/jcc.23136 · Cited by 134
Long loop prediction using the protein local optimization program
Zhu, K., Pincus, D. L., Zhao, S., & Friesner, R. A. (2006) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21040 · Cited by 117
Self-citationZhu, K., Shirts, M. R., & Friesner, R. A. (2007) · Journal of Chemical Theory and Computation · doi:10.1021/ct700166f · Cited by 103
Self-citationProgress in super long loop prediction
Zhao, S., Zhu, K., Li, J., & Friesner, R. A. (2011) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.23129 · Cited by 43
Self-citation
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
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
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
