The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Incorporation of protein flexibility and conformational energy penalties in docking screens to improve ligand discovery.
Open accessAbstract
Proteins fluctuate between alternative conformations, which presents a challenge for ligand discovery because such flexibility is difficult to treat computationally owing to problems with conformational sampling and energy weighting. Here we describe a flexible docking method that samples and weights protein conformations using experimentally derived conformations as a guide. The crystallographically refined occupancies of these conformations, which are observable in an apo receptor structure, define energy penalties for docking. In a large prospective library screen, we identified new ligands that target specific receptor conformations of a cavity in cytochrome c peroxidase, and we confirm both ligand pose and associated receptor conformation predictions by crystallography. The inclusion of receptor flexibility led to ligands with new chemotypes and physical properties. By exploiting experimental measures of loop and side-chain flexibility, this method can be extended to the discovery of new ligands for hundreds of targets in the Protein Data Bank for which similar experimental information is available.
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“Recently, Fischer et al. (2014) successfully identified new ligands targeting specific receptor conformations of cytochrome c peroxidase using a flexible docking method that samples and weights protein conformations guided by experimentally derived conformations, integrating the Boltzmann-weighted energy penalties related with the protein flexibility to the DOCK3.7 scoring function.”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Fischer et al. (2014)
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“In this context, the development of scoring functions adapted for flexible receptor docking is crucial to achieve real improvements in pose and affinity prediction (Totrov and Abagyan, 1997; Wei et al., 2002; Fischer et al., 2014; Ravindranath et al., 2015; Lam et al., 2017; Kong et al., 2018).”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Fischer et al., 2014
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Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196
ZINC: A Free Tool to Discover Chemistry for Biology
Irwin, J. J., Sterling, T., Mysinger, M. M., Bolstad, E. S. D., & Coleman, R. G. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci3001277 · Cited by 2,611
Self-citationTemperature-dependent X-ray diffraction as a probe of protein structural dynamics
Frauenfelder, H., Petsko, G. A., & Tsernoglou, D. (1979) · Nature · doi:10.1038/280558a0 · Cited by 1,151
Automated docking with grid‐based energy evaluation
Meng, E. C., Shoichet, B. K., & Kuntz, I. D. (1992) · Journal of Computational Chemistry · doi:10.1002/jcc.540130412 · Cited by 949
Self-citationA Computer Program for Classifying Plants
Rogers, D. J., & Tanimoto, T. T. (1960) · Science · doi:10.1126/science.132.3434.1115 · Cited by 874
Accessing protein conformational ensembles using room-temperature X-ray crystallography
Fraser, J. S., van den Bedem, H., Samelson, A. J., Lang, P. T., Holton, J. M., Echols, N., & Alber, T. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1111325108 · Cited by 631
Self-citationHidden alternative structures of proline isomerase essential for catalysis
Fraser, J. S., Clarkson, M. W., Degnan, S. C., Erion, R., Kern, D., & Alber, T. (2009) · Nature · doi:10.1038/nature08615 · Cited by 503
Self-citationNMR spectroscopy brings invisible protein states into focus
Baldwin, A. J., & Kay, L. E. (2009) · Nature Chemical Biology · doi:10.1038/nchembio.238 · Cited by 436
FlexE: efficient molecular docking considering protein structure variations
Claußen, H., Buning, C., Rarey, M., & Lengauer, T. (2001) · Journal of Molecular Biology · doi:10.1006/jmbi.2001.4551 · Cited by 431
Rapid Context-Dependent Ligand Desolvation in Molecular Docking
Mysinger, M. M., & Shoichet, B. K. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100214a · Cited by 325
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Vivo, M. D., Masetti, M., Bottegoni, G., & Cavalli, A. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b01684 · Cited by 1,358
DOCK 6: Impact of new features and current docking performance
Allen, W. J., Balius, T. E., Mukherjee, S., Brozell, S. R., Moustakas, D. T., Lang, P. T., Case, D. A., Kuntz, I. D., & Rizzo, R. C. (2015) · Journal of Computational Chemistry · doi:10.1002/jcc.23905 · Cited by 870
Molecular dynamics-driven drug discovery: leaping forward with confidence
Ganesan, A., Coote, M. L., & Barakat, K. (2016) · Drug Discovery Today · doi:10.1016/j.drudis.2016.11.001 · Cited by 374
Guedes, I. A., Pereira, F. S. S., & Dardenne, L. E. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01089 · Cited by 331
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Irwin, J. J., & Shoichet, B. K. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b02008 · Cited by 284
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Metadata
- Authors
- Fischer, M., Coleman, R. G., Fraser, J. S., & Shoichet, B. K.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Fraser, James S.
- Shoichet, Brian K.
- Venue
- Nature Chemistry
- Published
- 2014-05-25
- Volume
- 6
- Issue
- 7
- Pages
- 575-583
- ISSN
- 1755-4330 (ISSN-L)
- 1755-4349
- PMC
- PMC4144196
- OpenAlex
- W2089879448
- Cited by
- 149
- References
- 51
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 11 · 2023: 14 · 2022: 11 · 2021: 9 · 2020: 8 · 2019: 8 · 2018: 15 · 2017: 14 · 2016: 16 · 2015: 16 · 2014: 6
- FWCI
- 18.63
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Chemistry (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health (DP5OD009180)
- National Institute of General Medical Sciences (F32GM096544, GM59957, R01GM059957)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- protein flexibility, flexible docking, conformational sampling, ligand discovery, cytochrome c peroxidase, ligand pose prediction, loop flexibility, side-chain flexibility
- MeSH terms
- Ligands, Models, Molecular, Protein Binding, Protein Conformation, Proteins
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