The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
ROSETTALIGAND: Protein–small molecule docking with full side‐chain flexibility
Abstract
Protein–small molecule docking algorithms provide a means to model the structure of protein–small molecule complexes in structural detail and play an important role in drug development. In recent years the necessity of simulating protein side‐chain flexibility for an accurate prediction of the protein–small molecule interfaces has become apparent, and an increasing number of docking algorithms probe different approaches to include protein flexibility. Here we describe a new method for docking small molecules into protein binding sites employing a Monte Carlo minimization procedure in which the rigid body position and orientation of the small molecule and the protein side‐chain conformations are optimized simultaneously. The energy function comprises van der Waals (VDW) interactions, an implicit solvation model, an explicit orientation hydrogen bonding potential, and an electrostatics model. In an evaluation of the scoring function the computed energy correlated with experimental small molecule binding energy with a correlation coefficient of 0.63 across a diverse set of 229 protein– small molecule complexes. The docking method produced lowest energy models with a root mean square deviation (RMSD) smaller than 2 Å in 71 out of 100 protein–small molecule crystal structure complexes (self‐docking). In cross‐docking calculations in which both protein side‐chain and small molecule internal degrees of freedom were varied the lowest energy predictions had RMSDs less than 2 Å in 14 of 20 test cases. Proteins 2006. © 2006 Wiley‐Liss, Inc.
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Cites
Development 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
Principles of docking: An overview of search algorithms and a guide to scoring functions
Halperin, I., Ma, B., Wolfson, H., & Nussinov, R. (2002) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.10115 · Cited by 1,294
McGovern, S. L., & Shoichet, B. K. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm0300330 · Cited by 276
Cited by
Leaver-Fay (2011) · doi:10.1016/b978-0-12-381270-4.00019-6 · Cited by 1,904
The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design
Alford, R. F., Leaver-Fay, A., Jeliazkov, J. R., O’Meara, M. J., DiMaio, F. P., Park, H., Shapovalov, M. V., Renfrew, P. D., Mulligan, V. K., Kappel, K., et al. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00125 · Cited by 1,532
Self-citationThe second wave of synthetic biology: from modules to systems
Purnick, P. E. M., & Weiss, R. (2009) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2698 · Cited by 1,181
Macromolecular modeling and design in Rosetta: recent methods and frameworks
Leman, J. K., Weitzner, B. D., Lewis, S. M., Adolf-Bryfogle, J., Alam, N., Alford, R. F., Aprahamian, M., Baker, D., Barlow, K. A., Barth, P., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0848-2 · Cited by 888
Self-citationMoitessier, N., Englebienne, P., Lee, D., Lawandi, J., & Corbeil, C. R. (2008) · British Journal of Pharmacology · doi:10.1038/sj.bjp.0707515 · Cited by 514
Metadata
- Authors
- Meiler, J., & Baker, D.
- Venue
- Proteins: Structure, Function, and Bioinformatics
- Publisher
- Wiley
- Published
- 2006-09-13
- Volume
- 65
- Issue
- 3
- Pages
- 538-548
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- OpenAlex
- W2103994532
- Cited by
- 505
- References
- 3
- FWCI
- 16.40
- Type
- journal article
- Language
- en
- Open access
- closed
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