The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Novel Procedure for Modeling Ligand/Receptor Induced Fit Effects
Abstract
We present a novel protein-ligand docking method that accurately accounts for both ligand and receptor flexibility by iteratively combining rigid receptor docking (Glide) with protein structure prediction (Prime) techniques. While traditional rigid-receptor docking methods are useful when the receptor structure does not change substantially upon ligand binding, success is limited when the protein must be "induced" into the correct binding conformation for a given ligand. We provide an in-depth description of our novel methodology and present results for 21 pharmaceutically relevant examples. Traditional rigid-receptor docking for these 21 cases yields an average RMSD of 5.5 A. The average ligand RMSD for docking to a flexible receptor for the 21 pairs is 1.4 A; the RMSD is < or =1.8 A for 18 of the cases. For the three cases with RMSDs greater than 1.8 A, the core of the ligand is properly docked and all key protein/ligand interactions are captured.
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
Self-citationImplications of protein flexibility for drug discovery
Teague, S. J. (2003) · Nature Reviews Drug Discovery · doi:10.1038/nrd1129 · Cited by 755
Comparative evaluation of eight docking tools for docking and virtual screening accuracy
Kellenberger, E., Rodrigo, J., Muller, P., & Rognan, D. (2004) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20149 · Cited by 577
Cited by
Molecular Docking: A Powerful Approach for Structure-Based Drug Discovery
Meng, X.-Y., Zhang, H.-X., Mezei, M., & Cui, M. (2011) · Current Computer Aided-Drug Design · doi:10.2174/157340911795677602 · Cited by 3,771
Improved Docking of Polypeptides with Glide
Tubert-Brohman, I., Sherman, W., Repasky, M., & Beuming, T. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400128m · Cited by 1,308
Self-citationCharacterization and engineering of a plastic-degrading aromatic polyesterase
Austin, H. P., Allen, M. D., Donohoe, B. S., Rorrer, N. A., Kearns, F. L., Silveira, R. L., Pollard, B. C., Dominick, G., Duman, R., El Omari, K., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1718804115 · Cited by 1,079
Class I histone deacetylases (HDAC1–3) are histone lysine delactylases
Moreno-Yruela, C., Zhang, D., Wei, W., Bæk, M., Liu, W., Gao, J., Danková, D., Nielsen, A. L., Bolding, J. E., Yang, L., et al. (2022) · Science Advances · doi:10.1126/sciadv.abi6696 · Cited by 841
Use of an Induced Fit Receptor Structure in Virtual Screening
Sherman, W., Beard, H. S., & Farid, R. (2006) · Chemical Biology & Drug Design · doi:10.1111/j.1747-0285.2005.00327.x · Cited by 697
Self-citationVirtual Screening Strategies in Drug Discovery: A Critical Review
Lavecchia, A., & Giovanni, C. (2013) · Current Medicinal Chemistry · doi:10.2174/09298673113209990001 · Cited by 686
Crystal Structure of a Lipid G Protein–Coupled Receptor
Hanson, M. A., Roth, C. B., Jo, E., Griffith, M. T., Scott, F. L., Reinhart, G., Desale, H., Clemons, B., Cahalan, S. M., Schuerer, S. C., et al. (2012) · Science · doi:10.1126/science.1215904 · Cited by 666
Advances and Challenges in Protein-Ligand Docking
Huang, S.-Y., & Zou, X. (2010) · International Journal of Molecular Sciences · doi:10.3390/ijms11083016 · Cited by 639
Moitessier, N., Englebienne, P., Lee, D., Lawandi, J., & Corbeil, C. R. (2008) · British Journal of Pharmacology · doi:10.1038/sj.bjp.0707515 · Cited by 514
Flexible ligand docking to multiple receptor conformations: a practical alternative
Totrov, M., & Abagyan, R. (2008) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2008.01.004 · Cited by 497
Virtual screening strategies in drug discovery
McInnes, C. (2007) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2007.08.033 · Cited by 469
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
Huang, S., & Zou, X. (2007) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.21214 · Cited by 401
Janus kinase (JAK) inhibitors in the treatment of inflammatory and neoplastic diseases
Roskoski, R. (2016) · Pharmacological Research · doi:10.1016/j.phrs.2016.07.038 · Cited by 373
In silico ADME/T modelling for rational drug design
Wang, Y., Xing, J., Xu, Y., Zhou, N., Peng, J., Xiong, Z., Liu, X., Luo, X., Luo, C., Chen, K., et al. (2015) · Quarterly Reviews of Biophysics · doi:10.1017/s0033583515000190 · Cited by 367
Sanjeevini: a freely accessible web-server for target directed lead molecule discovery
Jayaram, B., Singh, T., Mukherjee, G., Mathur, A., Shekhar, S., & Shekhar, V. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-s17-s7 · Cited by 257
Reliable and Accurate Solution to the Induced Fit Docking Problem for Protein–Ligand Binding
Miller, E. B., Murphy, R. B., Sindhikara, D., Borrelli, K. W., Grisewood, M. J., Ranalli, F., Dixon, S. L., Jerome, S., Boyles, N. A., Day, T., et al. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00136 · Cited by 210
Self-citationAcin-Perez, R., Gatti, D. L., Bai, Y., & Manfredi, G. (2011) · Cell Metabolism · doi:10.1016/j.cmet.2011.03.024 · Cited by 190
Nguyen, D. D., Cang, Z., Wu, K., Wang, M., Cao, Y., & Wei, G.-W. (2019) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-018-0146-6 · Cited by 155
Molecular Docking Screens Using Comparative Models of Proteins
Fan, H., Irwin, J. J., Webb, B. M., Klebe, G., Shoichet, B. K., & Sali, A. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci9003706 · Cited by 135
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
Understanding the challenges of protein flexibility in drug design.
Antunes, D. A., Devaurs, D., & Kavraki, L. E. (2015) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2015.1094458 · Cited by 131
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-citationLin, X., Huang, X.-P., Chen, G., Whaley, R., Peng, S., Wang, Y., Zhang, G., Wang, S. X., Wang, S., Roth, B. L., et al. (2012) · Journal of Medicinal Chemistry · doi:10.1021/jm300338m · Cited by 73
Protein flexibility in drug discovery: from theory to computation.
Buonfiglio, R., Recanatini, M., & Masetti, M. (2015) · ChemMedChem · doi:10.1002/cmdc.201500086 · Cited by 73
Sijbesma, E., Visser, E., Plitzko, K., Thiel, P., Milroy, L.-G., Kaiser, M., Brunsveld, L., & Ottmann, C. (2020) · Nature Communications · doi:10.1038/s41467-020-17741-0 · Cited by 72
Real-Time Imaging of the Azole Class of Antifungal Drugs in Live Candida Cells
Benhamou, R. I., Bibi, M., Steinbuch, K. B., Engel, H., Levin, M., Roichman, Y., Berman, J., & Fridman, M. (2017) · ACS Chemical Biology · doi:10.1021/acschembio.7b00339 · Cited by 71
Berko, E. R., Witek, G. M., Matkar, S., Petrova, Z. O., Wu, M. A., Smith, C. M., Daniels, A., Kalna, J., Kennedy, A., Gostuski, I., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38195-0 · Cited by 53
De Luca, M. A., Tocco, G., Mostallino, R., Laus, A., Caria, F., Musa, A., Pintori, N., Ucha, M., Poza, C., Ambrosio, E., et al. (2022) · Neuropharmacology · doi:10.1016/j.neuropharm.2022.109263 · Cited by 48
Smith, Z., Strobel, M., Vani, B. P., & Tiwary, P. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01698 · Cited by 47
Musella, S., Carotenuto, L., Iraci, N., Baroli, G., Ciaglia, T., Nappi, P., Basilicata, M. G., Salviati, E., Barrese, V., Vestuto, V., et al. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c00911 · Cited by 45
Induced-Fit Docking Enables Accurate Free Energy Perturbation Calculations in Homology Models
Xu, T., Zhu, K., Beautrait, A., Vendome, J., Borrelli, K. W., Abel, R., Friesner, R. A., & Miller, E. B. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00371 · Cited by 45
Self-citationEmpowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE
Gu, X., Aranganathan, A., & Tiwary, P. (2024) · eLife · doi:10.7554/elife.99702.3 · Cited by 33
Xu, Y., Cantwell, L., Molosh, A. I., Plant, L. D., Gazgalis, D., Fitz, S. D., Dustrude, E. T., Yang, Y., Kawano, T., Garai, S., et al. (2020) · Journal of Biological Chemistry · doi:10.1074/jbc.ra119.011527 · Cited by 32
de Oliveira, A. S., Gazolla, P. A. R., Oliveira, A. F. C. D. S., Pereira, W. L., de S. Viol, L. C., Maia, A. F. D. S., Santos, E. G., da Silva, Í. E. P., Mendes, T. A. D. O., da Silva, A. M., et al. (2019) · PLOS ONE · doi:10.1371/journal.pone.0223017 · Cited by 28
Modeling the Orthosteric Binding Site of the G Protein-Coupled Odorant Receptor OR5K1
Nicoli, A., Haag, F., Marcinek, P., He, R., Kreißl, J., Stein, J., Marchetto, A., Dunkel, A., Hofmann, T., Krautwurst, D., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00752 · Cited by 28
Challenges of docking in large, flexible and promiscuous binding sites
Kotev, M., Soliva, R., & Orozco, M. (2016) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2016.08.010 · Cited by 25
Xie, B., Le Rouzic, V. P., Goldberg, A., Tsai, M.-H. M., Chen, L., Zhang, T., Sinha, A., Pan, Y.-X., Baumann, M. H., & Shi, L. (2023) · Neuropharmacology · doi:10.1016/j.neuropharm.2023.109442 · Cited by 18
Trudeau, S. J., Hwang, H., Mathur, D., Begum, K., Petrey, D., Murray, D., & Honig, B. (2023) · Protein Science · doi:10.1002/pro.4594 · Cited by 16
The comprehensive SARS-CoV-2 ‘hijackome’ knowledge base
Huuskonen, S., Liu, X., Pöhner, I., Redchuk, T., Salokas, K., Lundberg, R., Maljanen, S., Belik, M., Reinholm, A., Kolehmainen, P., et al. (2024) · Cell Discovery · doi:10.1038/s41421-024-00748-y · Cited by 12
(2024) · doi:10.1021/acscentsci.3c00822 · Cited by 9
Bruno, S., Bersani, M., Astore, S., Chiabotto, G., Barge, A., Binello, A., & Spyrakis, F. (2022) · Toxicology · doi:10.1016/j.tox.2022.153257 · Cited by 7
Lemke, J., Gollasch, M., Tsvetkov, D., & Schulig, L. (2025) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2024.2438226 · Cited by 6
“All structures were prepared with the Protein Preparation Wizard [76], and docking calculations were performed using Glide [77] (Schrödinger release 2024.2). channels, thus it appears to be highly selective [89,90].”
InfluentialAccelerated Structural Prediction of Flexible Protein–Ligand Complexes: The SLICE Method
McFarlane, J. M. B., Krause, K. D., & Paci, I. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00688 · Cited by 4
Modeling the Orthosteric Binding Site of the G Protein-Coupled Odorant Receptor OR5K1
Nicoli, A., Haag, F., Marcinek, P., He, R., Kreißl, J., Stein, J., Marchetto, A., Dunkel, A., Hofmann, T., Krautwurst, D., et al. (2022) · doi:10.1101/2022.06.01.494157 · Cited by 2
Metadata
- Authors
- Sherman, W., Day, T., Jacobson, M. P., Friesner, R. A., & Farid, R.
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2005-12-23
- Volume
- 49
- Issue
- 2
- Pages
- 534-553
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- OpenAlex
- W1975691556
- Cited by
- 2,031
- References
- 3
- FWCI
- 34.52
- 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.
