Efficient flexible backbone protein-protein docking for challenging targets
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Abstract
Abstract Computational prediction of protein-protein complex structures facilitates a fundamental understanding of biological mechanisms and enables therapeutics design. Binding-induced conformational changes challenge all current computational docking algorithms by exponentially increasing the conformational space to be explored. To restrict this search to relevant space, some computational docking algorithms exploit the inherent flexibility of the protein monomers to simulate conformational selection from pre-generated ensembles. As the ensemble size expands with increased protein flexibility, these methods struggle with efficiency and high false positive rates. Here, we develop and benchmark a method that efficiently samples large conformational ensembles of flexible proteins and docks them using a novel, six-dimensional, coarse-grained score function. A strong discriminative ability allows an eight-fold higher enrichment of nearnative candidate structures in the coarse-grained phase compared to a previous method. Further, the method adapts to the diversity of backbone conformations in the ensemble by modulating sampling rates. It samples 100 conformations each of the ligand and the receptor backbone while increasing computational time by only 20–80%. In a benchmark set of 88 proteins of varying degrees of flexibility, the expected success rate for blind predictions after resampling is 77% for rigid complexes, 49% for moderately flexible complexes, and 31% for highly flexible complexes. These success rates on flexible complexes are a substantial step forward from all existing methods. Additionally, for highly flexible proteins, we demonstrate that when a suitable conformer generation method exists, RosettaDock 4.0 can dock the complex successfully. Significance Predicting binding-induced conformational plasticity in protein backbones remains a principal challenge in computational protein–protein docking. To date, there are no methods that can reliably dock proteins that undergo more than 1 Å root-mean-squared-deviation of the backbones of the interface residues upon binding. Here, we present a method that samples backbone motions and scores conformations rapidly, obtaining–for the first time–successful docking of nearly 50% of flexible target complexes with backbone conformational change up to 2.2 Å RMSD. This method will be applicable to a broader range of protein docking problems, which in turn will help us understand biomolecular assembly and protein function.
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Evidence points to the conformational selection binding mechanism, which allows one to precompute the conformational ensemble of the interacting molecule(s) and dock the separate conformers with limited conformational search.
“In a long-standing debate between the proponents of the “induced-fit” and the “conformational selection” binding mechanisms [21–23], the evidence pointing to the latter allows one to precompute the conformational ensemble of the interacting molecule(s) and dock the separate conformers with limited conformational search [24,25].”
From Challenges in protein docking (2020) · cites this paper as [24]
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Cites
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
The ClusPro web server for protein–protein docking
Kozakov, D., Hall, D. R., Xia, B., Porter, K. A., Padhorny, D., Yueh, C., Beglov, D., & Vajda, S. (2017) · Nature Protocols · doi:10.1038/nprot.2016.169 · Cited by 3,297 · see citing works
Anisotropy of Fluctuation Dynamics of Proteins with an Elastic Network Model
Atılgan, A. R., Durell, S. R., Jernigan, R. L., Demirel, M. C., Keskin, Ö., & Bahar, İ. (2001) · Biophysical Journal · doi:10.1016/s0006-3495(01)76033-x · Cited by 1,638 · see citing works
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 · see citing works
Self-citationMonte Carlo-minimization approach to the multiple-minima problem in protein folding.
Li, Z., & Scheraga, H. A. (1987) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.84.19.6611 · Cited by 1,475 · see citing works
Improved prediction of protein side‐chain conformations with SCWRL4
Krivov, G. G., Shapovalov, M. V., & Dunbrack, R. L. (2009) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22488 · Cited by 1,312 · see citing works
Gray, J. J., Moughon, S. E., Wang, C., Schueler‐Furman, O., Kuhlman, B. A., Rohl, C. A., & Baker, D. A. (2003) · Journal of Molecular Biology · doi:10.1016/s0022-2836(03)00670-3 · Cited by 1,130 · see citing works
Self-citationCoarse-Grained Protein Models and Their Applications
Kmiecik, S., Gront, D., Koliński, M., Wieteska, Ł., Badaczewska-Dawid, A. E., & Koliński, A. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00163 · Cited by 1,067 · see citing works
RosettaScripts: A Scripting Language Interface to the Rosetta Macromolecular Modeling Suite
Fleishman, S. J., Leaver‐Fay, A., Corn, J. E., Strauch, E., Khare, S. D., Koga, N., Ashworth, J. R., Murphy, P. M., Richter, F. M., Lemmon, G., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0020161 · Cited by 659 · see citing works
Accelerating Protein Docking in ZDOCK Using an Advanced 3D Convolution Library
Pierce, B. G., Hourai, Y., & Weng, Z. (2011) · PLoS ONE · doi:10.1371/journal.pone.0024657 · Cited by 645 · see citing works
Park, H., Bradley, P. H., Greisen, P. J., Liu, Y., Mulligan, V. K., Kim, D. E., Baker, D. A., & DiMaio, F. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00819 · Cited by 564 · see citing works
Protein–protein docking benchmark version 4.0
Hwang, H., Vreven, T., Janin, J., & Weng, Z. (2010) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22830 · Cited by 474 · see citing works
Assessment of blind predictions of protein–protein interactions: Current status of docking methods
Mendez, R., Leplae, R., Maria, L. D., & Wodak, S. (2003) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.10393 · Cited by 438 · see citing works
Vreven, T., Moal, I. H., Vangone, A., Pierce, B. G., Kastritis, P. L., Torchala, M., Chaleil, R., Jiménez-García, B., Bates, P. A., Fernandez-Recio, J., et al. (2015) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.07.016 · Cited by 424 · see citing works
Protein–Protein Docking with Backbone Flexibility
Wang, C., Bradley, P. H., & Baker, D. A. (2007) · Journal of Molecular Biology · doi:10.1016/j.jmb.2007.07.050 · Cited by 413 · see citing works
Alternate States of Proteins Revealed by Detailed Energy Landscape Mapping
Tyka, M. D., Keedy, D. A., André, I., DiMaio, F., Song, Y., Richardson, D. C., Richardson, J. S., & Baker, D. A. (2010) · Journal of Molecular Biology · doi:10.1016/j.jmb.2010.11.008 · Cited by 410 · see citing works
De Novo Enzyme Design Using Rosetta3
Richter, F. M., Leaver‐Fay, A., Khare, S. D., Bjelic, S., & Baker, D. A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0019230 · Cited by 359 · see citing works
Docking and scoring protein complexes: CAPRI 3rd Edition
Lensink, M., Mendez, R., & Wodak, S. (2007) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.21804 · Cited by 351 · see citing works
Conformational Selection or Induced Fit? A Critical Appraisal of the Kinetic Mechanism
Vogt, A. D., & Di, E. (2012) · Biochemistry · doi:10.1021/bi3006913 · Cited by 327 · see citing works
Protein-Protein Docking: From Interaction to Interactome
Vakser, I. A. (2014) · Biophysical Journal · doi:10.1016/j.bpj.2014.08.033 · Cited by 318 · see citing works
Smith, C. A., & Kortemme, T. (2008) · Journal of Molecular Biology · doi:10.1016/j.jmb.2008.05.023 · Cited by 314 · see citing works
Benchmarking and Analysis of Protein Docking Performance in Rosetta v3.2
Chaudhury, S., Berrondo, M., Weitzner, B. D., Muthu, P., Bergman, H., & Gray, J. J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0022477 · Cited by 306 · see citing works
Self-citationComputer analysis of protein-protein interaction
Wodak, S. J., & Janin, J. (1978) · Journal of Molecular Biology · doi:10.1016/0022-2836(78)90302-9 · Cited by 280 · see citing works
Conformational selection or induced fit? 50 years of debate resolved
Changeux, J., & Edelstein, S. J. (2011) · F1000 Biology Reports · doi:10.3410/b3-19 · Cited by 272 · see citing works
Combined Covalent-Electrostatic Model of Hydrogen Bonding Improves Structure Prediction with Rosetta
O’Meara, M. J., Leaver‐Fay, A., Tyka, M. D., Stein, A., Houlihan, K., DiMaio, F., Bradley, P. H., Kortemme, T., Baker, D. A., Snoeyink, J. S., et al. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/ct500864r · Cited by 232 · see citing works
Improved side‐chain modeling for protein–protein docking
Wang, C., Schueler‐Furman, O., & Baker, D. A. (2005) · Protein Science · doi:10.1110/ps.041222905 · Cited by 228 · see citing works
Modeling protein–protein and protein–peptide complexes: CAPRI 6th edition
Lensink, M. F., Velankar, S., & Wodak, S. J. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25215 · Cited by 222 · cited 1× · see citing works
“The most recent rounds of the blind docking challenge, Critical Assessment of PRediction of Interactions (CAPRI), demonstrated that protein flexibility is still a community-wide weakness, with flexible target complexes eliciting no successful predictions from any method (16, 17).”
BackgroundA Unified Conformational Selection and Induced Fit Approach to Protein-Peptide Docking
Trellet, M. E., Melquiond, A. S. J., & Bonvin, A. M. J. J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058769 · Cited by 200 · see citing works
Complementarity of Structure Ensembles in Protein-Protein Binding
Grünberg, R., Leckner, J., & Nilges, M. (2004) · Structure · doi:10.1016/j.str.2004.09.014 · Cited by 182 · see citing works
Chaudhury, S., & Gray, J. J. (2008) · Journal of Molecular Biology · doi:10.1016/j.jmb.2008.05.042 · Cited by 164 · see citing works
Self-citationSwarmDock and the Use of Normal Modes in Protein-Protein Docking
Moal, I. H., & Bates, P. A. (2010) · International Journal of Molecular Sciences · doi:10.3390/ijms11103623 · Cited by 154 · see citing works
Lensink, M. F., Velankar, S. S., Kryshtafovych, A., Huang, S., Schneidman‐Duhovny, D., Šali, A., Segura, J., Fernández‐Fuentes, N., Viswanath, S., Elber, R., et al. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25007 · Cited by 153 · see citing works
Self-citationPrediction of protein–protein interactions: the CAPRI experiment, its evaluation and implications
Wodak, S. J., & Méndez, R. (2004) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2004.02.003 · Cited by 148 · see citing works
Coarse-grain modelling of protein–protein interactions
Baaden, M., & Marrink, . J. (2013) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2013.09.004 · Cited by 135 · see citing works
Computational design of self-assembling cyclic protein homo-oligomers
Fallas, J. A., Ueda, G., Sheffler, W., Nguyen, V., McNamara, D. E., Sankaran, B., Pereira, J. H., Parmeggiani, F., Brunette, T., Cascio, D., et al. (2016) · Nature Chemistry · doi:10.1038/nchem.2673 · Cited by 135 · see citing works
Self-citationProtein–protein docking predictions for the CAPRI experiment
Gray, J. J., Moughon, S., Kortemme, T., et al. (2003) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10384 · Cited by 102 · see citing works
Self-citationFiberDock: a web server for flexible induced-fit backbone refinement in molecular docking
Mashiach, E., Nussinov, R., & Wolfson, H. J. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq373 · Cited by 91 · see citing works
Blind predictions of protein interfaces by docking calculations in CAPRI
Lensink, M. F., & Wodak, S. J. (2010) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22850 · Cited by 80 · see citing works
Non-Redundant Unique Interface Structures as Templates for Modeling Protein Interactions
Cukuroglu, E., Gürsoy, A., Nussinov, R., & Keskin, Ö. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086738 · Cited by 76 · see citing works
Flexible protein docking refinement using pose‐dependent normal mode analysis
Venkatraman, V., & Ritchie, D. W. (2012) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24115 · Cited by 66 · see citing works
Schindler, C., de Vries, S. J., & Zacharias, M. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24728 · Cited by 65 · see citing works
Quantifying the topography of the intrinsic energy landscape of flexible biomolecular recognition
Chu, X., Gan, L., Wang, E., & Wang, J. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1220699110 · Cited by 62 · see citing works
Sense and simplicity in HADDOCK scoring: Lessons from CASP‐CAPRI round 1
Vangone, A., Rodrigues, J., Xue, L. C., van Zundert, G. C. P., Geng, C., Kurkcuoglu, Z., Nellen, M., Narasimhan, S., Karaca, E., van Dijk, M., et al. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25198 · Cited by 62 · see citing works
CAPRI rounds 3–5 reveal promising successes and future challenges for RosettaDock
Daily, M. D., Masica, D. L., Sivasubramanian, A., Somarouthu, S., & Gray, J. J. (2005) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20555 · Cited by 59 · see citing works
Self-citationPredictive Bcl-2 Family Binding Models Rooted in Experiment or Structure
DeBartolo, J., Dutta, S., Reich, L. “., & Keating, A. E. (2012) · Journal of Molecular Biology · doi:10.1016/j.jmb.2012.05.022 · Cited by 56 · see citing works
A generalized approach to sampling backbone conformations with RosettaDock for CAPRI rounds 13–19
Sircar, A., Chaudhury, S., Kilambi, K. P., et al. (2010) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22765 · Cited by 48 · see citing works
Incorporating biochemical information and backbone flexibility in RosettaDock for CAPRI rounds 6–12
Chaudhury, S., Sircar, A., Sivasubramanian, A., Berrondo, M., & Gray, J. J. (2007) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21731 · Cited by 44 · see citing works
Self-citationPushing the Backbone in Protein-Protein Docking
Kuroda, D., & Gray, J. J. (2016) · Structure · doi:10.1016/j.str.2016.06.025 · Cited by 44 · see citing works
Self-citationPredicting Protein Dynamics and Allostery Using Multi-Protein Atomic Distance Constraints
Greener, J. G., Filippis, I., & Sternberg, M. J. E. (2017) · Structure · doi:10.1016/j.str.2017.01.008 · Cited by 44 · see citing works
Utilizing Experimental Data for Reducing Ensemble Size in Flexible-Protein Docking
Xu, M., & Lill, M. A. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci200428t · Cited by 38 · see citing works
Oliwa, T., & Shen, Y. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv252 · Cited by 34 · see citing works
Sense and Simplicity in HADDOCK Scoring: Lessons from CASP‐CAPRI (page 418)
Vangone, A., Rodrigues, J., Xue, L. C., van Zundert, G. C. P., Geng, C., Kurkcuoglu, Z., Nellen, M., Narasimhan, S., Karaca, E., van Dijk, M., et al. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25339 · Cited by 33 · see citing works
Structural templates for comparative protein docking
Anishchenko, I. V., Kundrotas, P. J., Tuzikov, A. V., & Vakser, I. A. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24736 · Cited by 26 · see citing works
Replica Exchange Improves Sampling in Low-Resolution Docking Stage of RosettaDock
Zhang, Z., & Lange, O. F. (2013) · PLoS ONE · doi:10.1371/journal.pone.0072096 · Cited by 21 · see citing works
Modeling oblong proteins and water‐mediated interfaces with RosettaDock in CAPRI rounds 28–35
Marze, N. A., Jeliazkov, J. R., Burman, S. S. R., Boyken, S. E., DiMaio, F., & Gray, J. J. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25168 · Cited by 21 · see citing works
Self-citationKilambi, K. P., Pacella, M. S., Xu, J., Labonte, J. W., Porter, J. R., Muthu, P., Drew, K., Kuroda, D., Schueler‐Furman, O., Bonneau, R., et al. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24425 · Cited by 20 · see citing works
Self-citationMonte Carlo replica-exchange based ensemble docking of protein conformations
Zhang, Z., Ehmann, U., & Zacharias, M. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25262 · Cited by 19 · see citing works
Conformational Heterogeneity of Unbound Proteins Enhances Recognition in Protein–Protein Encounters
Pallara, C., Rueda, M., Abagyan, R. A., & Fernández‐Recio, J. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00204 · Cited by 15 · see citing works
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Zaidman, D., Prilusky, J., & London, N. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00589 · Cited by 224 · see citing works
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Siebenmorgen, T., & Zacharias, M. (2020) · WIREs Computational Molecular Science · doi:10.1002/wcms.1448 · Cited by 184 · see citing works
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MethodVakser, I. A. (2020) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2020.07.001 · Cited by 62 · see citing works
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Self-citation
- Authors
- Marze, N. A., Roy Burman, S. S., Sheffler, W., & Gray, J. J.
- Affiliations
- Johns Hopkins University (US)
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- University of Washington (US)
- Corresponding author
- Gray, Jeffrey J
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-04-30
- Volume
- 34
- Issue
- 20
- Pages
- 3461-3469
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC6184633
- OpenAlex
- W2778809918
- Cited by
- 173
- References
- 58
- Citations by year
- 2026: 19 · 2025: 29 · 2024: 24 · 2023: 29 · 2022: 23 · 2021: 23 · 2020: 19 · 2019: 5 · 2018: 2
- FWCI
- 4.33
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (R01-GM078221)
- National Science Foundation (1507736)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- protein-protein docking, flexible proteins, conformational selection, backbone flexibility, conformational ensembles, blind docking
- MeSH terms
- Algorithms, Ligands, Protein Binding, Protein Conformation, Proteins, Software
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