OSPREY 3.0: Open-source protein redesign for you, with powerful new features
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Abstract
We present osprey 3.0, a new and greatly improved release of the osprey protein design software. Osprey 3.0 features a convenient new Python interface, which greatly improves its ease of use. It is over two orders of magnitude faster than previous versions of osprey when running the same algorithms on the same hardware. Moreover, osprey 3.0 includes several new algorithms, which introduce substantial speedups as well as improved biophysical modeling. It also includes GPU support, which provides an additional speedup of over an order of magnitude. Like previous versions of osprey, osprey 3.0 offers a unique package of advantages over other design software, including provable design algorithms that account for continuous flexibility during design and model conformational entropy. Finally, we show here empirically that osprey 3.0 accurately predicts the effect of mutations on protein-protein binding. Osprey 3.0 is available at http://www.cs.duke.edu/donaldlab/osprey.php as free and open-source software. © 2018 Wiley Periodicals, Inc.
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Most existing machine-learning methods use physical quantities as input features, requiring considerable computational time.
“Most of the existing machine-learning methods use physical quantities as input features, which require considerable computational time to solve [17, 18].”
From Deep geometric representations for modeling effects of mutations on protein-protein binding affinity (2021) · cites this paper as [18]
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Cites
Advances in Neural Information Processing Systems 19
(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 16,037 · see citing works
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1993.341010 · Cited by 14,474 · see citing works
A Formal Basis for the Heuristic Determination of Minimum Cost Paths
Hart, P., Nilsson, N., & Raphael, B. (1968) · IEEE Transactions on Systems Science and Cybernetics · doi:10.1109/tssc.1968.300136 · Cited by 12,738 · see citing works
Salomón–Ferrer, R., Götz, A. W., Poole, D., LeGrand, S., & Walker, R. C. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400314y · Cited by 3,373 · see citing works
Nicholls, A. O., & Honig, B. H. (1991) · Journal of Computational Chemistry · doi:10.1002/jcc.540120405 · Cited by 1,272 · see citing works
Conformation of amino acid side-chains in proteins
Janin, J., Wodak, S. J., Levitt, M., & Maigret, B. (1978) · Journal of Molecular Biology · doi:10.1016/0022-2836(78)90408-4 · Cited by 786 · see citing works
Cuff, J. A., & Barton, G. J. (2000) · Proteins Structure Function and Bioinformatics · doi:10.1002/1097-0134(20000815)40:3 · Cited by 768 · see citing works
The dead-end elimination theorem and its use in protein side-chain positioning
Desmet, J., Maeyer, M. D., Hazes, B., & Lasters, I. (1992) · Nature · doi:10.1038/356539a0 · Cited by 699 · see citing works
Rocchia, W., Sridharan, S., Nicholls, A. O., Alexov, E. G., Chiabrera, A., & Honig, B. H. (2001) · Journal of Computational Chemistry · doi:10.1002/jcc.1161 · Cited by 687 · see citing works
Pelletier, H., & Kraut, J. (1992) · Science · doi:10.1126/science.1334573 · Cited by 677 · see citing works
Strong scaling of general-purpose molecular dynamics simulations on GPUs
Gläser, J., Nguyen, T. D., Anderson, J. A., Lui, P., Spiga, F., Millan, J. A., Morse, D. C., & Glotzer, S. C. (2015) · Computer Physics Communications · doi:10.1016/j.cpc.2015.02.028 · Cited by 665 · see citing works
Word, J. M., Lovell, S. C., LaBean, T. H., Taylor, H. C., Zalis, M. E., Presley, B. K., Richardson, J. S., & Richardson, D. C. (1999) · Journal of Molecular Biology · doi:10.1006/jmbi.1998.2400 · Cited by 531 · see citing works
Structure of the Quaternary Complex of Interleukin-2 with Its α, ß, and γ c Receptors
Wang, X., Rickert, M., & García, K. C. (2005) · Science · doi:10.1126/science.1117893 · Cited by 476 · see citing works
Structural Linkage between Ligand Discrimination and Receptor Activation by Type I Interferons
Thomas, C., Moraga, I., Levin, D., Krutzik, P. O., Podoplelova, Y., Trejo, A., Lee, C.-H., Yarden, G., Vleck, S. E., Glenn, J. S., et al. (2011) · Cell · doi:10.1016/j.cell.2011.06.048 · Cited by 358 · see citing works
De novo design of a transmembrane Zn 2+ -transporting four-helix bundle
Joh, N. H., Wang, T., Bhate, M., Acharya, R., Wu, Y., Grabe, M., Hong, M., Grigoryan, G., & DeGrado, W. F. (2014) · Science · doi:10.1126/science.1261172 · Cited by 301 · see citing works
Rudicell, R. S., Kwon, Y. D., Ko, S., Pegu, A., Louder, M. K., Georgiev, I. S., Wu, X., Zhu, J., Boyington, J. C., Chen, X., et al. (2014) · Journal of Virology · doi:10.1128/jvi.02213-14 · Cited by 282 · see citing works
Self-citationInteraction of barnase with its polypeptide inhibitor barstar studied by protein engineering
Schreiber, G., & Fersht, A. R. (1993) · Biochemistry · doi:10.1021/bi00070a025 · Cited by 280 · see citing works
The Backrub Motion: How Protein Backbone Shrugs When a Sidechain Dances
Davis, I., Arendall, W. B., Richardson, D. C., & Richardson, J. S. (2006) · Structure · doi:10.1016/j.str.2005.10.007 · Cited by 264 · see citing works
Crystal structure of the Atx1 metallochaperone protein at 1.02 Å resolution
Rosenzweig, A. C., Huffman, D. L., Hou, M. Y., Wernimont, A. K., Pufahl, R. A., & O’Halloran, T. V. (1999) · Structure · doi:10.1016/s0969-2126(99)80082-3 · Cited by 236 · see citing works
Computational structure-based redesign of enzyme activity
Chen, C., Georgiev, I. S., Anderson, A. C., & Donald, B. R. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0900266106 · Cited by 210 · see citing works
Self-citationPiehler, J., Roisman, L. C., & Schreiber, G. (2000) · Journal of Biological Chemistry · doi:10.1074/jbc.m006854200 · Cited by 152 · see citing works
Georgiev, I. S., Lilien, R., & Donald, B. R. (2008) · Journal of Computational Chemistry · doi:10.1002/jcc.20909 · Cited by 136 · see citing works
Self-citationFrisch, C., Schreiber, G., Johnson, C. M., & Fersht, A. R. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1997.0892 · Cited by 133 · see citing works
Bingham, R. J., Rudiño-Piñera, E., Meenan, N. A. G., Schwarz‐Linek, U., Turkenburg, J. P., Höök, M., Garman, E. F., & Potts, J. R. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0803556105 · Cited by 128 · see citing works
Algorithms in Structural Molecular Biology
Donald, B. R. (2011) · Cited by 126 · see citing works
Self-citationPredicting resistance mutations using protein design algorithms
Frey, K. M., Georgiev, I. S., Donald, B. R., & Anderson, A. C. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1002162107 · Cited by 121 · see citing works
Self-citationLilien, R., Stevens, B. W., Anderson, A. C., et al. (2005) · Journal of Computational Biology · doi:10.1089/cmb.2005.12.740 · Cited by 117 · see citing works
Computational Design of a PDZ Domain Peptide Inhibitor that Rescues CFTR Activity
Roberts, K. E., Cushing, P. R., Boisguérin, P., Madden, D. R., & Donald, B. R. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002477 · Cited by 116 · see citing works
Self-citationGaínza, P., Roberts, K. E., Georgiev, I. S., Lilien, R., Keedy, D. A., Chen, C., Reza, F., Anderson, A. C., Richardson, D. C., Richardson, J. S., et al. (2013) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-394292-0.00005-9 · Cited by 115 · see citing works
Self-citationGorczynski, M. J., Grembecka, J. E., Zhou, Y., Kong, Y., Roudaia, L., Douvas, M. G., Newman, M. N., Bielnicka, I., Baber, G. B., Corpora, T., et al. (2007) · Chemistry & Biology · doi:10.1016/j.chembiol.2007.09.006 · Cited by 114 · see citing works
Self-citationProtein Design Using Continuous Rotamers
Gaínza, P., Roberts, K. E., & Donald, B. R. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002335 · Cited by 101 · see citing works
Self-citationFast and accurate algorithms for protein side-chain packing
Xu, J., & Berger, B. (2006) · Journal of the ACM · doi:10.1145/1162349.1162350 · Cited by 89 · see citing works
Mapping Polyclonal HIV-1 Antibody Responses via Next-Generation Neutralization Fingerprinting
Doria‐Rose, N. A., Altae-Tran, H., Roark, R. S., Schmidt, S. D., Sutton, M. S., Louder, M. K., Chuang, G., Bailer, R. T., Cortez, V. A., Kong, R., et al. (2017) · PLoS Pathogens · doi:10.1371/journal.ppat.1006148 · Cited by 87 · see citing works
Georgiev, I. S., Rudicell, R. S., Saunders, K. O., Shi, W., Kirys, T., McKee, K., O'Dell, S. J., Chuang, G., Yang, Z., Ofek, G., et al. (2014) · The Journal of Immunology · doi:10.4049/jimmunol.1302515 · Cited by 85 · see citing works
Hallen, M. A., Keedy, D. A., & Donald, B. R. (2012) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24150 · Cited by 85 · see citing works
Self-citationAlgorithm for backrub motions in protein design
Georgiev, I. S., Keedy, D. A., Richardson, J. S., Richardson, D. C., & Donald, B. R. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn169 · Cited by 85 · see citing works
Self-citationChuang, G., Geng, H., Pancera, M., Xu, K. Y., Cheng, C., Acharya, P., Chambers, M. J., Druz, A., Tsybovsky, Y., Wanninger, T. G., et al. (2017) · Journal of Virology · doi:10.1128/jvi.02268-16 · Cited by 83 · see citing works
Stevens, B. W., Lilien, R., Georgiev, I. S., et al. (2006) · Biochemistry · doi:10.1021/bi061788m · Cited by 72 · see citing works
Gaínza, P., Nisonoff, H. M., & Donald, B. R. (2016) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2016.03.006 · Cited by 72 · see citing works
Self-citationStructure-Guided Deimmunization of Therapeutic Proteins
Parker, A. S., Choi, Y., Griswold, K. E., & Bailey‐Kellogg, C. (2013) · Journal of Computational Biology · doi:10.1089/cmb.2012.0251 · Cited by 61 · see citing works
Zhao, H., Verma, D., Li, W., et al. (2015) · Chemistry & Biology · doi:10.1016/j.chembiol.2015.04.017 · Cited by 57 · see citing works
Robb, R. J., Rusk, C. M., & Neeper, M. P. (1988) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.85.15.5654 · Cited by 56 · see citing works
Georgiev, I. S., Lilien, R., & Donald, B. R. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl220 · Cited by 54 · see citing works
Self-citationWater-mediated interaction at a protein–protein interface
Ikura, T., Urakubo, Y., & Ito, N. (2004) · Chemical Physics · doi:10.1016/j.chemphys.2004.05.010 · Cited by 53 · see citing works
Protein design algorithms predict viable resistance to an experimental antifolate
Reeve, S. M., Gaínza, P., Frey, K. M., Georgiev, I. S., Donald, B. R., & Anderson, A. C. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1411548112 · Cited by 51 · see citing works
Self-citationHuang, E. S., Koehl, P., Levitt, M., Pappu, R. V., & Ponder, J. W. (1998) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(19981101)33:2 · Cited by 49 · see citing works
Electronic Structure Calculations on Graphics Processing Units
(2016) · Wiley eBooks · doi:10.1002/9781118670712 · Cited by 49 · see citing works
Erman, J. E., Kresheck, G. C., Vitello, L. B., et al. (1997) · Biochemistry · doi:10.1021/bi962632x · Cited by 34 · see citing works
Hallen, M. A., & Donald, B. R. (2016) · Journal of Computational Biology · doi:10.1089/cmb.2015.0188 · Cited by 34 · see citing works
Self-citationSalvat, R. S., Choi, Y., Bishop, A., et al. (2015) · Biotechnology and Bioengineering · doi:10.1002/bit.25554 · Cited by 30 · see citing works
Reeve, S. M., Scocchera, E. W., G-Dayanadan, N., Keshipeddy, S. K., Krucinska, J., Hajian, B., Ferreira, J., Nailor, M. D., Aeschlimann, J. R., Wright, D. L., et al. (2016) · Cell chemical biology · doi:10.1016/j.chembiol.2016.11.007 · Cited by 30 · see citing works
Ojewole, A. A., Jou, J., Fowler, V. G., et al. (2018) · Journal of Computational Biology · doi:10.1089/cmb.2017.0267 · Cited by 29 · see citing works
Self-citationJou, J., Jain, S., Georgiev, I. S., et al. (2016) · Journal of Computational Biology · doi:10.1089/cmb.2015.0194 · Cited by 24 · see citing works
Self-citationDesign of epitope-specific probes for sera analysis and antibody isolation
Georgiev, I. S., Acharya, P., Schmidt, S., et al. (2012) · Retrovirology · doi:10.1186/1742-4690-9-s2-p50 · Cited by 23 · see citing works
Compact Representation of Continuous Energy Surfaces for More Efficient Protein Design
Hallen, M. A., Gaínza, P., & Donald, B. R. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/ct501031m · Cited by 21 · see citing works
Self-citationAn efficient parallel algorithm for accelerating computational protein design
Zhou, Y., Xu, W., Donald, B. R., & Zeng, J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu264 · Cited by 19 · see citing works
Self-citationHallen, M. A., & Donald, B. R. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx277 · Cited by 19 · see citing works
Self-citationFast gap-free enumeration of conformations and sequences for protein design
Roberts, K. E., Gaínza, P., Hallen, M. A., & Donald, B. R. (2015) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24870 · Cited by 18 · see citing works
Self-citationOSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design
Ojewole, A. A., Lowegard, A. U., Gaínza, P., et al. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-6637-0_15 · Cited by 17 · see citing works
Self-citationHallen, M. A., Jou, J., & Donald, B. R. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-31957-5_9 · Cited by 5 · see citing works
Self-citationA critical analysis of computational protein design with sparse residue interaction graphs
Jain, S., Jou, J., Georgiev, I. S., et al. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005346 · Cited by 3 · see citing works
Self-citationcOSPREY: A Cloud-Based Distributed Algorithm for Large-Scale Computational Protein Design
Pan, Y., Dong, Y. C., Zhou, J., et al. (2016) · Journal of Computational Biology · doi:10.1089/cmb.2015.0234 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 12
- [12]Georgiev I, Acharya P, Schmidt S, Li Y, Wycuff D, Ofek G, Doria-Rose N, Luongo T, Yang Y, Zhou T,, Retrovirology 9, P50 (2012).
- [18]Hallen MA, Jou JD, and Donald BR, in International Conference on Research in Computational Molecular Biology (Springer, 2016), pp. 122– 136.
- [34]Doria-Rose NA, Altae-Tran HR, Roark RS, Schmidt SD, Sutton MS, Louder MK, Chuang GY, Bailer RT, Cortez V, Kong R,, PLoS Pathog. 13, e1006148 (2017). 28052137 PMC5241146
- [41]Szerwinski R and Güneysu T, in International Workshop on Cryptographic Hardware and Embedded Systems (Springer, 2008), pp.* 79– 99.
- [42]Glaser J, Nguyen TD, Anderson JA, Lui P, Spiga F, Millan JA, Morse DC, and Glotzer SC, Computer Physics Communications 192, 97 (2015).
- [43]Salomon-Ferrer R, Götz AW, Poole D, Le Grand S, and Walker RC, Journal of Chemical Theory and Computation 9, 3878 (2013). 26592383
- [44]Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, and Lindahl E, SoftwareX 1, 19 (2015).
- [51]Nicholls A and Honig B, Journal of Computational Chemistry 12, 435 (1991). 11913378
- [52]Rochia W, Sridharan S, Nicholls A, Alexov E, Chiabrera A, and Honig B, Journal of Computational Chemistry 23, 128 (2002). 11913378
- [55]Hart PE, Nilsson NJ, and Raphael B, IEEE Transactions on Systems Science and Cybernetics 4, 100 (1968).
- [56]Xu J and Berger B, Journal of the ACM 53, 533 (2006).
- [58]Ikura T, Urakubo Y, and Ito N, Chemical Physics 307, 111 (2004).
Cited by
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Advances in protein structure prediction and design
Kuhlman, B., & Bradley, P. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0163-x · Cited by 806 · see citing works
Deep geometric representations for modeling effects of mutations on protein-protein binding affinity
Liu, X., Luo, Y., Li, P., Song, S., & Peng, J. (2021) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009284 · Cited by 140 · see citing works
- Authors
- Hallen, M. A., Martin, J. W., Ojewole, A., Jou, J. D., Lowegard, A. U., Frenkel, M. S., Gainza, P., Nisonoff, H. M., Mukund, A., Wang, S., Holt, G. T., Zhou, D., Dowd, E., & Donald, B. R.
- Affiliations
- Toyota Technological Institute at Chicago (US)
- Duke University (US)
- Duke Medical Center (US)
- Department of Computer Science Duke University Durham North Carolina, 27708
- Corresponding author
- Donald, Bruce R.
- ORCID
- Venue
- Journal of Computational Chemistry
- Publisher
- Wiley
- Published
- 2018-10-14
- Volume
- 39
- Issue
- 30
- Pages
- 2494-2507
- ISSN
- 0192-8651 (ISSN-L)
- 1096-987X
- PMC
- PMC6391056
- OpenAlex
- W2951122824
- Cited by
- 72
- References
- 62
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 12 · 2023: 8 · 2022: 14 · 2021: 10 · 2020: 9 · 2019: 9 · 2018: 1
- FWCI
- 2.57
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Computational Chemistry (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation
- Pharmaceutical Research and Manufacturers of America Foundation
- National Institutes of Health (R01 GM‐118543, R01 GM‐78031)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- Pandion haliaetus, protein design, protein redesign, open source software, Python interface, GPU acceleration, conformational entropy, protein-protein binding, mutation effect prediction, design algorithm
- MeSH terms
- Algorithms, Models, Molecular, Protein Binding, Protein Conformation, Proteins, Software
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