The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.
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Schrödinger. Biologics design. https://www.schrodinger.com/science-articles/biologics-design (2020).
Chemical Computing Group. Molecular Operating Environment (MOE) | MOEsaic | PSILO. https://www.chemcomp.com/Products.htm (2020).
Dassault Systèmes. BIOVIA, Discovery Studio Modeling Environment, release 2017. https://www.3dsbiovia.com/products/collaborative-science/biovia-discovery-studio/ (2016).
Steinegger, M. et al. HH-suite3 for fast remote homology detection and deep protein annotation. BMC Bioinformatics 20, 473 (2019).
Vu, O., Mendenhall, J., Altarawy, D. & Meiler, J. BCL:Mol2D-a robust atom environment descriptor for QSAR modeling and lead optimization. J. Comput. Aided Mol. Des. 33, 477–486 (2019).
Webb, B. et al. Integrative structure modeling with the Integrative Modeling Platform. Protein Sci. 27, 245–258 (2018).
O’Boyle, N. M. et al. Open Babel: an open chemical toolbox. J. Cheminform. 3, 33 (2011).
Brooks, B. R. et al. CHARMM: the biomolecular simulation program. J. Comput. Chem. 30, 1545–1614 (2009).
Wang, J., Wolf, R. M., Caldwell, J. W., Kollman, P. A. & Case, D. A. Development and testing of a general amber force field. J. Comput. Chem. 25, 1157–1174 (2004).
Van Der Spoel, D. et al. GROMACS: fast, flexible, and free. J. Comput. Chem. 26, 1701–1718 (2005).
Eastman, P. et al. OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLoS Comput. Biol. 13, e1005659 (2017).
Senior, A. W. et al. Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13). Proteins 87, 1141–1148 (2019).
Senior, A. W. et al. Improved protein structure prediction using potentials from deep learning. Nature 577, 706–710 (2020).
Zheng, W. et al. Deep-learning contact-map guided protein structure prediction in CASP13. Proteins 87, 1149–1164 (2019).
Xu, J. & Wang, S. Analysis of distance-based protein structure prediction by deep learning in CASP13. Proteins 87, 1069–1081 (2019).
Fiser, A. & Sali, A. Modeller: generation and refinement of homology-based protein structure models. Methods Enzymol. 374, 461–491 (2003).
Bienert, S. et al. The SWISS-MODEL Repository—new features and functionality. Nucleic Acids Res. 45 D1, D313–D319 (2017).
Yang, J. et al. The I-TASSER Suite: protein structure and function prediction. Nat. Methods 12, 7–8 (2015).
van Zundert, G. C. P. et al. The HADDOCK2.2 web server: user-friendly integrative modeling of biomolecular complexes. J. Mol. Biol. 428, 720–725 (2016).
Pierce, B. G. et al. ZDOCK server: interactive docking prediction of protein-protein complexes and symmetric multimers. Bioinformatics 30, 1771–1773 (2014).
Padhorny, D. et al. Protein-protein docking by fast generalized Fourier transforms on 5D rotational manifolds. Proc. Natl Acad. Sci. USA 113, E4286–E4293 (2016).
Trott, O. & Olson, A. J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461 (2010).
BioSolveIT GmbH. FlexX version 4.1. http://www.biosolveit.de/FlexX (2019).
Tubert-Brohman, I., Sherman, W., Repasky, M. & Beuming, T. Improved docking of polypeptides with Glide. J. Chem. Inf. Model. 53, 1689–1699 (2013).
Sorenson, J. M. & Head-Gordon, T. Matching simulation and experiment: a new simplified model for simulating protein folding. J. Comput. Biol. 7, 469–481 (2000).
Koehler Leman, J. et al. Better together: Elements of successful scientific software development in a distributed collaborative community. PLoS Comput. Biol. 16, e1007507 (2020).
Leaver-Fay, A. et al. ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol. 487, 545–574 (2011).
Alford, R. F. et al. The Rosetta all-atom energy function for macromolecular modeling and design. J. Chem. Theory Comput. 13, 3031–3048 (2017).
Park, H. et al. Simultaneous optimization of biomolecular energy functions on features from small molecules and macromolecules. J. Chem. Theory Comput. 12, 6201–6212 (2016).
Chaudhury, S., Lyskov, S. & Gray, J. J. PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta. Bioinformatics 26, 689–691 (2010).
Fleishman, S. J. et al. RosettaScripts: a scripting language interface to the Rosetta macromolecular modeling suite. PLoS One 6, e20161 (2011).
Cooper, S. et al. Predicting protein structures with a multiplayer online game. Nature 466, 756–760 (2010).
Bender, B. J. et al. Protocols for molecular modeling with Rosetta3 and RosettaScripts. Biochemistry https://doi.org/10.1021/acs.biochem.6b00444 (2016).
Simoncini, D. et al. Guaranteed discrete energy optimization on large protein design problems. J. Chem. Theory Comput. 11, 5980–5989 (2015).
Leaver-Fay, A. et al. Scientific benchmarks for guiding macromolecular energy function improvement. Methods Enzymol. 523, 109–143 (2013).
Jorgensen, W. L., Jorgensen, W. L., Maxwell, D. S. & Tirado-Rives, J. Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J. Am. Chem. Soc. 118, 11225–11236 (1996).
Radzicka, A. & Wolfenden, R. Comparing the polarities of the amino acids: side-chain distribution coefficients between the vapor phase, cyclohexane, 1-octanol, and neutral aqueous solution. Biochemistry 27, 1664–1670 (1988).
O’Meara, M. J. et al. Combined covalent-electrostatic model of hydrogen bonding improves structure prediction with Rosetta. J. Chem. Theory Comput. 11, 609–622 (2015).
Conway, P., Tyka, M. D., DiMaio, F., Konerding, D. E. & Baker, D. Relaxation of backbone bond geometry improves protein energy landscape modeling. Protein Sci. 23, 47–55 (2014).
Park, H., Lee, H. & Seok, C. High-resolution protein-protein docking by global optimization: recent advances and future challenges. Curr. Opin. Struct. Biol. 35, 24–31 (2015).
Kellogg, E. H., Leaver-Fay, A. & Baker, D. Role of conformational sampling in computing mutation-induced changes in protein structure and stability. Proteins 79, 830–838 (2011).
Mills, J. H. et al. Computational design of an unnatural amino acid dependent metalloprotein with atomic level accuracy. J. Am. Chem. Soc. 135, 13393–13399 (2013).
Kappel, K. et al. Blind tests of RNA-protein binding affinity prediction. Proc. Natl Acad. Sci. USA 116, 8336–8341 (2019).
Bhardwaj, G. et al. Accurate de novo design of hyperstable constrained peptides. Nature 538, 329–335 (2016).
Hosseinzadeh, P. et al. Comprehensive computational design of ordered peptide macrocycles. Science 358, 1461–1466 (2017).
Leaver-Fay, A., Butterfoss, G. L., Snoeyink, J. & Kuhlman, B. Maintaining solvent accessible surface area under rotamer substitution for protein design. J. Comput. Chem. 28, 1336–1341 (2007).
Boyken, S. E. et al. De novo design of protein homo-oligomers with modular hydrogen-bond network-mediated specificity. Science 352, 680–687 (2016).
Lu, P. et al. Accurate computational design of multipass transmembrane proteins. Science 359, 1042–1046 (2018).
Chen, Z. et al. Programmable design of orthogonal protein heterodimers. Nature 565, 106–111 (2019).
Maguire, J. B., Boyken, S. E., Baker, D. & Kuhlman, B. Rapid sampling of hydrogen bond networks for computational protein design. J. Chem. Theory Comput. 14, 2751–2760 (2018).
Pavlovicz, R.E., Park, H. & DiMaio, F. Efficient consideration of coordinated water molecules improves computational protein-protein and protein-ligand docking. Preprint at bioRxiv https://doi.org/10.1101/618603 (2019).
Bhowmick, A., Sharma, S. C., Honma, H. & Head-Gordon, T. The role of side chain entropy and mutual information for improving the de novo design of Kemp eliminases KE07 and KE70. Phys. Chem. Chem. Phys. 18, 19386–19396 (2016).
König, R. & Dandekar, T. Solvent entropy-driven searching for protein modeling examined and tested in simplified models. Protein Eng. 14, 329–335 (2001).
Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K. & Moult, J. Critical assessment of methods of protein structure prediction (CASP)—round XIII. Proteins https://doi.org/10.1002/prot.25823 (2019).
Song, Y. et al. High-resolution comparative modeling with RosettaCM. Structure 21, 1735–1742 (2013).
Robetta. http://new.robetta.org/ (2020).
Park, H., Kim, D. E., Ovchinnikov, S., Baker, D. & DiMaio, F. Automatic structure prediction of oligomeric assemblies using Robetta in CASP12. Proteins 86(Suppl. 1), 283–291 (2018).
Kamisetty, H., Ovchinnikov, S. & Baker, D. Assessing the utility of coevolution-based residue-residue contact predictions in a sequence- and structure-rich era. Proc. Natl Acad. Sci. USA 110, 15674–15679 (2013).
Ovchinnikov, S. et al. Protein structure determination using metagenome sequence data. Science 355, 294–298 (2017).
Park, H., Ovchinnikov, S., Kim, D. E., DiMaio, F. & Baker, D. Protein homology model refinement by large-scale energy optimization. Proc. Natl Acad. Sci. USA 115, 3054–3059 (2018).
Tyka, M. D. et al. Alternate states of proteins revealed by detailed energy landscape mapping. J. Mol. Biol. 405, 607–618 (2011).
Friedland, G. D., Linares, A. J., Smith, C. A. & Kortemme, T. A simple model of backbone flexibility improves modeling of side-chain conformational variability. J. Mol. Biol. 380, 757–774 (2008).
Kapp, G. T. et al. Control of protein signaling using a computationally designed GTPase/GEF orthogonal pair. Proc. Natl Acad. Sci. USA 109, 5277–5282 (2012).
Stein, A. & Kortemme, T. Improvements to robotics-inspired conformational sampling in rosetta. PLoS One 8, e63090 (2013).
Lin, M. S. & Head-Gordon, T. Improved energy selection of nativelike protein loops from loop decoys. J. Chem. Theory Comput. 4, 515–521 (2008).
Rohl, C. A., Strauss, C. E. M., Chivian, D. & Baker, D. Modeling structurally variable regions in homologous proteins with rosetta. Proteins 55, 656–677 (2004).
Wang, C., Bradley, P. & Baker, D. Protein-protein docking with backbone flexibility. J. Mol. Biol. 373, 503–519 (2007).
Canutescu, A. A. & Dunbrack, R. L. Jr. Cyclic coordinate descent: a robotics algorithm for protein loop closure. Protein Sci. 12, 963–972 (2003).
Mandell, D. J., Coutsias, E. A. & Kortemme, T. Sub-angstrom accuracy in protein loop reconstruction by robotics-inspired conformational sampling. Nat. Methods 6, 551–552 (2009).
Mandell, D. J. & Kortemme, T. Backbone flexibility in computational protein design. Curr. Opin. Biotechnol. 20, 420–428 (2009).
Marze, N. A., Roy Burman, S. S., Sheffler, W. & Gray, J. J. Efficient flexible backbone protein-protein docking for challenging targets. Bioinformatics 34, 3461–3469 (2018).
Roy Burman, S. S., Yovanno, R. A. & Gray, J. J. Flexible backbone assembly and refinement of symmetrical homomeric complexes. Structure 27, 1041–1051.e8 (2019).
DiMaio, F., Leaver-Fay, A., Bradley, P., Baker, D. & André, I. Modeling symmetric macromolecular structures in Rosetta3. PLoS One 6, e20450 (2011).
Meiler, J. & Baker, D. ROSETTALIGAND: protein-small molecule docking with full side-chain flexibility. Proteins 65, 538–548 (2006).
Fu, D. Y. & Meiler, J. Predictive power of different types of experimental restraints in small molecule docking: a review. J. Chem. Inf. Model. 58, 225–233 (2018).
Fu, D. Y. & Meiler, J. RosettaLigandEnsemble: a small-molecule ensemble-driven docking approach. ACS Omega 3, 3655–3664 (2018).
Johnson, D. K. & Karanicolas, J. Druggable protein interaction sites are more predisposed to surface pocket formation than the rest of the protein surface. PLoS Comput. Biol. 9, e1002951 (2013).
Johnson, D. K. & Karanicolas, J. Selectivity by small-molecule inhibitors of protein interactions can be driven by protein surface fluctuations. PLoS Comput. Biol. 11, e1004081 (2015).
Johnson, D. K. & Karanicolas, J. Ultra-high-throughput structure-based virtual screening for small-molecule inhibitors of protein-protein interactions. J. Chem. Inf. Model. 56, 399–411 (2016).
Sircar, A., Kim, E. T. & Gray, J. J. RosettaAntibody: antibody variable region homology modeling server. Nucleic Acids Res. 37, W474–W479 (2009).
Weitzner, B. D., Kuroda, D., Marze, N., Xu, J. & Gray, J. J. Blind prediction performance of RosettaAntibody 3.0: grafting, relaxation, kinematic loop modeling, and full CDR optimization. Proteins 82, 1611–1623 (2014).
Weitzner, B. D. et al. Modeling and docking of antibody structures with Rosetta. Nat. Protoc. 12, 401–416 (2017).
Sivasubramanian, A., Sircar, A., Chaudhury, S. & Gray, J. J. Toward high-resolution homology modeling of antibody Fv regions and application to antibody-antigen docking. Proteins 74, 497–514 (2009).
Marze, N. A., Lyskov, S. & Gray, J. J. Improved prediction of antibody VL-VH orientation. Protein Eng. Des. Sel. 29, 409–418 (2016).
Finn, J. A. et al. Improving loop modeling of the antibody complementarity-determining region 3 using knowledge-based restraints. PLoS One 11, e0154811 (2016).
Weitzner, B. D. & Gray, J. J. Accurate structure prediction of CDR H3 loops enabled by a novel structure-based C-terminal constraint. J. Immunol. 198, 505–515 (2017).
DeKosky, B. J. et al. Large-scale sequence and structural comparisons of human naive and antigen-experienced antibody repertoires. Proc. Natl Acad. Sci. USA 113, E2636–E2645 (2016).
Jeliazkov, J. R. et al. Repertoire analysis of antibody CDR-H3 loops suggests affinity maturation does not typically result in rigidification. Front. Immunol. 9, 413 (2018).
Norn, C. H., Lapidoth, G. & Fleishman, S. J. High-accuracy modeling of antibody structures by a search for minimum-energy recombination of backbone fragments. Proteins 85, 30–38 (2017).
Lapidoth, G., Parker, J., Prilusky, J. & Fleishman, S. J. AbPredict 2: a server for accurate and unstrained structure prediction of antibody variable domains. Bioinformatics 35, 1591–1593 (2019).
Sircar, A. & Gray, J. J. SnugDock: paratope structural optimization during antibody-antigen docking compensates for errors in antibody homology models. PLoS Comput. Biol. 6, e1000644 (2010).
Sircar, A., Sanni, K. A., Shi, J. & Gray, J. J. Analysis and modeling of the variable region of camelid single-domain antibodies. J. Immunol. 186, 6357–6367 (2011).
Adolf-Bryfogle, J. et al. RosettaAntibodyDesign (RAbD): a general framework for computational antibody design. PLoS Comput. Biol. 14, e1006112 (2018).
North, B., Lehmann, A. & Dunbrack, R. L. Jr. A new clustering of antibody CDR loop conformations. J. Mol. Biol. 406, 228–256 (2011).
King, C. et al. Removing T-cell epitopes with computational protein design. Proc. Natl Acad. Sci. USA 111, 8577–8582 (2014).
Nivón, L. G., Bjelic, S., King, C. & Baker, D. Automating human intuition for protein design. Proteins 82, 858–866 (2014).
Lapidoth, G. D. et al. AbDesign: an algorithm for combinatorial backbone design guided by natural conformations and sequences. Proteins 83, 1385–1406 (2015).
Baran, D. et al. Principles for computational design of binding antibodies. Proc. Natl Acad. Sci. USA 114, 10900–10905 (2017).
Vaissier Welborn, V. & Head-Gordon, T. Computational design of synthetic enzymes. Chem. Rev. 119, 6613–6630 (2019).
Marcos, E. & Silva, D.-A. Essentials of de novo protein design: methods and applications. Wiley Interdiscip. Rev. Comput. Mol. Sci. 8, e1374 (2018).
Marcos, E. et al. Principles for designing proteins with cavities formed by curved β sheets. Science 355, 201–206 (2017).
Zhou, J., Panaitiu, A. E. & Grigoryan, G. A general-purpose protein design framework based on mining sequence-structure relationships in known protein structures. Proc. Natl Acad. Sci. USA 117, 1059–1068 (2020).
Jacobs, T. M. et al. Design of structurally distinct proteins using strategies inspired by evolution. Science 352, 687–690 (2016).
Guffy, S. L., Teets, F. D., Langlois, M. I. & Kuhlman, B. Protocols for requirement-driven protein design in the Rosetta modeling program. J. Chem. Inf. Model. 58, 895–901 (2018).
Lapidoth, G. et al. Highly active enzymes by automated combinatorial backbone assembly and sequence design. Nat. Commun. 9, 2780 (2018).
Huang, P.-S. et al. RosettaRemodel: a generalized framework for flexible backbone protein design. PLoS One 6, e24109 (2011).
Leaver-Fay, A., Jacak, R., Stranges, P. B. & Kuhlman, B. A generic program for multistate protein design. PLoS One 6, e20937 (2011).
Sevy, A. M., Jacobs, T. M., Crowe, J. E. Jr. & Meiler, J. Design of protein multi-specificity using an independent sequence search reduces the barrier to low energy sequences. PLoS Comput. Biol. 11, e1004300 (2015).
Sevy, A. M. et al. Multistate design of influenza antibodies improves affinity and breadth against seasonal viruses. Proc. Natl Acad. Sci. USA 116, 1597–1602 (2019).
Sauer, M. F., Sevy, A. M., Crowe, J. E. & Meiler, J. Multi-state design of flexible proteins predicts sequences optimal for conformational change. PLoS Comput. Biol. 16, e1007339 (2020).
Correia, B. E. et al. Proof of principle for epitope-focused vaccine design. Nature 507, 201–206 (2014).
Bonet, J. et al. Rosetta FunFolDes — a general framework for the computational design of functional proteins. PLoS Comput. Biol. 14, e1006623 (2018).
Kroncke, B. M. et al. Documentation of an imperative to improve methods for predicting membrane protein stability. Biochemistry 55, 5002–5009 (2016).
Kortemme, T. & Baker, D. A simple physical model for binding energy hot spots in protein-protein complexes. Proc. Natl Acad. Sci. USA 99, 14116–14121 (2002).
Kortemme, T., Kim, D. E. & Baker, D. Computational alanine scanning of protein-protein interfaces. Sci. STKE 2004, pl2 (2004).
Conchúir, Ó. et al. Web resource for standardized benchmark datasets, metrics, and Rosetta protocols for macromolecular modeling and design. PLoS One 10, e0130433 (2015).
Barlow, K. A. et al. Flex ddG: Rosetta ensemble-based estimation of changes in protein-protein binding affinity upon mutation. J. Phys. Chem. B 122, 5389–5399 (2018).
Smith, C. A. & Kortemme, T. Backrub-like backbone simulation recapitulates natural protein conformational variability and improves mutant side-chain prediction. J. Mol. Biol. 380, 742–756 (2008).
Crick, F. H. C. The Fourier transform of a coiled-coil. Acta Crystallogr. 6, 685–689 (1953).
Dang, B. et al. De novo design of covalently constrained mesosize protein scaffolds with unique tertiary structures. Proc. Natl Acad. Sci. USA 114, 10852–10857 (2017).
Alam, N. et al. High-resolution global peptide-protein docking using fragments-based PIPER-FlexPepDock. PLoS Comput. Biol. 13, e1005905 (2017).
Kozakov, D., Brenke, R., Comeau, S. R. & Vajda, S. PIPER: an FFT-based protein docking program with pairwise potentials. Proteins 65, 392–406 (2006).
Raveh, B., London, N. & Schueler-Furman, O. Sub-angstrom modeling of complexes between flexible peptides and globular proteins. Proteins 78, 2029–2040 (2010).
Pacella, M. S., Koo, C. E., Thottungal, R. A. & Gray, J. J. Using the RosettaSurface algorithm to predict protein structure at mineral surfaces. Methods Enzymol. 532, 343–366 (2013).
Lubin, J. H., Pacella, M. S. & Gray, J. J. A parametric Rosetta energy function analysis with LK peptides on SAM surfaces. Langmuir 34, 5279–5289 (2018).
Frenz, B., Walls, A. C., Egelman, E. H., Veesler, D. & DiMaio, F. RosettaES: a sampling strategy enabling automated interpretation of difficult cryo-EM maps. Nat. Methods 14, 797–800 (2017).
Wang, R. Y.-R. et al. Automated structure refinement of macromolecular assemblies from cryo-EM maps using Rosetta. eLife 5, e17219 (2016).
Labonte, J. W., Adolf-Bryfogle, J., Schief, W. R. & Gray, J. J. Residue-centric modeling and design of saccharide and glycoconjugate structures. J. Comput. Chem. 38, 276–287 (2017).
Frenz, B. et al. Automatically fixing errors in glycoprotein structures with Rosetta. Structure 27, 134–139.e3 (2019).
Nerli, S. & Sgourakis, N. G. CS-ROSETTA. Methods Enzymol. 614, 321–362 (2019).
Rohl, C. A. & Baker, D. De novo determination of protein backbone structure from residual dipolar couplings using Rosetta. J. Am. Chem. Soc. 124, 2723–2729 (2002).
Yagi, H. et al. Three-dimensional protein fold determination from backbone amide pseudocontact shifts generated by lanthanide tags at multiple sites. Structure 21, 883–890 (2013).
Schmitz, C., Vernon, R., Otting, G., Baker, D. & Huber, T. Protein structure determination from pseudocontact shifts using ROSETTA. J. Mol. Biol. 416, 668–677 (2012).
Pilla, K. B., Otting, G. & Huber, T. Pseudocontact shift-driven iterative resampling for 3d structure determinations of large proteins. J. Mol. Biol. 428, 522–532 (2016). 2 Pt B.
Lange, O. F. & Baker, D. Resolution-adapted recombination of structural features significantly improves sampling in restraint-guided structure calculation. Proteins 80, 884–895 (2012).
Bowers, P. M., Strauss, C. E. M. & Baker, D. De novo protein structure determination using sparse NMR data. J. Biomol. NMR 18, 311–318 (2000).
Meiler, J. & Baker, D. Rapid protein fold determination using unassigned NMR data. Proc. Natl Acad. Sci. USA 100, 15404–15409 (2003).
Raman, S. et al. NMR structure determination for larger proteins using backbone-only data. Science 327, 1014–1018 (2010).
Lange, O. F. et al. Determination of solution structures of proteins up to 40 kDa using CS-Rosetta with sparse NMR data from deuterated samples. Proc. Natl Acad. Sci. USA 109, 10873–10878 (2012).
Reichel, K. et al. Systematic evaluation of CS-Rosetta for membrane protein structure prediction with sparse NOE restraints. Proteins 85, 812–826 (2017).
Sgourakis, N. G. et al. Determination of the structures of symmetric protein oligomers from NMR chemical shifts and residual dipolar couplings. J. Am. Chem. Soc. 133, 6288–6298 (2011).
Rossi, P. et al. A hybrid NMR/SAXS-based approach for discriminating oligomeric protein interfaces using Rosetta. Proteins 83, 309–317 (2015).
Demers, J.-P. et al. High-resolution structure of the Shigella type-III secretion needle by solid-state NMR and cryo-electron microscopy. Nat. Commun. 5, 4976 (2014).
Thompson, J. M. et al. Accurate protein structure modeling using sparse NMR data and homologous structure information. Proc. Natl Acad. Sci. USA 109, 9875–9880 (2012).
Braun, T., Koehler Leman, J. & Lange, O. F. Combining evolutionary information and an iterative sampling strategy for accurate protein structure prediction. PLoS Comput. Biol. 11, e1004661 (2015).
Evangelidis, T. et al. Automated NMR resonance assignments and structure determination using a minimal set of 4D spectra. Nat. Commun. 9, 384 (2018).
Lange, O. F. Automatic NOESY assignment in CS-RASREC-Rosetta. J. Biomol. NMR 59, 147–159 (2014).
Kuenze, G., Bonneau, R., Koehler Leman, J. & Meiler, J. Integrative protein modeling in RosettaNMR from sparse paramagnetic restraints. Structure 27, 1721–1734.e5 (2019).
Aprahamian, M. L., Chea, E. E., Jones, L. M. & Lindert, S. Rosetta protein structure prediction from hydroxyl radical protein footprinting mass spectrometry data. Anal. Chem. 90, 7721–7729 (2018).
Aprahamian, M. L. & Lindert, S. Utility of covalent labeling mass spectrometry data in protein structure prediction with Rosetta. J. Chem. Theory Comput. https://doi.org/10.1021/acs.jctc.9b00101 (2019).
Hauri, S. et al. Rapid determination of quaternary protein structures in complex biological samples. Nat. Commun. 10, 192 (2019).
Watkins, A. M. et al. Blind prediction of noncanonical RNA structure at atomic accuracy. Sci. Adv. 4, eaar5316 (2018).
Sripakdeevong, P., Kladwang, W. & Das, R. An enumerative stepwise ansatz enables atomic-accuracy RNA loop modeling. Proc. Natl Acad. Sci. USA 108, 20573–20578 (2011).
Das, R. Atomic-accuracy prediction of protein loop structures through an RNA-inspired Ansatz. PLoS One 8, e74830 (2013).
Chou, F.-C., Sripakdeevong, P., Dibrov, S. M., Hermann, T. & Das, R. Correcting pervasive errors in RNA crystallography through enumerative structure prediction. Nat. Methods 10, 74–76 (2013).
Chou, F.-C., Echols, N., Terwilliger, T. C. & Das, R. RNA structure refinement using the ERRASER-Phenix pipeline. in Nucleic Acid Crystallography 269–282 (Springer, 2016); https://doi.org/10.1007/978-1-4939-2763-0_17
Kappel, K. & Das, R. Sampling native-like structures of RNA-protein complexes through Rosetta folding and docking. Structure 27, 140–151.e5 (2019).
Kappel, K. et al. De novo computational RNA modeling into cryo-EM maps of large ribonucleoprotein complexes. Nat. Methods 15, 947–954 (2018).
Thyme, S. B. et al. Exploitation of binding energy for catalysis and design. Nature 461, 1300–1304 (2009).
Ashworth, J. et al. Computational redesign of endonuclease DNA binding and cleavage specificity. Nature 441, 656–659 (2006).
Ashworth, J. et al. Computational reprogramming of homing endonuclease specificity at multiple adjacent base pairs. Nucleic Acids Res. 38, 5601–5608 (2010).
Havranek, J. J. & Harbury, P. B. Automated design of specificity in molecular recognition. Nat. Struct. Biol. 10, 45–52 (2003).
Thyme, S. B. et al. Reprogramming homing endonuclease specificity through computational design and directed evolution. Nucleic Acids Res. 42, 2564–2576 (2014).
Thyme, S. B., Baker, D. & Bradley, P. Improved modeling of side-chain—base interactions and plasticity in protein—DNA interface design. J. Mol. Biol. 419, 255–274 (2012).
Yanover, C. & Bradley, P. Extensive protein and DNA backbone sampling improves structure-based specificity prediction for C2H2 zinc fingers. Nucleic Acids Res. 39, 4564–4576 (2011).
Ashworth, J. & Baker, D. Assessment of the optimization of affinity and specificity at protein-DNA interfaces. Nucleic Acids Res. 37, e73 (2009).
Thyme, S. B. et al. Massively parallel determination and modeling of endonuclease substrate specificity. Nucleic Acids Res. 42, 13839–13852 (2014).
Overington, J. P., Al-Lazikani, B. & Hopkins, A. L. How many drug targets are there? Nat. Rev. Drug Discov. 5, 993–996 (2006).
Koehler Leman, J., Ulmschneider, M. B. & Gray, J. J. Computational modeling of membrane proteins. Proteins 83, 1–24 (2015).
Yarov-Yarovoy, V., Schonbrun, J. & Baker, D. Multipass membrane protein structure prediction using Rosetta. Proteins 62, 1010–1025 (2006).
Barth, P., Schonbrun, J. & Baker, D. Toward high-resolution prediction and design of transmembrane helical protein structures. Proc. Natl Acad. Sci. USA 104, 15682–15687 (2007).
Alford, R. F. et al. An integrated framework advancing membrane protein modeling and design. PLoS Comput. Biol. 11, e1004398 (2015).
Baugh, E. H., Lyskov, S., Weitzner, B. D. & Gray, J. J. Real-time PyMOL visualization for Rosetta and PyRosetta. PLoS One 6, e21931 (2011).
Koehler Leman, J., Mueller, B. K. & Gray, J. J. Expanding the toolkit for membrane protein modeling in Rosetta. Bioinformatics 33, 754–756 (2017).
Koehler Leman, J., Lyskov, S. & Bonneau, R. Computing structure-based lipid accessibility of membrane proteins with mp_lipid_acc in RosettaMP. BMC Bioinformatics 18, 115 (2017).
Koehler Leman, J. & Bonneau, R. A novel domain assembly routine for creating full-length models of membrane proteins from known domain structures. Biochemistry https://doi.org/10.1021/acs.biochem.7b00995 (2017).
Lai, J. K., Ambia, J., Wang, Y. & Barth, P. Enhancing structure prediction and design of soluble and membrane proteins with explicit solvent-protein interactions. Structure 25, 1758–1770.e8 (2017).
Alford, R. F., Fleming, P. J., Fleming, K. G. & Gray, J. J. Protein structure prediction and design in a biologically realistic implicit membrane. Biophys. J. 118, 2042–2055 (2020).
Varki, A. Biological roles of oligosaccharides: all of the theories are correct. Glycobiology 3, 97–130 (1993).
Varki, A. et al. Essentials of Glycobiology (Cold Spring Harbor Laboratory Press, 2009).
Nivedha, A. K., Thieker, D. F., Makeneni, S., Hu, H. & Woods, R. J. Vina-Carb: improving glycosidic angles during carbohydrate docking. J. Chem. Theory Comput. 12, 892–901 (2016).
Gray, J. J., Chaudhury, S., Lyskov, S. & Labonte, J. W. The PyRosetta interactive platform for protein structure prediction and design: a set of educational modules. (CreateSpace, 2014).
Schenkelberg, C. D. & Bystroff, C. InteractiveROSETTA: a graphical user interface for the PyRosetta protein modeling suite. Bioinformatics 31, 4023–4025 (2015).
Kleffner, R. et al. Foldit Standalone: a video game-derived protein structure manipulation interface using Rosetta. Bioinformatics 33, 2765–2767 (2017).
Cooper, S., Sterling, A. L. R., Kleffner, R., Silversmith, W. M. & Siegel, J. B. Repurposing citizen science games as software tools for professional scientists. in Proc. 13th Int. Conf. Foundations of Digital Games – FDG ’18 https://doi.org/10.1145/3235765.3235770 (ACM Press, 2018).
Lyskov, S. et al. Serverification of molecular modeling applications: the Rosetta Online Server that Includes Everyone (ROSIE). PLoS One 8, e63906 (2013).
Moretti, R., Lyskov, S., Das, R., Meiler, J. & Gray, J. J. Web-accessible molecular modeling with Rosetta: the Rosetta Online Server that Includes Everyone (ROSIE). Protein Sci. 27, 259–268 (2018).
Institute for Protein Design. Audacious Project. https://www.ipd.uw.edu/audacious/ (2019).
Mulligan, V.K. et al. Designing peptides on a quantum computer. Preprint at bioRxiv https://doi.org/10.1101/752485 (2019).
Gront, D., Kulp, D. W., Vernon, R. M., Strauss, C. E. M. & Baker, D. Generalized fragment picking in Rosetta: design, protocols and applications. PLoS One 6, e23294 (2011).
Marcos, E. et al. De novo design of a non-local β-sheet protein with high stability and accuracy. Nat. Struct. Mol. Biol. 25, 1028–1034 (2018).
DeLuca, S., Khar, K. & Meiler, J. Fully flexible docking of medium sized ligand libraries with RosettaLigand. PLoS One 10, e0132508 (2015).
Davis, I. W. & Baker, D. RosettaLigand docking with full ligand and receptor flexibility. J. Mol. Biol. 385, 381–392 (2009).
Gowthaman, R. et al. DARC: mapping surface topography by ray-casting for effective virtual screening at protein interaction sites. J. Med. Chem. 59, 4152–4170 (2016).
Khar, K. R., Goldschmidt, L. & Karanicolas, J. Fast docking on graphics processing units via Ray-Casting. PLoS One 8, e70661 (2013).
Gowthaman, R., Lyskov, S. & Karanicolas, J. DARC 2.0: improved docking and virtual screening at protein interaction sites. PLoS One 10, e0131612 (2015).
Toor, J. S. et al. A recurrent mutation in anaplastic lymphoma kinase with distinct neoepitope conformations. Front. Immunol. 9, 99 (2018).
Gowthaman, R. & Pierce, B. G. TCRmodel: high resolution modeling of T cell receptors from sequence. Nucleic Acids Res. 46 W1, W396–W401 (2018).
Blacklock, K. M., Yang, L., Mulligan, V. K. & Khare, S. D. A computational method for the design of nested proteins by loop-directed domain insertion. Proteins 86, 354–369 (2018).
Ollikainen, N., de Jong, R. M. & Kortemme, T. Coupling protein side-chain and backbone flexibility improves the re-design of protein-ligand specificity. PLoS Comput. Biol. 11, e1004335 (2015).
Raveh, B., London, N., Zimmerman, L. & Schueler-Furman, O. Rosetta FlexPepDock ab-initio: simultaneous folding, docking and refinement of peptides onto their receptors. PLoS One 6, e18934 (2011).
Sedan, Y., Marcu, O., Lyskov, S. & Schueler-Furman, O. Peptiderive server: derive peptide inhibitors from protein-protein interactions. Nucleic Acids Res. 44 W1, W536–W541 (2016).
Rubenstein, A. B., Pethe, M. A. & Khare, S. D. MFPred: rapid and accurate prediction of protein-peptide recognition multispecificity using self-consistent mean field theory. PLoS Comput. Biol. 13, e1005614 (2017).
Pacella, M. S. & Gray, J. J. A benchmarking study of peptide–biomineral interactions. Cryst. Growth Des. 18, 607–616 (2018).
Wang, R. Y.-R. et al. De novo protein structure determination from near-atomic-resolution cryo-EM maps. Nat. Methods 12, 335–338 (2015).
DiMaio, F. et al. Improved low-resolution crystallographic refinement with Phenix and Rosetta. Nat. Methods 10, 1102–1104 (2013).
DiMaio, F. et al. Atomic-accuracy models from 4.5-Å cryo-electron microscopy data with density-guided iterative local refinement. Nat. Methods 12, 361–365 (2015).
Das, R., Karanicolas, J. & Baker, D. Atomic accuracy in predicting and designing noncanonical RNA structure. Nat. Methods 7, 291–294 (2010).
Cheng, C. Y., Chou, F.-C. & Das, R. Modeling complex RNA tertiary folds with Rosetta. Methods Enzymol. 553, 35–64 (2015).
Sripakdeevong, P. et al. Structure determination of noncanonical RNA motifs guided by 1H NMR chemical shifts. Nat. Methods 11, 413–416 (2014).
Chou, F. C., Kladwang, W., Kappel, K. & Das, R. Blind tests of RNA nearest-neighbor energy prediction. Proc. Natl Acad. Sci. USA 113, 8430–8435 (2016).
Ford, A. S., Weitzner, B. D. & Bahl, C. D. Integration of the Rosetta suite with the python software stack via reproducible packaging and core programming interfaces for distributed simulation. Protein Sci. 29, 43–51 (2020).
Khatib, F. et al. Algorithm discovery by protein folding game players. Proc. Natl Acad. Sci. USA 108, 18949–18953 (2011).
Hooper, W. F., Walcott, B. D., Wang, X. & Bystroff, C. Fast design of arbitrary length loops in proteins using InteractiveRosetta. BMC Bioinformatics 19, 337 (2018).
RosettaCommons is supported by NIH R01 GM073151 to B. Kuhlman, NSF, the Packard Foundation, the Beckman Foundation, the Alfred P. Sloan Foundation and the Simons Foundation. This work was also supported by a 100,000,000 CPU-hour donation from Google Inc to P.C. and a 125,760,000 CPU-hour allocation on the Mira and Theta supercomputers through the Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program to D.B., F.D., A.L.-F. and V.K.M. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science user facility supported under Contract DE-AC02-06CH11357. Supported by AHA 18POST34080422 to G.K., AMED J-PRIDE JP18fm0208022h to D.K., the Biltema Foundation to B.E.C. and Boehringer Ingelheim Fonds to C.N.; computing was performed using resources of the Argonne Leadership Computing Facility at Argonne National Laboratory, which is supported by the Office of Science of the United States to P.C.; DFG KU 3510/1-1 to G.K.; DP120100561 to T.H.; DP150100383 to T.H. and K.B.P.; EMBO long-term fellowship ALTF 698-2011 to A. Stein; EPFL-Fellows H2020 Marie Sklodowska-Curie to J.B.; European Research Council Grant 310873 to O.S.-F. and N.A.; European Research Council Grant 310873 to Y. Sedan and O.M.; European Research Council Starting grant 716058 to B.E.C. and A. Scheck; FT0991709 to T.H.; Foundation of Knut and Alice Wallenberg 20160023 to L.M.; a Hertz Foundation Fellowship to R.F.A.; the Howard Hughes Medical Institute to D.B.; Hyak supercomputer system supported in part by the University of Washington eScience Institute to the D.B. and F.D. labs; Israel Science Foundation 2017717 to O.S.-F. and N.A.; Japan Society for the Promotion of Science JP17K18113 to D.K.; MCB1330760 to S.D.K.; Marie Curie International Outgoing Fellowship FP7-PEOPLE-2011-IOF 298976 to E.M.; National Science Centre, Poland, 2018/29/B/ST6/01989 to D.G.; NIAID T32AI007244 to J.A.-B.; NIAID U19 AI117905 to A.M.S.; NIEHS P42ES004699 to J.B.S.; NIGMS Ruth L Kirschstein National Research Service Award T32GM008268 to P.C.; NIGMS T32 GM007628 to B.J.B.; NIH 1R35 GM122579 to R. Das; NSF DMREF award 1728858 and DMR-0820341 to R.B.; NIH 1UH2CA203780 to S.C. and F.K.; NIH 5F32GM110899-02 to T.L.; NIH F31GM123616 to J.R.J.; NIH F32CA189246 to J.W.L.; NIH P01 U19AI117905, R01 AI113867 and UM1 Al100663 to W.S.; NIH R00 GM120388 to S.H.; NIH R01 AI143997 to N.G.S.; NIH R01 DK097376, R01 GM080403, R01 HL122010 and R01 GM099842 to J. Meiler; NIH R01 GM073960, R01 GM117968 and R01067553 to B. Kuhlman; NIH R01 GM076324 to J.B.S.; NIH R01 GM127578 and R01 GM078221 to J.J.G.; NIH R01 GM084453 to R. Dunbrack; NIH R01 GM088277 and R01 GM121487 to P. Bradley; NIH R01 GM092802, R01 GM092802, R01084433 and GM092802 to D.B.; NIH R01 GM098101, R01 GM110089 and R01 GM117189 to T.K.; NIH R01 GM099959 to J.K.; NIH R01 GM123089 to F.D.; NIH R01 GM126299 to B.G.P.; NIH R01 GM099827 to C.B.; NIH R01088277 to S.B.T.; NIH R21 AI121799 to J. Meiler; NIH R21 CA219847 and R21 GM102716 to R. Das; NIH R35 GM122517 to R. Dunbrack; NIH R35 GM125034 to N.G.S.; NIH RL1CA133832 to D.B.; NIH U19 AI117905 to J. Meiler; NIH/NCI Cancer Center support grant P30 CA006927 to J.K.; NSF 1507736 and NSF DMR 1507736 to J.J.G.; NSF 1627539, 1805510 and 1827246 to J.B.S.; NSF 1629879 to S.C.; NSF CHE 1305874, CISE 1629811 and CNS-1629811 to J. Meiler; NSF CHE 1750666 to S. Lindert; NSF DBI-1262182 and DBI-1564692 to T.K.; NSF GRF DGE-1433187 to A.R.; NSF Graduate Research Fellowships to R.F.A., K.K., B. Koepnick and S.B.T.; NSF MCB1330760 and MCB1716623 to S.D.K.; Open Philanthropy to B.C.; PhRMA Informatics Pre-Doctoral Fellowship U22879-001 to S.S.; a PhRMA Foundation Predoctoral Fellowship to D.Y.F.; RosettaCommons to L.G., A.R., F.D., S.C., A.W., M.S., C.G., K.B., R. Das, S.D.K., J. Koehler Leman and K.K.; Career Award at the Scientific Interface from Burroughs Wellcome Fund to S.E.B.; Simons Foundation to V.K.M., R.B., P.D.R. and J. Koehler Leman; a Stanford Graduate Fellowship to K.K.; a Starter Grant from the European Research Council to G.L.; Swiss National Science Foundation – NCCR Molecular Systems Engineering 51NF40-141825 to B.E.C.; Swiss National Science Foundation 310030_163139 to B.E.C.; Swiss National Science Foundation SNF 200021 160188 to L.M. and H.K.; UCSF/UCB Graduate Program in Bioengineering to X.P.; USA-Israel Binational Science Foundation 2009418 to B.R., L.Z. and N.L.; USA-Israel Binational Science Foundation 2009418 and 2015207 to O.S.-F. and N.A.; USA-Israel Binational Science Foundation 2015207 to A.K.; Washington Research Foundation Innovation Postdoctoral Fellowship to B.D.W.; XSEDE, which is supported by NSF ACI-1548562; NIH R01 GM097207 to P. Barth; and the MCB120101 XSEDE allocation to P. Barth. The authors would like to thank Jason C. Klima for his work on PyRosetta.
Rosetta software has been licensed to numerous non-profit and for-profit organizations. Rosetta Licensing is managed by UW CoMotion, and royalty proceeds are managed by the RosettaCommons. Under institutional participation agreements between the University of Washington, acting on behalf of the RosettaCommons, their respective institutions may be entitled to a portion of revenue received on licensing Rosetta software including programs described here. D.B., L.M., D.G., J.M., O.S.-F., J.J.G., N.G.S., S.L., J.K., R.B., T.K. and P.B. are unpaid board members of the RosettaCommons. As members of the Scientific Advisory Board of Cyrus Biotechnology, D.B. and J.J.G. are granted stock options. Y.S., I.C.K., S.M.L., B.F., K.R.K. and R.E.P. are employed at Cyrus Biotechnology with granted stock options. Cyrus Biotechnology distributes the Rosetta software. B.D.W. and S.E.B. hold equity in Lyell Immunopharma. V.K.M. is a cofounder of and shareholder in Menten Biotechnology Labs, Inc. The content of this manuscript is relevant to work performed at Lyell and Menten. J.B.S. is a cofounder and shareholder of Digestiva, Inc. and PvP Biologics Inc. D.B. is a cofounder of, shareholder in, or advisor to the following companies: ARZEDA, PvP Biologics, Cyrus Biotechnology, Cue Biopharma, Icosavax, Neoleukin Therapeutics, Lyell Immunotherapeutics, Sana Biotechnology and A-Alpha Bio.
Peer review information Allison Doerr was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
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Leman, J.K., Weitzner, B.D., Lewis, S.M. et al. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat Methods 17, 665–680 (2020). https://doi.org/10.1038/s41592-020-0848-2
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