Macromolecular modeling and design in Rosetta: recent methods and frameworks | Nature Methods
-
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).
-
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).