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Macromolecular Modeling and Design in Rosetta: New Methods and Frameworks

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Macromolecular Modeling and Design in Rosetta: New Methods and Frameworks Supplementary information for: Macromolecular modeling and design in Rosetta: new methods and frameworks Rosetta is licensed and distributed through www.rosettacommons.org. Licenses for academic, non-profit and government laboratories are free of charge, there is a license fee for industry users. The main documentation page can be found at https://www.rosettacommons.org/docs/latest/Home. Tutorials, demos and protocol captures are documented at https://www.rosettacommons.org/demos/latest/Home. RosettaScripts documentation is available at https://www.rosettacommons.org/docs/latest/scripting_documentation/RosettaScripts/RosettaScripts. PyRosetta tutorials are available at http://www.pyrosetta.org/tutorials. Method Use Developer(s) Lab developed Documentation Protocol capture / demo Limitations Competing methods Protein structure prediction fragment picker1 picks protein Dominik Gront Dominik Gront*,** https://www.rosettacommons.org/docs/latest/applicati Rosetta/demos/public/fragment_picking fragments for on_documentation/utilities/app-fragment-picker various modeling tasks RosettaCM2 comparative Yifan Song formerly David https://www.rosettacommons.org/docs/latest/applicati Rosetta/demos/public/homology_modeling_threa model quality depends on manual MODELLER3, iTasser4, modeling from Baker on_documentation/structure_prediction/RosettaCM ding_basic adjustment of sequence alignment, which HHpred5 multiple templates Supp to 2 can take time to do well iterative recombination of Sergey David Baker, https://www.rosettacommons.org/docs/latest/Iterative hybridize6,7 model Ovchinnikov, Sergey Hybridize substructures for Hahnbeom Park Ovchinnikov de novo modeling Loop modeling MODELLER8 NGK (next- Next Generation Amelie Stein Tanja Kortemme https://www.rosettacommons.org/docs/latest/applicati Rosetta/demos/tutorials/loop_modeling performance deteriorates for loops longer Sphinx10, DiSGro11 generation KIC) 9 Kinematic loop on_documentation/structure_prediction/loop_modelin than 12 residues, as all loop modeling closure g/next-generation-KIC methods do https://www.rosettacommons.org/docs/latest/applicati on_documentation/structure_prediction/loop_modelin g/loopmodel-kinematic (for -vicinity_sampling) LoopHashKIC uses a loop Xingjie Pan Tanja Kortemme https://www.rosettacommons.org/docs/latest/scripting database and KIC _documentation/RosettaScripts/Movers/movers_page for loop design s/LoopModelerMover Consensus_Loop_ uses sequence Enrique Marcos, David Baker https://www.rosettacommons.org/docs/latest/scripting Rosetta/main/tests/integration/tests/ConsensusL LOOPY14, PLOP15, PS116, Design12,13 profiles of known Tom Linsky _documentation/RosettaScripts/TaskOperations/tasko oopDesign PS217 loops to design perations_pages/ConsensusLoopDesignOperation new ones GenKIC Generalized Vikram Mulligan Richard Bonneau* https://www.rosettacommons.org/docs/latest/scripting https://www.rosettacommons.org/demos/latest/tu generality requires more setup and can be molecular dynamics (generalized KIC) KInematic loop _documentation/RosettaScripts/composite_protocols/ torials/GeneralizedKIC/generalized_kinematic_cl less efficient than more biased methods; simulation packages 18 Closure: robotics generalized_kic/GeneralizedKIC osure_1, parameter tuning can require some based approach to https://www.rosettacommons.org/docs/latest/scripting https://www.rosettacommons.org/demos/latest/tu experience sample non- _documentation/RosettaScripts/Movers/movers_page torials/GeneralizedKIC/generalized_kinematic_cl canonical loop s/GeneralizedKICMover osure_2, conformations https://www.rosettacommons.org/docs/latest/scripting https://www.rosettacommons.org/demos/latest/tu _documentation/RosettaScripts/composite_protocols/ torials/GeneralizedKIC/generalized_kinematic_cl generalized_kic/GeneralizedKICperturber osure_3, https://www.rosettacommons.org/docs/latest/scripting https://www.rosettacommons.org/demos/latest/tu _documentation/RosettaScripts/composite_protocols/ torials/GeneralizedKIC/generalized_kinematic_cl generalized_kic/GeneralizedKICfilter osure_4 https://www.rosettacommons.org/docs/latest/scripting Supp to 18,19 _documentation/RosettaScripts/composite_protocols/ generalized_kic/GeneralizedKICselector 1 Method Use Developer(s) Lab developed Documentation Protocol capture / demo Limitations Competing methods https://www.rosettacommons.org/demos/latest/tutorial s/GeneralizedKIC/generalized_kinematic_closure_1 https://www.rosettacommons.org/demos/latest/tutorial s/GeneralizedKIC/generalized_kinematic_closure_2 https://www.rosettacommons.org/demos/latest/tutorial s/GeneralizedKIC/generalized_kinematic_closure_3 https://www.rosettacommons.org/demos/latest/tutorial s/GeneralizedKIC/generalized_kinematic_closure_4 Protein-protein docking RosettaDock4.020 protein-protein Nick Marze, Jeffrey Gray https://www.rosettacommons.org/docs/latest/applicati Rosetta/demos/tutorials/Protein-Protein-Docking global docking requires two orders or InterEVDock21, ClusPro22, docking Shourya Roy on_documentation/docking/docking-protocol https://www.rosettacommons.org/demos/latest/tu magnitude more sampling than local GalaxyTongDock23, Burman torials/Protein-Protein-Docking/Protein-Protein- docking – in these cases RosettaDock is HADDOCK24, PPI3D25 Docking slower than FFT-based methods; like for other tools, large conformational changes make docking difficult Rosetta docking of Shourya Roy (formerly Ingemar https://www.rosettacommons.org/docs/latest/SymDoc SymDock is sensitive to quality of input GalaxyTongDock23, SymDock226 symmetric proteins Burman André), Jeffrey kProtocol monomer; as with other algorithms, SAM27, HSYMDOCK28 Gray performance deteriorates for higher-order symmetries Small molecule ligand docking RosettaLigand29–31 small molecule Sam DeLuca, Jens Meiler https://www.rosettacommons.org/demos/latest/tutorial Rosetta/demos/tutorials/ligand_docking not as fully a dynamic approach as MD AutoDock Vina32, docking to proteins Darwin Fu, s/ligand_docking/ligand_docking_tutorial simulations; performs worse in more GlideScore33 Shannon Smith, solvent-exposed pockets or small (Schrodinger), Rocco Moretti pockets; requires ligand conformers to be DrugScore34, Molecular pre-generated; assumes ligand binding Operating Environment site is known (MOE)35 from Chemical Computing Group, GOLD36 RosettaLigandEns docks ligand Darwin Fu, Jens Meiler https://www.rosettacommons.org/docs/latest/scripting Rosetta/demos/protocol_capture/rosettaligand_e same limitations as RosettaLigand; HybridDock38 emble37 ensembles into Rocco Moretti _documentation/RosettaScripts/Movers/movers_page nsemble assumes ligands in ensemble follow similar proteins s/HighResEnsembleMover binding modes pocket samples binding David Johnson John Karanicolas https://www.rosettacommons.org/docs/latest/applicati Supp to 39,40 competitors use small optimization39,40 pocket on_documentation/utilities/pocket-relax probe molecules conformations https://www.rosettacommons.org/docs/latest/applicati (FTMap41) or molecular during docking on_documentation/analysis/pocket-measure dynamics42 DARC43–45 Docking Approach Ragul Gowthaman, John Karanicolas https://www.rosettacommons.org/docs/latest/applicati Rosetta/demos/public/darc there are numerous using Ray Casting: Karen Khar on_documentation/docking/DARC Supp to 43–45 approaches (f.ex. identifies binding DOCKTITE46 in MOE) for pockets small-molecule docking / virtual screening, but none really comparable to how DARC works Modeling of antibodies and immune system proteins RosettaAntibody47– antibody homology Jeliazko Jeliazkov, Jeffrey Gray https://www.rosettacommons.org/docs/latest/applicati Rosetta/main/tests/integration/tests/antibody_cc performance deteriorates for loops longer SAbPred51, PIGS52, 50 modeling and Nick Marze, Brian on_documentation/antibody/antibody-protocol than 12 residues; unusual CDR loops are BIOVIA Discovery docking Weitzner, hard to model because they are likely not Studio53, MOE54, Jared Adolf- represented in the database Schrodinger55 Bryfogle, Sergey Lyskov, Daisuke Kuroda AbPredict56,57 antibody structure Christoffer Norn, Sarel Fleishman https://www.rosettacommons.org/docs/latest/applicati https://www.rosettacommons.org/demos/latest/tu performance deteriorates for loops longer SAbPred51, PIGS52, prediction Gideon Lapidoth on_documentation/structure_prediction/AbPredict2 torials/AbPredict/AbPredict than 12 residues; unusual CDR loops are BIOVIA Discovery hard to model because they are likely not Studio53, MOE54, represented in the database Schrodinger55 2 Method Use Developer(s) Lab developed Documentation Protocol capture / demo Limitations Competing methods RosettaMHC58 modeling and Santrupti Nerli Nik Sgourakis https://github.com/snerligit/mhc-pep-threader https://github.com/snerligit/mhc-pep- the binding energies cannot fully bioinformatics-based docking of antigen threader/tree/master/examples discriminate best binders from average methods59: NetMHCPan60, / MHC-I / binders MHCFlurry61 (chaperone or T- structure-based cell receptor) methods62: AutoDock63, DINC64 TCRModel65 structure prediction Ragul Gowthaman Brian Pierce https://www.rosettacommons.org/docs/latest/applicati Rosetta/main/tests/integration/tests/tcrmodel challenging to model long CDR loops LYRA66, ImmuneScape67 of T-cell receptors on_documentation/structure_prediction/TCRmodel SnugDock68 docking of Jeliazko Jeliazkov, Jeffrey Gray https://www.rosettacommons.org/docs/latest/applicati Rosetta/main/tests/integration/tests/SnugDock performance deteriorates for loops longer usually for FFT methods
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