Protocols for Molecular Modeling with Rosetta3 and RosettaScripts

By Brian Joseph Bender, Alberto Cisneros, Amanda M. Duran, J. A. Finn, Darwin Y. Fu, A. D. Lokits, Benjamin K. Mueller, Amandeep K. Sangha, Marion Sauer, Alexander M. Sevy and 5 more. 2016

176 citations recorded by the source corpus.

Previously, we published an article providing an overview of the Rosetta suite of biomacromolecular modeling software and a series of step-by-step tutorials [Kaufmann, K. W., et al. (2010) Biochemistry 49, 2987–2998]. The overwhelming positive response to this publication we received motivates us to here share the next iteration of these tutorials that feature de novo folding, comparative modeling, loop construction, protein docking, small molecule docking, and protein design. This updated and expanded set of tutorials is needed, as since 2010 Rosetta has been fully redesigned into an object-oriented protein modeling program Rosetta3. Notable improvements include a substantially improved energy function, an XML-like language termed “RosettaScripts” for flexibly specifying modeling task, new analysis tools, the addition of the TopologyBroker to control conformational sampling, and support for multiple templates in comparative modeling. Rosetta’s ability to model systems with symmetric proteins, membrane proteins, noncanonical amino acids, and RNA has also been greatly expanded and improved.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Macromolecular modeling and design in Rosetta: recent methods and frameworks (2020, methodology): “Comprehensive knowledge of the codebase is unnecessary since most of the underlying modules 177 have been thoroughly documented – documentation is now also generated using XML schema, which validates the RosettaScripts XML files at runtime.”
Multistate design of influenza antibodies improves affinity and breadth against seasonal viruses (2019, methodology): “We created homology models of HA proteins from the sequences of 524 viruses in the Influenza Research Database using the RosettaCM multitemplate comparative modeling protocol (14, 15).”
A Recurrent Mutation in Anaplastic Lymphoma Kinase with Distinct Neoepitope Conformations (2018, methodology): “(3) We computed the binding energies of the relaxed models using the InterfaceAnalyzer protocol (79, 82) by separating the MHC and the peptide energy contributions and subtracting them from the energy of the bound p/MHC (30).”
Integration of the Rosetta suite with the python software stack via reproducible packaging and core programming interfaces for distributed simulation (2019, background): “To make the software accessible to a broader range of scientists, the suite exposes multiple, high-level interfaces including RosettaScripts,(1) PyRosetta,(2) and protocol-specific command-line executables.(3) Current academic efforts to make Rosetta more accessible, such as Robetta(4) or ROSIE,(5) ”
CS-ROSETTA (2018, background): “Determination of high-resolution protein structures from amino acid sequence alone remains as one of the most challenging problems in computational biology (Bender et al., 2016).”
Structural basis of ligand binding modes at the neuropeptide Y Y1 receptor (2018, methodology): “IP accumulation assays of WT Y1R for NPY/NPY analogues NPY Ac-NPY [A1]NPY [A2]NPY [A1,A2]NPY NPY(3-36) NPY(13-36)”
Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints (2018, methodology): “Well established fragment-based methods such as Rosetta [4] and FRAGFOLD [5] add constraints from predicted contacts to an existing fragment assembly pipeline.”
Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks (2020, methodology): “These 85 methods either rely on explicit biophysical modeling of amino acid interactions coupled with 86 conformational sampling of protein structures (7-11) or apply machine/statistical learning to 87 extract patterns from various types of amino acid sequence, evolutionary, and/or protein 88 struct”
Ultrafast neuronal imaging of dopamine dynamics with designed genetically encoded sensors (2018, methodology): “The threaded structures were then hybridized together with rosetta_cm protocol for membrane protein (43).”