Macromolecular modeling and design in Rosetta: recent methods and frameworks

By Julia Koehler Leman, Brian D. Weitzner, Steven M. Lewis, Jared Adolf-Bryfogle, Nawsad Alam, Rebecca F. Alford, Melanie L. Aprahamian, David Baker, Kyle A. Barlow, Patrick Barth and 98 more. 2020

783 citations recorded by the source corpus.

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. This Perspective reviews tools developed over the past five years in the macromolecular modeling, docking and design software Rosetta.

Subfields: Materials Chemistry, Molecular Biology, Spectroscopy, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Jeffrey J. Gray
2. David Baker
3. Jens Meiler
4. Yang Zhang
5. Rhiju Das
6. Frank DiMaio
7. Ivan V. Anishchenko
8. Andriy Kryshtafovych

In Molecular Biology

1. David Baker
2. Jeffrey J. Gray
3. Jens Meiler
4. Frank DiMaio
5. Yang Zhang
6. Rhiju Das
7. Andriy Kryshtafovych
8. Ivan V. Anishchenko

In Spectroscopy

1. David Baker
2. Frank DiMaio
3. Jeffrey J. Gray
4. Jens Meiler
5. Enrique Marcos
6. Sandeepkumar Kothiwale
7. J. Beton
8. Maya Topf

In Computational Biology & Bioinformatics

1. Jeffrey J. Gray
2. David Baker
3. Jens Meiler
4. Frank DiMaio
5. Andriy Kryshtafovych
6. Ivan V. Anishchenko
7. Maya Topf
8. Rhiju Das

Citations in the corpus

• Robust deep learning based protein sequence design using ProteinMPNN (2022, positive): “AlphaFold (9) and RoseTTAfold (13) produce remarkably good structure predictions for native proteins given multiple sequence alignments which can contain substantial co-evolutionary and other information reflecting aspects of the 3D structure, but generally produce much poorer structures when…”
• Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants (2021, methodology): “After structural predictions were generated, model relaxation by the Amber program, 63 which as reported by Jumper et al. 24 was used to ameliorate minor structural defects without impacting accuracy, was replaced by the constrained FastRelax protocol in the Rosetta program, 46 as detailed below.”
• CASP15 cryoEM protein and RNA targets: refinement and analysis using experimental maps (2023, methodology): “It is available as part of the ROSETTA package 25 .”
• A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia (2022, methodology): “RoseTTAFold is based on the Rosetta software, which is designed for macromolecular modeling, docking and design [20] RoseTTAFold also has good application [21] in the research of protein structure prediction.”
• The impact of AlphaFold2 one year on (2022, neutral): “One possible way forward in that respect will be to very carefully curate standardized test sets of protein structures, against which different prediction programs can be calibrated.”
• Growing Glycans in Rosetta: Accurate de novo glycan modeling, density fitting, and rational sequon design (2021, background): “Thus, optimizing the density and composition of glycans displayed on protein-based vaccines—either on the antigen and/or protein nano-particle scaffold—provides a framework for engineering glycan recognition to optimize vaccine efficacy.”
• Identifying well-folded de novo proteins in the new era of accurate structure prediction (2022, methodology): “Rosetta (Leman et al., 2020) was used to analyse the local sequence-structure compatibility of each design using the fragment picking protocol.”
• UCSF ChimeraX: Tools for structure building and analysis (2023, methodology): “The StarMap ChimeraX extension (Lugmayr et al., 2023) provides a simple user interface to run automated structure refinement in cryo-EM maps using Rosetta (Leman et al., 2020).”
• Introduction to the BioChemical Library (BCL): An Application-Based Open-Source Toolkit for Integrated Cheminformatics and Machine Learning in Computer-Aided Drug Discovery (2022, methodology): “…scienti fi c advances and software changes required to functionally integrate and compile the BCL in the Rosetta macromolecular modeling suite (Leman et al., 2020), enabling access to protocol development at the C++ (Rosetta applications), Python (PyRosetta), and XML (RosettaScripts)…”