Article
Version 2
This version is not peer-reviewed
Macromolecular Modeling and Design in Rosetta: New Methods and Frameworks
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Version 1
: Received: 21 April 2019 / Approved: 24 April 2019 / Online: 24 April 2019 (10:16:38 CEST)
Version 2 : Received: 29 April 2019 / Approved: 8 May 2019 / Online: 8 May 2019 (08:24:25 CEST)
Version 3 : Received: 15 October 2019 / Approved: 16 October 2019 / Online: 16 October 2019 (05:40:52 CEST)
Version 2 : Received: 29 April 2019 / Approved: 8 May 2019 / Online: 8 May 2019 (08:24:25 CEST)
Version 3 : Received: 15 October 2019 / Approved: 16 October 2019 / Online: 16 October 2019 (05:40:52 CEST)
A peer-reviewed article of this Preprint also exists.
Abstract
The Rosetta software suite for macromolecular modeling, docking, and design is widely used in pharmaceutical, industrial, academic, non-profit, and government laboratories. Considering its broad modeling capabilities, Rosetta consistently ranks highly when compared to other leading methods created for highly specialized protein modeling and design tasks. Developed for over two decades by a global community of scientists at more than 60 institutions, Rosetta has undergone multiple refactorings, and now comprises over three million lines of code. Here we discuss the methods developed in the last five years, involving the latest protocols for structure prediction, protein–protein and protein–small molecule docking, protein structure and interface design, loop modeling, the incorporation of various types of experimental data, and modeling of peptides, antibodies and other proteins in the immune system, nucleic acids, non-standard amino acids, carbohydrates, and membrane proteins. We briefly discuss improvements to the energy function, user interfaces, and usability of the software. Rosetta is available at www.rosettacommons.org.
Keywords
structure prediction; Rosetta; computational modeling; protein design
Subject
Chemistry and Materials Science, Theoretical Chemistry
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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