Preprint Article Version 1 This version is not peer-reviewed

Macromolecular Modeling and Design in Rosetta: New Methods and Frameworks

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)

How to cite: Koehler Leman, J.; Weitzner, B.D.; Lewis, S.M.; Consortium, R.; Bonneau, R. Macromolecular Modeling and Design in Rosetta: New Methods and Frameworks. Preprints 2019, 2019040263 (doi: 10.20944/preprints201904.0263.v1). Koehler Leman, J.; Weitzner, B.D.; Lewis, S.M.; Consortium, R.; Bonneau, R. Macromolecular Modeling and Design in Rosetta: New Methods and Frameworks. Preprints 2019, 2019040263 (doi: 10.20944/preprints201904.0263.v1).

Abstract

The Rosetta software suite for macromolecular modeling, docking, and design is widely used in pharmaceutical, industrial, academic, non-profit, and government laboratories. Despite its broad modeling capabilities, Rosetta remains consistently among leading software suites when compared to other methods created for highly specialized protein modeling and design tasks. Developed for over two decades by a global community of over 60 laboratories, Rosetta has undergone multiple refactorings, and now comprises over three million lines of code. Here we discuss methods developed in the last five years in Rosetta, 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; modeling of peptides, antibodies and proteins in the immune system, nucleic acids, non-standard chemistries, 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.

Subject Areas

structure prediction; Rosetta; computational modeling; protein design

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