Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design

Kathy Le
* ,
Jared Adolf-Bryfogle
ORCID logo ,
Jason Klima
,
Sergey Lyskov
,
Jason Labonte
,
Steven Bertolani
,
Shourya Roy Burman
,
Andrew Leaver-Fay
,
Brian Weitzner
,
Jack Maguire
,
Ramya Rangan
,
Matt Adrianowycz
,
Rebecca Alford
,
Aleexan Adal
,
Morgan Nance
,
Rhiju Das
,
Roland Dunbrack
,
William Schief
,
Brian Kuhlman
,
Justin Siegel
,
Jeffrey Gray
Version 1 : Received: 6 February 2020 / Approved: 7 February 2020 / Online: 7 February 2020 (11:42:09 CET)

A peer-reviewed article of this Preprint also exists.

Le, K.H.; Adolf-Bryfogle, J.; Klima, J.C.; Lyskov, S.; Labonte, J.W.; Bertolani, S.; Burman, S.S.R.; Leaver-Fay, A.; Weitzner, B.D.; Maguire, J.; et al. PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design. The Biophysicist 2021, 2, 108–122, doi:10.35459/tbp.2019.000147. Le, K.H.; Adolf-Bryfogle, J.; Klima, J.C.; Lyskov, S.; Labonte, J.W.; Bertolani, S.; Burman, S.S.R.; Leaver-Fay, A.; Weitzner, B.D.; Maguire, J.; et al. PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design. The Biophysicist 2021, 2, 108–122, doi:10.35459/tbp.2019.000147.

Abstract

Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of fifteen modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications like protein docking, antibody design, and RNA structure prediction. Our modules are based on PyRosetta, a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in their science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks.

Supplementary and Associated Material

Keywords

Protein structure and dynamics; Molecular structure and modeling; Protein and macromolecules; Computational methods and bioinformatics; Computer-based teaching tools; Learning materials and teaching tools; Multimedia teaching tools

Subject

Biology and Life Sciences, Biochemistry and Molecular Biology

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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