PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design.

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 sixteen 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

Déclaration de conflit d'intérêts

Conflict of Interest The teaching tools described in this paper relate to the Rosetta software, and some of the funding for this study was provided by the Rosetta Commons. JJG and JBS are unpaid board members of the Rosetta Commons. Under institutional participation agreements between the University of Washington, acting on behalf of the Rosetta Commons, Johns Hopkins University and UC-Davis (as well as other Rosetta Commons institutions where work was performed on this report) may be entitled to a portion of revenue received on commercial licensing of Rosetta software. As a member of the Scientific Advisory Board, JJG has a financial interest in Cyrus Biotechnology. Cyrus Biotechnology distributes the Rosetta software, which may include methods described in this paper. These arrangements have been reviewed and approved by the Johns Hopkins University in accordance with its conflict of interest policies.