PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design

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.

Developing and Implementing Cloud-Based Tutorials That Combine Bioinformatics Software, Interactive Coding, and Visualization Exercises for Distance Learning on Structural Bioinformatics

“…It is worth noting that these tutorials have been made public through scientific publications and/or under permissive MIT and GNU GPL v2.0 licenses. This is the case for Lab.04 on comparative modeling, partly based on the MODELLER tutorial while also replacing the target protein; Lab.06 on molecular docking, which largely replicates the AutoDock tutorial on binding of indinavir to HIV-1 protease but uses a different ligand and receptor preparation; Lab.07 on molecular dynamics (MD), which is extensively based on the GROMACS tutorials by Justin Lemkul but changes the target protein; Lab.08 on analyzing MD simulations, based on the MDAnalysis tutorial; , Lab.11 on coevolutionary analysis, based on the tutorial for pyDCA but adding the preparation of RNA sequence alignments with infernal; and Lab.05 on comparative modeling of membrane proteins and Lab.12 on ab initio modeling of soluble proteins inspired by a recent cloud-computing implementation of pyRosetta …”

“…In this context, learning tutorials tailored for specific bioinformatics tasks can be developed and shared with students and researchers in the form of Jupyter notebooks. Such an approach has been employed for the development of tutorials on biomolecular structure prediction and design based on PyRosetta and to encourage open data science in metabolomics …”

Growing Glycans in Rosetta: Accuratede novoglycan modeling, density fitting, and rational sequon design

“…All tools are available as components for RosettaScripts and PyRosetta. In addition, the use of all components are covered in publicly accessible tutorials 63 and detailed protocol captures 64 . Results of this study are continuously benchmarked using the Rosetta automated scientific testing framework 65 .…”

Ten Simple Rules for Implementing a Flipped Classroom

“…Using more interactive material such as Jupyter notebooks (Le et al, 2020;Teaching and Learning with Jupyter, 2019) or the Rosalind auto-correcting exercise collection (Compeau & Pevzner, 2018) are even more beneficial. Finally, it is possible to engage students working as teams on open research challenges (Abdollahi et al, 2018).…”