Multimedia Jupyter Notebooks for Learning Structure Prediction and Design

Abstract

PyRosetta is a Python library of the Rosetta computational methods for protein structure prediction and design. Because the field attracts students from many different majors, teaching a class with a wide variety of skills and experience levels is a challenge. Therefore, the goal of this project is to develop interactive multimedia material that promotes self-paced active learning. We have developed a hands-on education strategy with a set of nine modules to teach topics in the field, from protein structural analysis to protein folding and docking. These workshop modules are created in Jupyter Notebooks, a shareable web application that supports live code, visualizations, and text. When used in the Google Colaboratory environment, students can access and learn PyRosetta with no local machine setup necessary. By capitalizing on the advantages of this online digital format, we have embedded images, molecular visualization movies, and interactive coding examples. This multimedia approach may better reach students from different majors and experience levels as well as attract more researchers from smaller labs and related disciplines to leverage PyRosetta in their work.