Protein design is of great technological importance and can solve many biomedically relevant challenges. Examples include new therapeutic agents (COVID-19 inhibitors, COVID-19 vaccines, flu inhibitors), nanomaterials (cages, layers, fibers) and sensors.

The workshop is set up as a “crash course” in protein design, highlighting what is possible with current technology. Participants will learn how to build a four-helix bundle protein from scratch (i.e. using only mathematics). At the end of the workshop, they will obtain a sequence for the protein that is predicted to fold into the designed structure. The basics of Rosetta, parametric backbone design, design of loops and design of side chains will be introduced. The latest deep learning methods (Alphafold2, ProteinMPNN and RFDiffusion) will be presented.  

Target audience: The workshop is intended for undergraduate and graduate students as well as postdocs and young scientists interested in protein design. Participants should know the basics of protein structure (what is an alpha helix, what are backbone and sidechains). The basics of Python (how to import modules and execute commands in a Jupyter Notebook) are also needed. 

Prerequisites: Participants  need  to bring  their  own  laptop.  Google  Colab  will be  used  as  the computational environment so only a browser is needed. Participants will also need a Google account with 1.5 GB free space on the Google drive.