Mini Course: AI for Protein Engineering

Artificial intelligence is rapidly transforming protein engineering by enabling the design of entirely new proteins with desired structures and functions. This hands-on tutorial introduces participants to RFdiffusion, a state-of-the-art generative AI model for de novo protein design, using Google Colab. Participants will learn the complete AI protein design workflow, including backbone generation, symmetry specification, sequence design with ProteinMPNN, and structural prediction. Attendees will design a minimal symmetric light-harvesting protein assembly while gaining experience with modern computational tools used in protein engineering. No software installation is required, but participants will need to bring their own laptop and have a Gmail account.

This tutorial is intended for undergraduate and graduate students, postdoctoral researchers, and scientists interested in structural biology, protein engineering, computational biology, synthetic biology, and artificial intelligence. Basic knowledge of proteins is helpful but not required.

 

Dr. Saacnicteh Toledo Patiño is a structural biologist and protein engineer whose research focuses on the evolution and de novo design of proteins using artificial intelligence. She combines state-of-the-art AI methods, including RFdiffusion, AlphaFold, and ProteinMPNN, with experimental structural biology to investigate protein evolution and engineer novel protein architectures.

 

Use the link below to sign up.

Mini-Course Registration Form

 

RFdiffusion 3: https://www.ipd.uw.edu/2025/12/rfdiffusion3-now-available/