About

Recent advances in high-throughput experimental methods and machine learning approaches have fueled interest in ML-driven protein design. These advances may enable more rapid development of designed proteins with applications ranging from biopharmaceuticals, catalysis, material design and basic science research. However, this excitement has exposed important research questions across the foundation of this emerging engineering discipline. For example:

• What experimental approaches can feed the data-driven design cycle?

• Which machine learning models and parameterizations of proteins hold the right inductive biases?

• What are the limits of the growing structural and evolutionary data in the PDB and UniProt?

• How do we use our trained models to guide data collection?

We think these questions will be best addressed by a collaborative, interdisciplinary community. Thus, the ML4Protein Engineering community runs a bi-weekly seminar series to address these advances and other outstanding problems, such as high-throughput screening, model-based optimization, and representation learning.

To access announcements, please follow us on Twitter! You can also visit our YouTube to see recordings of past talks! Also, be sure to join our NEW Slack Community, where we discuss even more opportunities beyond the seminar series!

Check out our Slack Community!

Upcoming Seminars

Every other Tuesday 4-5pm EST unless otherwise noted

For a list of past seminars and recordings, check the full schedule page.

April- June 2024

April 2nd— Zhangzhi Peng, PhD student (Duke)

PTM-Mamba: A PTM-Aware Protein Language Model with Bidirectional Gated Mamba Blocks

April 16th — Brian Hie, PhD (Stanford)

Sequence modeling and design from molecular to genome scale with Evo

April 30th — Francesca-Zhoufan Li, PhD student (CalTech)

Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models

POSTPONED TO A LATER DATE

May 14th— Francisco Vargas, PhD student (Cambridge)

A framework for conditional diffusion modelling with applications in motif scaffolding for protein design

May 28th — Zaixiang Zheng, PhD (ByteDance Research)

Diffusion Language Models Are Versatile Protein Learners

June 11th — Céline Marquet, PhD student (TU Munich)

Bridging Sequence and Structure: Latent Diffusion for Conditional Protein Generation

Meg Taylor
UW-Madison Biophysics PhD Student

Tianyu Lu
Stanford Bioengineering PhD Student

Ria Vinod
Brown University Computational Biology PhD Student

Kevin K. Yang
Senior Researcher, Microsoft Research

Brian L. Trippe
Postdoctoral Fellow, Columbia University

Ava P. Soleimany
Senior Researcher, Microsoft Research

Lucy Colwell
Research Scientist, Google Research

Jody Mou
MIT HST PhD Student

Amy Lu
UC Berkeley EECS PhD Student