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
*JUST ADDED* May 7th— Jeff Ruffolo, PhD and Stephen Nayfach, PhD
Design of highly functional genome editors by modeling the universe of CRISPR-Cas sequences
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
Organizers
Meg Taylor
UW-Madison Biophysics PhD Student
Tianyu Lu
Stanford Bioengineering PhD Student
Ria Vinod
Brown University Computational Biology PhD Student
Contact us : mlproteinengineering@gmail.com
Past Organizers
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
Alex X. Lu
Senior Researcher, Microsoft Research
Marshall Case
Computational Biologist, Manifold Bio
David Belanger
Research Scientist, Google Research
Andreea Gane
Research Scientist, Google Research