First time at #MLCB! I'll be speaking tomorrow about my development of an ML predictor of domain–peptide interaction affinity to model proteome-scale signaling networks. It'll be livestreamed too.

I am beyond excited and honored to receive a BWF CASI! This amazing program will support my transition from postdoc to faculty as I continue to develop new modeling frameworks for elucidating and programming cellular behaviors.

AFESM: a metagenomic guide through the protein structure universe! We clustered 821M structures (AFDB&ESMatlas) into 5.12M groups; revealing biome-specific groups, only 1 new fold even after AlphaFold2 re-prediction & many novel domain combos. 🧵
🌐 afesm.foldseek.com
📄 www.biorxiv.org/content/10.1...

Small proteins can be more complex than they look!

We know proteins fluctuate between different conformations- but by how much? How does it vary from protein to protein? Can highly stable domains have low stability segments? @ajrferrari.bsky.social experimentally tested >5,000 domains to find out!

Structural biology is in an era of dynamics & assemblies but turning raw experimental data into atomic models at scale remains challenging. @minhuanli.bsky.social and I present ROCKET🚀: an AlphaFold augmentation that integrates crystallographic and cryoEM/ET data with room for more! 1/14.

Enjoying #BPS2025! I'll present my development of a ML predictor of domain–peptide binding affinity to understand how affinity is optimized across the proteome for cell signaling. Finish out the meeting by coming to my talk tomorrow (Wed) @ 1pm!

Excited to announce the newest member of the flock - STARLING (conSTruction of intrinsicAlly disoRdered proteins ensembles efficientLy vIa multi-dimeNsional Generative models).
www.biorxiv.org/content/10.1...

Can we learn protein biology from a language model?

In new work led by @liambai.bsky.social and me, we explore how sparse autoencoders can help us understand biology—going from mechanistic interpretability to mechanistic biology.

Super excited to preprint our work on developing a Biomolecular Emulator (BioEmu): Scalable emulation of protein equilibrium ensembles with generative deep learning from @msftresearch.bsky.social ch AI for Science.

www.biorxiv.org/content/10.1...

Could one envision a synthetic receptor technology that is fully programmable, able to detect diverse extracellular antigens – both soluble and cell-attached – and convert that recognition into a wide range of intracellular responses, from gene expression and real-time fluorescence to modulation..

Introducing ESM Cambrian, a new family of protein language models, focused on creating representations of the underlying biology of proteins.