Keating Lab

To solve this, Birnbaum and Keating present PottsMPNN, a sequence design model that learns a sequence-energy landscape from MSAs. PottsMPNN outperforms other sequence design models and is a drop-in alternative to ProteinMPNN. Code: github.com/KeatingLab/Pot… (2/2)

Here’s a new preprint from the Keating Lab: biorxiv.org/content/10.648… Foster Birnbaum and Amy E. Keating demonstrate that sequence design models, such as ProteinMPNN, are limited because they were trained only on native sequences. (1/2) #ProteinDesign #StructuralBiology

We’re expanding our digital footprint! To keep up with the latest protein design research, lab news, and updates from the Keating Lab, follow us over on Bluesky: bsky.app/profile/keatin…. See you there! #ProteinDesign #StructuralBiology

A self-supervised approach aligning protein sequence and structure spaces enables efficient binder screening with only backbone structural information — a powerful asset for early-stage protein binder design. 🔗 go.aps.org/3UY7OVe

To learn more, read out the publication in PRX Life and use the publicly available code (github.com/MadryLab/rla). Thanks to Foster Birnbaum, Saachi Jain, Aleksander Madry, and Amy E. Keating for this work! 3/3

RLA has been tested on several benchmark sets, including several design libraries of miniprotein designs for a variety of protein targets. For all but two targets, filtering with RLA results in a higher success rate after subsequent AF2-based filtering. 2/3

New work from the lab! Check out RLA (journals.aps.org/prxlife/abstra…), a contrastive-learning approach that assesses sequence-structure compatibility by aligning sequence and structure machine learning representations! RLA can successfully filter protein binder designs. 1/3 🧶

First twitter thread🧵and also my first BioRxiv preprint! I’m excited to finally release my undergrad work into the world: combining GNNs, Potts models, and Tertiary Motifs (TERMs) for protein design! See the preprint here: biorxiv.org/content/10.110… 1/

Our first keynote speaker: Dr. Amy Keating (@keating_lab). Interested in protein interaction specificity, Dr. Keating highlights the power of data-driven computational exploration of protein interactions. Dr. Keating was our student choice of #PEC2022 and we are ecstatc to host!

Fast, reliable, computational methods for designing protein-binding peptides would be immensely useful. As a first step, we show that tertiary structural motifs from the PDB can be used to reconstruct known peptide structures and generate new ones. doi.org/10.1002/pro.43… (1/9)

Scientists in @keating_lab designed a screening method to probe how short stretches of amino acids called SLiMs selectively bind to certain proteins, and distinguish between binding partners with similar structures. I covered this recent work for MIT News: bit.ly/3B4piEb

Excited to share our newest work! We describe a surprising mechanism behind how a short linear motif binding domain achieves interaction specificity. elifesciences.org/articles/70601

Register today! proteinsociety.org/e/in/eid=7 #PS35

@KevinKaichuang @KeatingLab Hi Kevin! Thanks for sharing our review. We're not quite sure who nabbed the original @KeatingLab twitter handle, but we assure you that we're the real thing, and they're the impostor :)

Review of data-driven protein design, including structure, sequences, and high-throughput functional datasets. Vincent Frappier @KeatingLab sciencedirect.com/science/articl…

Our COSB mini-review on data-driven protein design is now available here: authors.elsevier.com/a/1czDI,LqArIa…

Read about the amazing accomplishments of alum MIT biologist and president of The Protein Society, Prof. Amy Keating (PhD ’98 Houk/García-Garibay groups) bit.ly/3w0kA7O @houk1000 @GaribayLab @MITBiology @ProteinSociety