Thrilled to announce our latest work, from postdoctoral fellow Bob Battaglia @RNABob in the lab! Transcription termination is a key determinant of gene expression. However, the sequence grammar defining transcription termination has long been elusive. (1/2)

Interested in genetically encodable inhibitors of your favorite biomolecular condensate? Excited to announce our latest work, w/ @jibin_sadasivan, @GeneWeiLiLab, & @LindsayCase19, on protein fragments as generalizable regulators of phase separation. (1/n) biorxiv.org/content/10.64898…

Today we and @PrincetonMolBio welcome Andrew Savinov (@bioSavinov) from @MIT for a special seminar on “Massively parallel discovery of peptides to control cellular protein interactions”

Excited to release BoltzGen which brings SOTA folding performance to binder design! The best part of this project has been collaborating with many leading biologists who tested BoltzGen at an unprecedented scale, showing success on many novel targets and pushing its limits! 🧵..

Grateful for the opportunity to speak at the @BostonBacterial Meeting yesterday on a panel about how we can best apply AI to microbiology research! Much of the discussion was focused on how existing AI tools can help researchers. But perhaps the more important question we should be asking ourselves as researchers is how the data that we generate in our labs can improve the very AI tools we use. Building a living infrastructure of biological data that evolves real-time with scientific progress is more critical than ever. Stay tuned for updates on what we are building next at @tatta_bio : tattabio.substack.com/p/toda… Big thanks to David Giacalone, @bioSavinov & Teja Jammalamadaka for keeping the conversation engaging!

What are cells doing?? Here we showed that proteomic composition encodes information about cellular reaction rates. This is a step forward to reconstructing these important activities using protein-based observations. academic.oup.com/ismej/advan…

Today in @Nature, we share our latest paper. We developed an algorithm to predict novel peptides cleaved by proconvertases across all human tissues. Using this method, we identified BRP, a brain-derived anti-obesity peptide. nature.com/articles/s41586-0…

Researchers in the Department of Biology use an AI-driven approach to computationally predict short amino acid sequences that can bind to or inhibit a target, with a potential for great impact on fundamental biological research and therapeutic applications. 🧵1/5

Thrilled to announce the final version of our paper on computational discovery of protein fragments that can inhibit cellular protein interactions, led by Andrew Savinov (@bioSavinov), now published in @PNASNews. @MITBiology @ScienceMIT @HHMINews

Happy to announce that our paper on systematically discovering peptides to inhibit protein-protein interactions in living cells is out now @PNASNews: pnas.org/doi/10.1073/pnas.23…. @MITBiology @ScienceMIT @HHMINews See quoted thread on the updated preprint for details:

Excited to share our updated manuscript on FragFold, the massively parallel AlphaFold-based approach we’ve developed to discover protein fragment inhibitors at scale -- w/ @SassSeabass, @keating_lab, and @GeneWeiLiLab. biorxiv.org/content/10.1101/… (1/n)

Excited to share some work from my postdoc! In our recent preprint (w/ @gregorybritten @GeneWeiLiLab Sean Hackett and Mick Follows), we ask how to use proteomic data to estimate in vivo reaction rates? (1/n)