Gene-Wei Li Lab @GeneWeiLiLab
Gene-Wei Li's lab @MITBiology, studying the quantitative biology of gene expression. Account run by lab members. gwli.scripts.mit.edu/group/ Joined February 2022-
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Here we show that widespread biases in coding strand sequence composition allow mRNAs to escape premature termination, while simultaneously suppressing antisense transcription -- sculpting bacterial genomes across lineages. nature.com/articles/s4156… @MITBiology @hhmi_science (2/2)
We're excited to share our latest publication, from graduate student Julia Dierksheide in the lab! In B. subtilis, the fast-moving RNA polymerase outpaces the leading ribosome, leaving mRNA transcripts vulnerable to the transcription termination factor Rho. (1/2)
Check out this fantastic new paper spearheaded by my postdoc colleague Bob Battaglia @RNABob! With Ebru Ermis and Expery Omollo @expery_. This work reveals a previously hidden element defining the core regulatory grammar of transcription termination.
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)
Here, we employ precise termination site mapping on a massively parallel scale to reveal the HUB (hairpin, U-tract, & bubble-edge sequences) as the defining element of intrinsic terminators, across diverse bacterial phyla. @MITBiology @hhmi_science (2/2) biorxiv.org/content/10.648…
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)
Great to collaborate with @BabitzkePaul and Katsuhiko Murakami on this work. @MITBiology @ScienceMIT @MIT @hhmi_science (2/2)
Excited to share our recent work expanding our understanding of transcription termination, from Jenny Cascino in the lab! Here we show that transcription termination in cyanobacteria proceeds largely outside of known, canonical mechanisms. journals.asm.org/doi/10.1128/ms… (1/2)
Thrilled to share the latest from @bioSavinov, K99 fellow in the lab & corresponding on this work together w/ @LinsdayCase19. Andrew & @jibin_sadasivan show that protein fragments discovered with our FragFold AI method act as generalizable regulators of biomolecular condensates!
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/10.648…
What a fantastic visit. Thanks to @omenndarlingbio and @PrincetonMolBio for hosting!
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”
Thrilled to contribute to this exciting work on protein binder design together w/ @HannesStaerk, @felix_faltings, @BarzilayRegina, Tommi Jaakkola, and co. We applied BoltzGen to design novel antimicrobial peptides targeting DNA gyrase based on inhibitory protein fragments. (1/n)
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! 🧵..
Agreed, Yunha. Fantastic to discuss the interplay of AI & biology with you, Teja, & great attendees @BostonBacteria. Many thanks to David for moderating. Opportunities for new discoveries are vast, esp. if we keep pushing systematic experimental measurements alongside AI models!
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
An exciting step towards connecting proteomes with one of the most basic phenotypes of a cell! Work led by @scottmccain5 w/ @GeneWeiLiLab @gregorybritten Sean Hackett and Mick Follows. @MIT @MITBiology @HHMINEWS
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/advance-…
Hot on the heels of excellent work from @SvenssonLab, more evidence of large-scale generation of functional protein fragments by proteolysis nature.com/articles/s4158… Very exciting to see this convergence with our work showing pervasive function of protein fragments in vivo! (1/2)
Fantastic to see these physiologically functional protein fragments, congrats all! This aligns with our work demonstrating pervasive functionality of protein fragments in living cells (& ability to predict their binding partners): pnas.org/doi/10.1073/pn… pnas.org/doi/10.1073/pn…
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/s4158…
A new method developed in @MITBiology builds on existing artificial intelligence models to computationally predict protein fragments that can bind to and inhibit full-length proteins in E. coli buff.ly/4k1Ga4N
Great to see this piece from @MITBiology highlighting our work on discovering protein fragments to inhibit protein interactions in cells! Thanks @LillianWEden for the write-up.
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
Story coming soon from @LillianWEden
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
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/pn…. @MITBiology @ScienceMIT @HHMINews See quoted thread on the updated preprint for details:
for the folks who missed @bioSavinov's fantastic seminar this week, you can check out the recording 👇 youtu.be/s31yC7nQqDs and thanks again to @pillar_vc for hosting!
Check out Li lab postdoc Andrew Savinov's (@bioSavinov) updated manuscript on our computational approach to predict inhibitory protein fragments at scale -- now complemented by extensive new deep mutational scanning experiments. @MITBiology @ScienceMIT @HHMINews
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/10.110… (1/n)
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