Gene-Wei Li Lab

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Gene-Wei Li Lab

Gene-Wei Li Lab

@GeneWeiLiLab

Gene-Wei Li's lab @MITBiology, studying the quantitative biology of gene expression. Account run by lab members.

Katılım Şubat 2022
45 Takip Edilen688 Takipçiler
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Gene-Wei Li Lab
Gene-Wei Li Lab@GeneWeiLiLab·
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
Scott McCain@scottmccain5

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-…

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Andrew Savinov
Andrew Savinov@bioSavinov·
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)
Hannes Stark@HannesStaerk

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! 🧵..

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Andrew Savinov
Andrew Savinov@bioSavinov·
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!
Yunha Hwang@Micro_Yunha

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/todays-seque… Big thanks to David Giacalone, @bioSavinov & Teja Jammalamadaka for keeping the conversation engaging!

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Andrew Savinov
Andrew Savinov@bioSavinov·
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)
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Andrew Savinov
Andrew Savinov@bioSavinov·
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…
SvenssonLab@SvenssonLab

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…

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MIT Science
MIT Science@ScienceMIT·
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
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Andrew Savinov
Andrew Savinov@bioSavinov·
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.
MIT Biology@MITBiology

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

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Gene-Wei Li Lab
Gene-Wei Li Lab@GeneWeiLiLab·
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
Andrew Savinov@bioSavinov

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:

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Gene-Wei Li Lab
Gene-Wei Li Lab@GeneWeiLiLab·
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
Andrew Savinov@bioSavinov

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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Andrew Savinov
Andrew Savinov@bioSavinov·
Massive congratulations to my postdoc mentor Gene Wei-Li @GeneWeiLiLab for being named an HHMI Investigator! Deeply deserved, and I am incredibly excited for the transformative science that lies ahead. Congrats as well to the rest of this amazing cohort. @MITBiology @ScienceMIT
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Gene-Wei Li Lab
Gene-Wei Li Lab@GeneWeiLiLab·
Congratulations to Li lab graduate student Manraj Gill on being awarded the William Asbjornsen Albert Memorial Fellowship for his academic accomplishments and his commitment to the MIT community! @MITGradStudents @ScienceMIT
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Keşfet

@omenndarlingbio @PrincetonMolBio @HannesStaerk @felix_faltings @BarzilayRegina @BostonBacteria @SvenssonLab @MITBiology