Rafi Brent

PhD student at the Institute for Protein Design | Baker Lab | Machine Learning for Protein Design | University of Washington Molecular Engineering || Yale MB&B

[Saman Salike @samansalike31]

Designing ligases for pharmaceutically relevant small molecules has evaded us for years. To our amazement, these were some banger enzymes straight out of the computer and a great example of how de novo enzymes can transform synthesis of complex molecules. More to come on ligases!

[Nate Corley @nscorley]

1/n

Crossover episode! We introduce RFD4-Proteina, our latest design model - and the result of a year-long collaboration between the Baker Lab and NVIDIA GenAIR (the name says it all…). I hope you’ll agree - the results are astonishing. 🌐 Website: https://t.co/FRbZU8OiKy

[Kieran Didi @DidiKieran]

Avengers assemble: 1 year BakerLab + NVIDIA GenAIR = 🔥 https://t.co/Cd2qiugCJo

Presenting RFD4-Proteina, our latest protein design model, significantly better and faster than everything we compared against, both in silico and in the lab! 1/30

[Maxwell Kazman @maxkazman]

Great things come from great teams! Super excited to share https://t.co/TnVV8YAW3f, a step-function improvement in generative protein design! A year in the making with @UWproteindesign , NVIDIA GenAIR and the amazing @DidiKieran @nscorley and @RafiBrent! 🧵1/n

[UW Medicine Newsroom @uwmnewsroom]

RFD4-Proteina, co-developed by @UWproteindesign @UWMedicine & @NVIDIAHealth, debuts today at GTC Berlin. The open-source generative model will use biomolecular reasoning to find new protein solutions for medicine, sustainability, materials & biotech. https://t.co/GSy9hwvoRM

[Christian Dallago @sacdallago]

A step change in protein design: RFD4-Proteina is enables everyone today.

We partenered with @huggingface to make available a free space.

You can solve your hardest biology problem right now. No CS degree required: https://t.co/VcYse3Ddvx

More to come...

[Karsten Kreis @karsten_kreis]

It has been incredibly exciting to bring the RFdiffusion and Proteina model families together. An amazing release! 💜💚🔥🎉

And I am just as excited about the applications! 🧬

Massive congrats to @DidiKieran, @nscorley, @RafiBrent, @maxkazman and the many more contributors!

[Arash Vahdat @ArashVahdat]

🚀 Today at GTC Berlin we introduced RFD4-Proteina! A close one-year collaboration between GenAIR and David Baker's group at @UWproteindesign !

[Julia Bonzanini @jBonzaniniSci]

Super excited about this work! We are finally able to not only screen binders for specificity, but design them to be specific 🤯 this will be transformational in the development of pMHC binders and therapeutics in general

[Magnus Bauer @kinasekid]

Protein design doesn’t have to stop at the canonical 20 amino acids! After our work on phosphotyrosine binders, RFD4-Proteina adds phosphoserine and phosphothreonine (below) to the repertoire, with binders that distinguish adjacent H3pS10 and H3pT11 sites in the histone H3 tail!

[Rafi Brent @RafiBrent]

And finally, huge thanks to the fantastic folks at @UWproteindesign and Nvidia who helped make this dream a reality. I've been excited about protein design since high school due to a great TED talk from David Baker. Thanks David for creating such a great place to do science! n/n

[Rafi Brent @RafiBrent]

What a wild ride! After a fantastic collaboration between @UWproteindesign and Nvidia GenAIR, we've released the latest in the RFdiffusion and Proteina lineages, namely... RFD4-Proteina! Computationally and in the lab, the results have astonished us🧵 1/n https://t.co/PPddK8nlsb

[Rafi Brent @RafiBrent]

More details to come! For more info:

Website: https://t.co/Wk0rONNaV4
Paper: https://t.co/aEGsy6JBxt
Code: https://t.co/0R3lNNWVNO
Demo: https://t.co/8sHlNzDbSc

And check out fantastic threads from my coauthors:
https://t.co/RcBWJMrSyg
https://t.co/h2QDWq1uyX

[Kieran Didi @DidiKieran]

Avengers assemble: 1 year BakerLab + NVIDIA GenAIR = 🔥 https://t.co/Cd2qiugCJo

Presenting RFD4-Proteina, our latest protein design model, significantly better and faster than everything we compared against, both in silico and in the lab! 1/30