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We have trained ESM3 and we're excited to introduce EvolutionaryScale. ESM3 is a generative language model for programming biology. In experiments, we found ESM3 can simulate 500M years of evolution to generate new fluorescent proteins. Read more:

1,537,270 görüntüleme • 2 yıl önce •via X (Twitter)

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Alex Rives profil fotoğrafı
Alex Rives2 yıl önce

We prompted ESM3 to generate fluorescent proteins with a chain of thought. In the first plate, shown below, we were intrigued to find B8. While very dim, 50x dimmer than natural GFPs, it was far from any known GFPs -- 43% of its sequence differs from the closest natural protein. Continuing the chain of thought from B8 on the second plate below, ESM3 found C10 which is similarly bright to natural fluorescent proteins.

Garry P. Nolan profil fotoğrafı
Garry P. Nolan2 yıl önce

Extraordinary! Can it generate interaction partners of a given protein? Could you design with this a new "general" interaction partner framework (like a new class of small programmable binding proteins that would replace (too expensive) antibodies and which would be easy to produce in bacteria?

Brian Armstrong profil fotoğrafı
Brian Armstrong2 yıl önce

Congrats!

clem 🤗 profil fotoğrafı
clem 🤗2 yıl önce

Very cool and welcome to the @Lux_Capital fam. Let us know if we can help in any way on the @huggingface side (we're crazy excited about open/collaborative biology)!

Derya Unutmaz, MD profil fotoğrafı
Derya Unutmaz, MD2 yıl önce

Amazing! I will read the paper now. If what you claim is true, this would be the holy grail of programming biological systems! 😲 👏

Andrew White 🐦‍⬛ profil fotoğrafı
Andrew White 🐦‍⬛2 yıl önce

Interesting choice on the geometric attention - no equivariant layers. Just using frames and alignment based on heavy backbone atoms (?) Seems like scaling would be difficult but large structures are shown in the preprint. Congrats - love the compression results!!

Atheno profil fotoğrafı
Atheno2 yıl önce

Literally the nicest footer I’ve ever seen

Laurens van der Maaten profil fotoğrafı
Laurens van der Maaten2 yıl önce

Really cool, congratulations team!!

Christian von Uffel profil fotoğrafı
Christian von Uffel2 yıl önce

The best use cases I can imagine for ESM3 are finding proteins that can help people metabolize heavy metals and microplastics.

Jadechip profil fotoğrafı
Jadechip2 yıl önce

Hmm so proteins, sequence, structure are tokenized and ingested by geometric attention blocks? Interesting…

Benzer Videolar

What seemed like an intractable problem is now possible: To design proteins with a specified nonlinear mechanical response, capturing complex folding and unfolding mechanisms in singe and few-shot computations. We present ForceGen, an end-to-end algorithm for de novo protein generation based on nonlinear mechanical unfolding responses. Rooted in the physics of protein mechanics, this generative strategy provides a powerful way to design new proteins rapidly, including exquisite and rapid predictions about their dynamical behavior. Proteins, like any other mechanical object, respond to forces in peculiar ways. Think of the different response you'd get from pulling on a steel cable versus pulling on a rubber band, or the difference between honey and glass. Now, we can design proteins with a set of desirable mechanical characteristics, with applications from health to sustainable plastics. The key to solving this problem was to integrate a protein language model with denoising diffusion methods, and using accurate atomistic-level physical simulation data to endow the model a first-principles understanding. ForceGen can solve both forward and inverse tasks: In the forward task, we can predict how stable a protein is, how it will unfold and what the forces involved are, all given just the sequence of amino acids. In the inverse task, we can design new proteins that meet complex nonlinear mechanical signature targets. Read the paper, led by LAMM@MIT postdoc Bo Ni, published in Science Advances: Why do we care about the mechanics of proteins? The mechanics of proteins are critical elements of many living systems - as evidenced in many studies of mechanobiology. Through evolution, nature has presented a set of remarkable protein materials with unique mechanical functions like elastins, silks, keratins or collagens that play crucial roles in biology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. So far, the only way to do this was to use existing evolutionary concepts or to manually alter proteins. With our new generative model we can directly design proteins to meet complex nonlinear mechanical property-design objectives. ForceGen leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation from physical and chemical principles, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, and a detailed unfolding force-separation curves. ForceGen offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, to enable the discovery of new protein materials with superior mechanical properties. B. Ni, D.L. Kaplan, M.J. Buehler, ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model. Sci. Adv. 10, eadl4000 (2024). DOI: 10.1126/sciadv.adl4000 Codes and model weights available Hugging Face: David Kaplan

Markus J. Buehler

47,242 görüntüleme • 2 yıl önce