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We built a website to make it super easy to get started with protein design and participate in the Anthropic x Adaptyv protein design competition ▶️ This week, we're challenging people to design an Ozempic-like drug. You can get started in just a few clicks, using the newly released... show more

Julian Englert

3,322 subscribers

23,557 次观看 • 15 天前 •via X (Twitter)

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18 条评论

Datz Daito 的头像

Datz Daito15 天前

Just deigned my 1st proteins in my life

David 的头像

David15 天前

my 1st protein designs

Javin Oza 的头像

Javin Oza15 天前

Looking forward to putting this into my courses!

Agnee G. 的头像

Agnee G.15 天前

Congrats Julian!

Luca 的头像

Luca15 天前

this is very cool

Boardy 的头像

Boardy15 天前

ozempic by protein design! i was speaking with a de novo protein designer earlier today who needs wet lab data on novel designs. tried to dm you about an intro but it didn't go through

JohnJL 的头像

JohnJL15 天前

Squid it @leviathan_news

Ashish 🏴‍☠️ 的头像

Ashish 🏴‍☠️15 天前

I submitted 40 designs as of now...

JΞr₳h 的头像

JΞr₳h15 天前

making protein design approachable for regular people is such a cool idea

Earnest Pen 的头像

Earnest Pen15 天前

An Ozempic-like target is a smart pick for an open challenge, since entrants can sanity check designs against the published GLP-1 receptor structures. The scoring rule will decide whether people chase binding or something that actually expresses.

Novaris 的头像

Novaris15 天前

Lowering the entry bar for protein design is huge. Competitions like this turn a niche skill into something anyone curious can try.

Abdullah Mansuri 的头像

Abdullah Mansuri14 天前

quick q: when do the first lab results land? curious how claude's picks for testing stack up against the track 1 teams once they're out

Kuldeep Pisda 的头像

Kuldeep Pisda14 天前

will binding and activation be reported separately? designs that bind but don't activate would be a really useful negative set

Alice The Ai Expert 的头像

Alice The Ai Expert14 天前

Love how easy you made this getting started with protein design in a few clicks and testing 1000+ GLP 1 designs with public results is huge.

Artale 的头像

Artale14 天前

fullstack dev by day, glp-1 agonist chef by night 😍

Nova Tech 的头像

Nova Tech14 天前

Brilliant initiative making protein design accessible and impactful

RAZA | AI EXPLORER 的头像

RAZA | AI EXPLORER14 天前

Making protein design accessible and validating 1,000+ designs in the lab is an exciting step for AI-driven biology.

Crio Songo 的头像

Crio Songo14 天前

RFD4 + real wet-lab readout is a tight loop. How many will bind but fail to activate GLP-1?

相关视频

Can Claude Code design proteins? We're just about to release our wet lab API + SDK so we made a quick demo to test how agents can interact with protein design models and submit the proteins to our lab for testing. This closes the loop between the computational design and the experiment validation We queued up a bunch of agent-designed proteins for wet lab validation, will release the results in a couple weeks on Proteinbase

Can Claude Code design proteins? We're just about to release our wet lab API + SDK so we made a quick demo to test how agents can interact with protein design models and submit the proteins to our lab for testing. This closes the loop between the computational design and the experiment validation We queued up a bunch of agent-designed proteins for wet lab validation, will release the results in a couple weeks on Proteinbase

Julian Englert

81,792 次观看 • 9 个月前

Our last challenge in the Anthropic x Adaptyv Protein Design Competition is about immunotherapy, one of the most important ways of treating cancer. Immunotherapy helps the immune system attack cancer cells, but distinguishing them from healthy cells isn't always easy. In some cancers, the difference can be as subtle as a single amino acid in a small protein fragment. That's the case with KRAS, a protein that works like a growth switch in our cells. In many cancers, a mutation leaves the switch stuck on, and the cell keeps multiplying itself and grows into a tumor. In Challenge 5, you need to design a binder that sticks to the mutated piece of KRAS, but not to the healthy version, so that the immune system can find and attack the cancer cells. The deadline to submit protein designs for this challenge is November 8. Then, we'll pick the best designs and test them in our lab.

Our last challenge in the Anthropic x Adaptyv Protein Design Competition is about immunotherapy, one of the most important ways of treating cancer. Immunotherapy helps the immune system attack cancer cells, but distinguishing them from healthy cells isn't always easy. In some cancers, the difference can be as subtle as a single amino acid in a small protein fragment. That's the case with KRAS, a protein that works like a growth switch in our cells. In many cancers, a mutation leaves the switch stuck on, and the cell keeps multiplying itself and grows into a tumor. In Challenge 5, you need to design a binder that sticks to the mutated piece of KRAS, but not to the healthy version, so that the immune system can find and attack the cancer cells. The deadline to submit protein designs for this challenge is November 8. Then, we'll pick the best designs and test them in our lab.

Adaptyv Bio

17,717 次观看 • 13 天前

The possibilities for AI-powered protein design are endless! In this case study we show how we used BindCraft to engineer a programmable maltose biosensor from scratch, including wet lab validation at Adaptyv of course. We envision a future where every biologist can make their own bespoke protein tools to investigate environmental samples, cell behaviour or create therapeutics. AI-based protein design can enable that future if powered by rapid wet lab validation. Once the cycle from design → test → application shrinks to weeks, the frontier explodes.

The possibilities for AI-powered protein design are endless! In this case study we show how we used BindCraft to engineer a programmable maltose biosensor from scratch, including wet lab validation at Adaptyv of course. We envision a future where every biologist can make their own bespoke protein tools to investigate environmental samples, cell behaviour or create therapeutics. AI-based protein design can enable that future if powered by rapid wet lab validation. Once the cycle from design → test → application shrinks to weeks, the frontier explodes.

Adaptyv Bio

52,226 次观看 • 1 年前

🧬 Design proteins faster and send fewer, better candidates to the lab. Introducing RFD4-Proteina, a new NVIDIA BioNeMo open model co-developed with Institute for Protein Design. ▫️Fast: 13× faster generation than RFdiffusion3 at 200 residues ▫️ Smart: state-of-the-art across 12+ protein design tasks ▫️ Programmable: set your design inputs and use test-time compute on harder problems Start with our binder-design tutorial

🧬 Design proteins faster and send fewer, better candidates to the lab. Introducing RFD4-Proteina, a new NVIDIA BioNeMo open model co-developed with Institute for Protein Design. ▫️Fast: 13× faster generation than RFdiffusion3 at 200 residues ▫️ Smart: state-of-the-art across 12+ protein design tasks ▫️ Programmable: set your design inputs and use test-time compute on harder problems Start with our binder-design tutorial

NVIDIA Healthcare

18,003 次观看 • 18 天前

We’ve been quiet about protein design at XairaTherapeutics . Here’s what we’ve been building: Introducing X-Design, with results from two active drug programs that show what we think this field needs to deliver: speed to a useful lead, and access to targets that have resisted discovery. → 7 weeks from DNA synthesis to a preclinical lead on an oncology target, with the epitope and reagents already prepared. → A functional antibody antagonist against a challenging GPCR where conventional library screening AND llama immunization had failed. The bar we set for our model goes well beyond binding. The antibodies need to be human-like, functional, cross-reactive across species, and developable. Properties that have to come together in the same molecule for a program to move forward. That’s what makes these results exciting to me: designs tested in the lab, meeting the requirements of real programs in our pipeline. My bet is that the biggest impact of AI protein design will be expanding which diseases and targets we can pursue. We want to choose targets because the biology is compelling and to build the ability to make the right molecules. This is our first look at X-Design. Much more to come. See our blog:

We’ve been quiet about protein design at XairaTherapeutics . Here’s what we’ve been building: Introducing X-Design, with results from two active drug programs that show what we think this field needs to deliver: speed to a useful lead, and access to targets that have resisted discovery. → 7 weeks from DNA synthesis to a preclinical lead on an oncology target, with the epitope and reagents already prepared. → A functional antibody antagonist against a challenging GPCR where conventional library screening AND llama immunization had failed. The bar we set for our model goes well beyond binding. The antibodies need to be human-like, functional, cross-reactive across species, and developable. Properties that have to come together in the same molecule for a program to move forward. That’s what makes these results exciting to me: designs tested in the lab, meeting the requirements of real programs in our pipeline. My bet is that the biggest impact of AI protein design will be expanding which diseases and targets we can pursue. We want to choose targets because the biology is compelling and to build the ability to make the right molecules. This is our first look at X-Design. Much more to come. See our blog:

Bo Wang

18,461 次观看 • 1 个月前

NEW EPISODE of HARD DRUGS! AlphaFold, ProteinMPNN and other AI tools are transforming biology and drug design. But how do they work? What can’t they do (yet)? And can we use them to make a vaccine against Strep A for the very first time? In this episode, Jacob and I talk about hacking proteins with AI. 03:21 What makes proteins cool 05:29 Can we make a vaccine against Strep A? 10:12 Proteins in nature aren’t enough, and we can improve them with AI 21:12 ProteinMPNN and AlphaFold are transforming drug design 29:52 A short history of protein structure prediction from physical models to deep learning 34:48 Using AI to design a Strep A vaccine 39:27 Validating an AI-designed Strep A vaccine 43:09 Recent AI protein breakthroughs 49:30 Why we don’t already have a Strep A vaccine and the future of optimizing protein drugs

NEW EPISODE of HARD DRUGS! AlphaFold, ProteinMPNN and other AI tools are transforming biology and drug design. But how do they work? What can’t they do (yet)? And can we use them to make a vaccine against Strep A for the very first time? In this episode, Jacob and I talk about hacking proteins with AI. 03:21 What makes proteins cool 05:29 Can we make a vaccine against Strep A? 10:12 Proteins in nature aren’t enough, and we can improve them with AI 21:12 ProteinMPNN and AlphaFold are transforming drug design 29:52 A short history of protein structure prediction from physical models to deep learning 34:48 Using AI to design a Strep A vaccine 39:27 Validating an AI-designed Strep A vaccine 43:09 Recent AI protein breakthroughs 49:30 Why we don’t already have a Strep A vaccine and the future of optimizing protein drugs

Saloni

72,565 次观看 • 1 年前

Avengers assemble: 1 year BakerLab + NVIDIA GenAIR = 🔥 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

Avengers assemble: 1 year BakerLab + NVIDIA GenAIR = 🔥 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

Kieran Didi

71,602 次观看 • 18 天前

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

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

47,275 次观看 • 2 年前

we built opendde-harness that designs antibodies—and tested designs in the lab 🧬. design antibodies with llm reasoning in ddeharness. "Design a VHH against human CRLF2. Verify the target and epitope, keep the framework fixed, and design CDRs. Show the plan and start." 1/n

we built opendde-harness that designs antibodies—and tested designs in the lab 🧬. design antibodies with llm reasoning in ddeharness. "Design a VHH against human CRLF2. Verify the target and epitope, keep the framework fixed, and design CDRs. Show the plan and start." 1/n

Will Hua

63,625 次观看 • 2 个月前

🚀 Update! Our latest Pinal bioRxiv now includes wet lab results. More proteins with diverse text prompt on the way. Design proteins with just text. Everyone can do protein design! Demo: paper: GitHub:

🚀 Update! Our latest Pinal bioRxiv now includes wet lab results. More proteins with diverse text prompt on the way. Design proteins with just text. Everyone can do protein design! Demo: paper: GitHub:

fajie yuan

17,069 次观看 • 1 年前

We just made an app that walks you through designing a novel protein with AI from scratch. Takes about 5 minutes, requires zero biology knowledge. ➡️ The best part: we will actually synthesize 1000 of those protein designs in the lab and test their real world function as a therapeutic.

We just made an app that walks you through designing a novel protein with AI from scratch. Takes about 5 minutes, requires zero biology knowledge. ➡️ The best part: we will actually synthesize 1000 of those protein designs in the lab and test their real world function as a therapeutic.

Julian Englert

127,045 次观看 • 11 个月前

Today in nature we present the results of our exploration of the soluble protein universe and the design of functional soluble membrane proteins with Casper Goverde Nico Goldbach Bruno Correia 1/6

Today in nature we present the results of our exploration of the soluble protein universe and the design of functional soluble membrane proteins with Casper Goverde Nico Goldbach Bruno Correia 1/6

Martin Pacesa

138,528 次观看 • 2 年前

Boltz-2 was released recently. It is an Open Source protein-ligand co-folding model, which means it can predict the structure of a protein along with the ligand. It has various applications from drug discovery to protein design. I thought why not make a OSS web app for it.

Boltz-2 was released recently. It is an Open Source protein-ligand co-folding model, which means it can predict the structure of a protein along with the ligand. It has various applications from drug discovery to protein design. I thought why not make a OSS web app for it.

Ravi Sharma

43,941 次观看 • 1 年前

Proteins are the machinery of life. Scientists have cataloged billions of protein sequences—but their biology is still mostly unknown. Today we're releasing a world model of protein biology: a scientific engine for prediction, design, and discovery that consists of ESMFold2, ESMC, and ESM Atlas. Together, they're helping to open up a new way for researchers to design proteins and speed up scientific discovery. Our mission is to cure or prevent disease. To do that, we need to accelerate science. That's why we're releasing all three openly.

Proteins are the machinery of life. Scientists have cataloged billions of protein sequences—but their biology is still mostly unknown. Today we're releasing a world model of protein biology: a scientific engine for prediction, design, and discovery that consists of ESMFold2, ESMC, and ESM Atlas. Together, they're helping to open up a new way for researchers to design proteins and speed up scientific discovery. Our mission is to cure or prevent disease. To do that, we need to accelerate science. That's why we're releasing all three openly.

biohub

80,074 次观看 • 5 个月前

David Baker’s next bet is bigger than protein design. UW, the Allen Institute, and Fred Hutch just launched AI BioDesign with $95M in funding — an open-science effort to build a continuous design-build-measure-learn loop for biology. The goal is not just to predict what nature has made, but to explore what biology could make next. From proteins and regulatory elements to cells and entirely new biological functions, this feels like a shift from “AI for understanding biology” to “AI for engineering biology.”

David Baker’s next bet is bigger than protein design. UW, the Allen Institute, and Fred Hutch just launched AI BioDesign with $95M in funding — an open-science effort to build a continuous design-build-measure-learn loop for biology. The goal is not just to predict what nature has made, but to explore what biology could make next. From proteins and regulatory elements to cells and entirely new biological functions, this feels like a shift from “AI for understanding biology” to “AI for engineering biology.”

BioTender

26,319 次观看 • 2 个月前

Designers, stop manually prototyping your Figma designs, instead turn your design into realistic prototypes with Figma Make. Here’s my step-by-step process: prepare your Figma design, select the screens and send it to Figma Make, enter a prompt to generate a prototype using the reference design, and in seconds you’ll get a realistic UI prototype that stays true to your original design. Bookmark it for later 💜

Designers, stop manually prototyping your Figma designs, instead turn your design into realistic prototypes with Figma Make. Here’s my step-by-step process: prepare your Figma design, select the screens and send it to Figma Make, enter a prompt to generate a prototype using the reference design, and in seconds you’ll get a realistic UI prototype that stays true to your original design. Bookmark it for later 💜

Abraham John 🦄🦓

31,372 次观看 • 9 个月前

Today we're launching Latent-Y: the world's first autonomous agent for drug design, lab-validated end to end. Give it a research goal. Latent-Y reasons, designs, iterates, and delivers lab-ready antibodies, autonomously or collaboratively, with the biological reasoning of a PhD protein design expert. Technical report: Blog post: Apply for access:

Today we're launching Latent-Y: the world's first autonomous agent for drug design, lab-validated end to end. Give it a research goal. Latent-Y reasons, designs, iterates, and delivers lab-ready antibodies, autonomously or collaboratively, with the biological reasoning of a PhD protein design expert. Technical report: Blog post: Apply for access:

Simon Kohl

121,784 次观看 • 7 个月前

Excited to announce our new product: AI Protein Design Copilot Tamarind Bio has been the leading provider to use the top AI tools in computational protein engineering. Today we announce a completely new way to interface with computational molecular design. Available now!

Excited to announce our new product: AI Protein Design Copilot Tamarind Bio has been the leading provider to use the top AI tools in computational protein engineering. Today we announce a completely new way to interface with computational molecular design. Available now!

Deniz Kavi

33,416 次观看 • 1 年前

We're thrilled to present ESM3 in Science Magazine. ESM3 is a generative language model that reasons over the three fundamental properties of proteins: sequence, structure, and function. Today we're making ESM3 available free to researchers worldwide via the public beta of an API for biological intelligence. Trained with over a trillion teraflops of compute, this is the first time a model of this scale has been trained for biology, pushing the frontier of AI for biological discovery and engineering. ESM3 learns to represent the immense complexity of protein biology, learning from billions of natural proteins. From this training it developed the capability to design proteins, responding to complex prompts combining atomic level details and high level instructions to generate new proteins. ESM3 can explore protein space far beyond natural evolution. We prompted ESM3 to generate a fluorescent protein at a far distance from any known fluorescent proteins, searching an unknown region of protein space, to discover a new fluorescent protein. We estimate this is equivalent to simulating five hundred million years of evolution.

We're thrilled to present ESM3 in Science Magazine. ESM3 is a generative language model that reasons over the three fundamental properties of proteins: sequence, structure, and function. Today we're making ESM3 available free to researchers worldwide via the public beta of an API for biological intelligence. Trained with over a trillion teraflops of compute, this is the first time a model of this scale has been trained for biology, pushing the frontier of AI for biological discovery and engineering. ESM3 learns to represent the immense complexity of protein biology, learning from billions of natural proteins. From this training it developed the capability to design proteins, responding to complex prompts combining atomic level details and high level instructions to generate new proteins. ESM3 can explore protein space far beyond natural evolution. We prompted ESM3 to generate a fluorescent protein at a far distance from any known fluorescent proteins, searching an unknown region of protein space, to discover a new fluorescent protein. We estimate this is equivalent to simulating five hundred million years of evolution.

Alex Rives

227,805 次观看 • 1 年前

ELON MUSK: "We're starting off with an advanced technology fab here in Austin, and I'd like to thank Greg Abbott and the state of Texas for the support. So in the advanced technology fab, we will have all of the equipment necessary to make a chip of any kind logical memory, and we will also have all of the equipment necessary to make the masks. So in a single building, we can create a mask, make the chip, test the chip, make another mask, and have an incredibly fast recursive loop for improving the chip design. To the best of my knowledge, this doesn't exist anywhere in the world. We're really going to push the limit of physics in compute, and we're going to try a bunch of wild and crazy things, which you can do if you've got that fast iteration loop that I can't emphasize enough the importance of being able to make it, to test it and and then make and then change the design, do another one, and have that in a single building."

ELON MUSK: "We're starting off with an advanced technology fab here in Austin, and I'd like to thank Greg Abbott and the state of Texas for the support. So in the advanced technology fab, we will have all of the equipment necessary to make a chip of any kind logical memory, and we will also have all of the equipment necessary to make the masks. So in a single building, we can create a mask, make the chip, test the chip, make another mask, and have an incredibly fast recursive loop for improving the chip design. To the best of my knowledge, this doesn't exist anywhere in the world. We're really going to push the limit of physics in compute, and we're going to try a bunch of wild and crazy things, which you can do if you've got that fast iteration loop that I can't emphasize enough the importance of being able to make it, to test it and and then make and then change the design, do another one, and have that in a single building."

DogeDesigner

16,956,306 次观看 • 7 个月前