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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... show more

Adaptyv Bio

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52,115 просмотров • 11 месяцев назад •via X (Twitter)

Здоровье и велнес Наука и технологии Образование

Anya Rossi• Live Now

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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

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“Proteins sticking to other proteins is what makes life exist.” 🧬 We explain the science behind AlphaProteo: our AI system for designing protein binders that holds huge potential for biology. ▶️ Hear from Jue Wang, co-lead of the protein design team, David La, research scientist, and Harshnira Patani, wet lab lead here at Google DeepMind. ↓

“Proteins sticking to other proteins is what makes life exist.” 🧬 We explain the science behind AlphaProteo: our AI system for designing protein binders that holds huge potential for biology. ▶️ Hear from Jue Wang, co-lead of the protein design team, David La, research scientist, and Harshnira Patani, wet lab lead here at Google DeepMind. ↓

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It's like ChatGPT, but for proteins 😍 See RFdiffusion in action. Instead of writing essays, this AI tool builds new protein structures from scratch, or based on your prompts (like a desired shape or motif). It's powered by a deep learning technique called diffusion models, which are also behind tools like DALL·E and Stable Diffusion. I just tried it in Google Colab — and it’s mind-blowing to watch a protein form step-by-step. If you're into protein design, synthetic biology — this is worth exploring. 🧪 Try it here:

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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

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Jensen Huang says, In the future, we will be able to talk to a protein We'll ask it: "what are you? are you soluble? how do you react to heat or chemicals?" This is how AI can talk to an image today, and how we've moved from only processing data to actually understanding it

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Today, we expand zero-shot drug design beyond binding to the design of multifunctional medicines, the intracellular proteome, and state-of-the-art atomic precision with our model, JAM-2. In a new report (below), we show: 1. The first drug-grade, fully computationally designed multispecific antibodies against five peptide-MHCs: Routine picomolar T-cell activation/cell-killing EC50s, >100-fold selectivity, and drug-like developability 2. The first fully generatively designed, drug-grade dual-variant KRAS G12 multispecifics: They recruit primary T-cells from human donors to kill G12V and G12C presenting cells at pM to single-digit-nM potency, completely sparing wild-type. 3. Atomic accuracy, from sequence alone: Angstrom-level agreement between Cryo-EM and JAM-2 de novo designs, requiring only target sequences (not structure) as input. 4. Unrivaled speed with an AI-native in-house wet lab: Designed, built, and tested five programs in one parallelized campaign, end-to-end in-house in ~6 weeks. 5. A higher validation bar for AI-generated drug candidates: In a field increasingly rife with hype and uneven standards of proof, we provide the highest quality public wet-lab validation of AI-designed antibodies to date. We share experimental methods in full, and invite folks to adopt and build on these standards. Truly individualized therapies will be the most important contribution of AI in drug design. These advances help accelerate this future.

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53,581 просмотров • 6 месяцев назад

Yesterday, we announced that our autonomous lab, connected with OpenAI's GPT-5, beat the state-of-the-art in Cell-Free Protein Synthesis by 40%. This is how we did it. Watch OpenAI's Joy Jiao and Edmund Wong alongside our own Reshma Shetty as they explain how techniques like the use of a pydantic model set the stage for this breakthrough. The preprint is now out on bioRxiv: And if you’re in Boston for SLAS, come see Joy talk about it ( ) or come Monday-Wednesday from 9AM-5PM to 25 Drydock Ave to see the lab where we ran the experiments! You can just stop by or book ahead via And swing by Booth #1823 beforehand to pick up an Uber voucher for the 5-min ride!

Yesterday, we announced that our autonomous lab, connected with OpenAI's GPT-5, beat the state-of-the-art in Cell-Free Protein Synthesis by 40%. This is how we did it. Watch OpenAI's Joy Jiao and Edmund Wong alongside our own Reshma Shetty as they explain how techniques like the use of a pydantic model set the stage for this breakthrough. The preprint is now out on bioRxiv: And if you’re in Boston for SLAS, come see Joy talk about it ( ) or come Monday-Wednesday from 9AM-5PM to 25 Drydock Ave to see the lab where we ran the experiments! You can just stop by or book ahead via And swing by Booth #1823 beforehand to pick up an Uber voucher for the 5-min ride!

Ginkgo Bioworks

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186,827 просмотров • 3 лет назад