We just published: Fold on Tight — 10 lessons on AI structure prediction Andy Posbe @adnroide turned our ten lessons on AI structure prediction into a thread. Here it is, one lesson at a time. kamounlab.medium.com/fold-on…

Do I sense panic?

Our first protein 🥩 design paper! 🤠 Check out Minkyu's tour de force on T-REX 🦖, an agentic protein design controller for de novo binder design! TLDR we treat protein design as an online allocation problem over heterogeneous tools (Proteina-complexa, Boltzgen, Bindcraft, ✨ etc), use 🤖 agents to reason over what tools + settings to run, designed a granular classification system to manage the agents (e.g. Rescue🛟/Explore🔎/eXploit🚀), that is all orchestrated with a deterministic controller to efficiently brrr the GPUs. 💸 T-REX 🦖 is fully open source: github.com/ml-struct-bio/T-R… Preprint: biorxiv.org/content/10.64898… Please let us know if you have any feedback!

What's the opposite of genocide? theguardian.com/us-news/2026…

Âgé de seulement 17 ans, le Japonais Shin Ohashi a pulvérisé, ce jeudi, le record du monde du 200m brasse aux Jeux asiatiques. mrf.lu/2mt9c

WHAT JUST HAPPENED?!?! Shin Ohashi just CRUSHED the 200 Breaststroke WORLD RECORD with a 2:04.83 🤯🤯🤯 SPLITS: 28.27 31.62 32.51 32.43

🛡️ Pandemic preparedness starts before an outbreak. 🛡️ 🧬 The latest AlphaFold Database update gives researchers open access to more AI-predicted protein structures to support vaccine and treatment research across more than 2,800 viruses. 🤝 @NVIDIAHealth and global partners at @embl, @GoogleDeepMind, @SeoulNatlUni, the @UofGlasgow , @ISBSIB, @CEPIvaccines, and @skkuintl came together to build this resource using tools including #BioNeMo Inference Runtime. Read more 👇

Don't miss out! Fantastic opportunity to get your favorite plant-associated microorganism sequenced FOR FREE courtesy of our sponsor @GetGenome!

Very good point.

Talks for early career researchers kamounlab.dreamhosters.com/K…

Sam Altman is warning the world about Sam Altman, and says we need to stop Sam Altman because Sam Altman can’t stop Sam Altman.

Hmm. Why are journalists who have spent a lifetime working for one billionaire so keen to obscure the real reason for the Ed Sheeran furore: that the musician caved to censorship demanded by another billionaire? I explain what's really going on here: jonathancook.substack.com/pu…

Without a mechanism, I’m struggling to understand the importance of this. Host-pathogen interactions have been a rich source of biological tools for decades; it’s where CRISPR and restriction enzymes and Agrobacterium came from. But the way we get these tools to be great, and useful, is by understanding their mechanism such that we can begin engineering and improving them. I’m sure AI tools will be helpful for molecular characterization and engineering; so why not wait to announce until you actually do that?

Wild stuff. Anthropic’s new wet lab got its first win after 949 Claude agents autonomously tracked down a new enzyme system in just 21 hours. Claude was searching a huge amount of genetic data when it noticed an unusual pattern: the same small piece of DNA repeating again and again next to an enzyme. The campaign searched 1.94B protein clusters, recovered 198,290 RT clusters, and used 215.6M tokens across 21.5 hours. That pattern looked a little like CRISPR, so Anthropic’s scientists investigated it and found a new family of biological systems they call ART. The system also produces many small RNA molecules, suggesting those repeated DNA sections are doing something useful rather than sitting there randomly.

Today we announced the Claude-led discovery of a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of significance is not yet clear, but at minimum it is work I would have been proud to do as a PhD student. The work was done mostly, though not entirely, by Claude: our life sciences team suggested a broad area of research, Claude read through the literature and a bunch of genome data and discovered something interesting, then Claude proposed experiments to verify the discovery and our team carried them out. It’s easy to dismiss this as a one-off or curiosity, but we’ve repeatedly seen a pattern where AI performance in new intellectual domains goes from weak to superhuman in a matter of a few years. In 2023 models struggled to do math at the level of an average high-school student. In 2024 they started to do well on math competitions for the best high-schoolers in the country, in 2025 they started to solve minor open problems, in early 2026 more significant open problems, and in late 2026 they are beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend. The main difference between biology and mathematics, of course, is that math can be done purely theoretically, while biology requires experimentation. Some have used this to draw the conclusion that AI’s utility in biology will be limited. We think this is wrong. As we’ve demonstrated today, humans can collaborate with AI to perform the experiments, validate key results in a few weeks and, if necessary, work with the AI to iterate on what they find. Eventually it may even be possible for Claude itself to safely perform the experiments by autonomously controlling lab equipment, with appropriate safeguards in place, but we aren’t doing that today (our lab is also a BSL1/BSL2 facility that doesn't handle materials dangerous to humans). More broadly, biomedical advancement has many stages — from fundamental biology discoveries, to translational research, to drug discovery, clinical trials, and finally the actual delivery of medicines and health care to patients. We are also interested in these later stages, but even simply accelerating the first stage of fundamental biological discoveries has the potential to speed up and broaden the entire pipeline. Improving our understanding of biology and sharpening biologists’ tools can drive forward all of the later stages, for example by identifying new drug targets, finding new therapeutic modalities, allowing for more precise measurement, and speeding up the experimental loop which itself further accelerates our understanding of biology. This will not in itself speed up clinical trial times, but if it succeeds it could greatly increase the number of promising candidates that go into the pipeline — an increase in throughput even though latency remains. In Machines of Loving Grace, I wrote about AI’s potential to “cure most diseases in 5-10 years” — a goal that sounds impossible, but one I believe is just barely possible if AI is applied to every stage of the pipeline. The first step is showing that AI can first help with, and then drive, biological discoveries. Claude’s discovery is the latest in a line of related prior work that goes back decades, beginning with systems like CRISPR, and continuing with discoveries like the bridge recombinase and VIPR in the past few years. Recently, there has been heightened interest in systems based on reverse transcriptase (RT) enzymes, the enzyme underlying the system Claude identified. And most recently, a Stanford team working independently described a novel RT system with an associated non-coding array that is in some ways similar to the one Claude found, though they are distinct systems that evolved independently from each other. I believe that we’re at the very beginning of finding such systems and developing them into powerful tools for biotechnology. I’m proud of the resources Anthropic has invested in accelerating the public benefits of AI through the life sciences, and we’re aiming both to grow our life sciences team and to work with other scientists to extend this approach to a broad range of problems. If you have a proposal for a research collaboration or are interested in joining our life sciences team, please reach out.

#GGRoadToJeju2026 invites applications from the global research community to sequence the genomes of plant-associated microorganisms — including pathogenic and non-pathogenic bacteria, fungi and oomycetes. 📅 Apply by 2 November 2026 getgenome.net/callforproject…

You select the isolates, we sequence the genomes — for free! 🧬 Inspired by @ISMPMI #MPMI2027, we’re pleased to announce that the latest @GetGenome Call for Projects is now open! @GetGenome “Road to Jeju” 2026 getgenome.net/callforproject…

The rice blast fungus kills enough rice each year to feed about 60 million people (up to 30% of total yield.) This talk, by Nick Talbot, is what first got me so interested in this pathogen. He shows videos of the fungus "exploding" to enter plant leaves, and then wending its way through the plant cells. Mesmerizing. piped.video/watch?v=Kyz5hobm…

Nature Structural & Molecular Biology: Engineering protein pores beyond evolution A new study shows how scientists are moving from modifying natural proteins to building programmable molecular machines. By integrating a de novo designed component into the bacterial CsgG nanopore scaffold, researchers created semisynthetic conducting protein pores with programmable architecture and tunable ion-conduction properties. This represents a shift in protein engineering: From → predicting structures → understanding biological systems Toward → designing functional proteins with desired behaviors. The future of protein design may not only be discovering what nature has made — but engineering what nature never created. 🔬 Sculpting semisynthetic conducting protein pores by de novo design 📖 Nature Structural & Molecular Biology (2026) nature.com/articles/s41594-0…