ProteoArk is a web-based tool that offers a range of computational pipelines for comprehensive analysis and visualization of mass spectrometry-based proteomics … Source: ACS Publications search.app/PQ59uLoSgMTeFNa99

Building calcium channels from scratch, new from the Baker lab blog! bakerlab.org/2025/10/23/buil…

What’s hot in #MassSpectrometry this month? 🔬 Explore the most-read @J_ASMS articles — the research everyone’s talking about. bit.ly/42Mr6B3 #ACSMostRead #ACSEditorsChoice #JASMS

The genetic code is full of synonymous codons that, for decades, were assumed to be interchangeable. Today, with @NVIDIA, co-led by @genophoria, we announce CodonFM, a family of open-source AI models that reveal the grammar underlying codon choice: developer.nvidia.com/blog/in…

Insights, opportunities, and challenges provided by large cell atlases🤗 genomebiology.biomedcentral.…

🎉 We're excited to announce the 2025 Google PhD Fellows! @GoogleOrg is providing over $10 million to support 255 PhD students across 35 countries, fostering the next generation of research talent to strengthen the global scientific landscape. Read more: goo.gle/43wJWw8

Predicting protein-protein interactions in the human proteome Predicting which human proteins shake hands—and how—is a longstanding bottleneck. Proteins rarely act alone; they assemble into complexes that drive immunity, metabolism, signaling, and disease. But testing hundreds of millions of possible pairs experimentally is slow, expensive, and blind to many weak or transient interactions. Jing Zhang, Qian Cong, David Baker and coauthors tackle this with a smart AI + data pipeline. First, they amplify evolutionary “clues” by assembling omicMSAs—deep multiple sequence alignments mined from petabytes of raw eukaryotic genomic data—so coevolution across species pops out. Second, they train a fast interaction model, RoseTTAFold2-PPI, not just on scarce complex structures, but on domain–domain contacts distilled from ~200M AlphaFold monomers—a huge synthetic training set that teaches the network what real interfaces look like. The payoff is big: a proteome-scale screen over ~200M human pairs yields ~18,000 PPIs at ~90% precision (and ~29k at 80%), including ~3,600 not previously reported. The method excels on transmembrane interactions, a class that’s notoriously hard in the lab, and produces 3D complex models—so you don’t just get a yes/no, you see the interface. Mapping human variants onto these models flags ~4,950 PPIs with disease mutations at the contact surface, offering concrete hypotheses for mechanism. Beyond pairs, the team reconstructs higher-order assemblies and nominates new components for well-studied complexes (e.g., telomere maintenance, GPI-GnT, cilia/flagella machinery), and highlights GPCR partners and mitochondrial modules that have been hiding in plain sight. Stepping back: this is a credible path toward a computed 3D human interactome—faster, cheaper, and increasingly comprehensive as more genomes and structures arrive. It doesn’t replace experiments; it prioritizes them, focusing bench time where the biology is richest. Paper: science.org/doi/full/10.1126…

Today, we’re announcing a major breakthrough that marks a significant step forward in the world of quantum computing. For the first time in history, our teams at @GoogleQuantumAI demonstrated that a quantum computer can successfully run a verifiable algorithm, 13,000x faster than leading classical supercomputers. This continues to build momentum on past quantum computing discoveries. Back in 2019, we proved a quantum computer could solve a problem that would take a classical computer thousands of years. Then in 2024, our new Willow chip solved a major issue in quantum error correction that challenged the field for nearly 30 years. Today’s breakthrough moves us closer to quantum computers that can drive discoveries in areas like medicine and materials science.

The virtual cell is no longer a dream — it’s becoming reality. Great @TIME piece on how AI is transforming biology from data-driven to model-driven science. Our work on scGPT and BioReason laid early foundations for this vision, and at @Xaira_Thera , we’re now building next-gen virtual cell models that reason across DNA, RNA, and proteins. The goal: true biological intelligence. 🔗 time.com/7324119/what-is-vir…

Review of IL-1 family cytokines in inflammation and immunity - including, IL-1, IL-18, IL-33, IL-36, IL-37, IL-38, IL-1Ra, etc. Full read -- Good Reference!! buff.ly/jmT18rl

The Lipid Brain Atlas is out now! If you think lipids are boring and membranes are all the same, prepare to be surprised. Led by Luca Fusar Bassini with Gioele La Manno's lab, we mapped membrane lipids in the mouse brain at high resolution. biorxiv.org/cgi/content/shor…

BREAKING NEWS The 2025 #NobelPrize in Physiology or Medicine has been awarded to Mary E. Brunkow, Fred Ramsdell and Shimon Sakaguchi “for their discoveries concerning peripheral immune tolerance.”

Just out in the New England Journal of Medicine! Our comprehensive Review on MGUS: Monoclonal Gammopathy of Undetermined Significance @NEJM nejm.org/doi/full/10.1056/NE… 5% of people over age 50 have MGUS. Every physician needs to know and understand MGUS. Lots of Tables and Figures. Bookmark! How to test and manage patients. Who needs bone marrow exams and scans. It’s all in here! MGUS is important not just because it’s a precursor to multiple myeloma. It causes a lot of other problems. Learn all about MGUS, and the various terms you hear MGCS, MGRS etc. in this Review @myelomaMD and I have tried to keep every sentence in this Review simple and easy to follow. #MGUSVR

With 𝗗𝗜𝗔-𝗡𝗡 𝟮.𝟯.𝟬 Preview (Academia-only for now), we showcase the transformative new capabilities that have been developed in the past months. Download: github.com/vdemichev/DiaNN/r…

Microsoft just dropped a 35‑page report on medical AI. Hope it’s useful.

Thrilled to share that today we report our human protein interactome in @ScienceMagazine! Predicting which pairs of proteins in the human proteome is a great challenge due to the shallow eukaryotic evolutionary signals but hugely important to understand. science.org/doi/10.1126/scie…

Over the past week, @arcinstitute published three new discoveries that I’m very proud of. • The world's first functional AI-generated genomes. Using Evo 2 (the largest biology ML model ever trained, which Arc released in partnership with @nvidia in February), Arc scientists took advantage of the fact that Evo 2 is a generative model to produce completely new sequences for complete phage genomes. That is, they used AI to produce wholly new, never-before-seen-by-nature genomes. They experimentally synthesized these genomes and showed that these AI-generated phages actually work, killing E. coli bacteria with high efficacy. • Germinal, an AI system for creating new antibodies. Antibody design is one of the great problems of medical biology given their obvious importance and usefulness for creating therapeutics. (Antibodies are tiny particles that help the immune system identify pathogens and other harmful intruders. See also the recent Works in Progress article on this topic: [1].) Today, designing effective antibodies is very expensive and slow. Germinal is a cheap and fast way to produce drug candidates, with success rates of up to 22%. This means that one can go from having to screen thousands of candidates in the lab to screening perhaps a few dozen. It's early, but I suspect that better methods for designing antibodies will be a very big deal for disease treatment in the coming years. • Today, we published a paper showing that “bridge editing”, which Arc scientists first introduced last year, can make precise edits in human cells that are up to 1 million base pairs long, and without relying on intrinsically unpredictable cellular repair machinery (which CRISPR requires, often leading to editing mistakes). They showed that it’s possible to use this editing to cut out the DNA repeats that cause Friedreich’s ataxia (a neurological disease), an approach which should also be relevant to Huntington’s and other similar disorders. One particularly cool thing about it is that it’s possible to specify every nucleotide within the extended editing window, meaning that recursive bridge edits could potentially be a powerful way to reprogram even biological traits that are caused by many genetic mutations. (Genetic therapies today target single mutations.) Arc is pretty new. Its doors opened in mid 2022, and it's now 300 people. I’m excited about these discoveries because they show that a number of our hopes in starting Arc are starting to pay off: • AI/ML and computation are at the center of all three. That is obviously true for the first two, but the mobile genetic element behind bridge editing was also discovered as a result of a complex computational search. One of our premises in starting Arc was the belief that the intersection of software/AI and experimental wet lab biology should enable great things. (And besides requiring great computational work, all three of these also required strong wet lab work, tightly coordinated under a single physical roof.) • We’ve been toying with the idea that a handful of technologies are enabling a new kind of “Turing loop” in biology: sequencing advances (including single-cell sequencing) give us new ways to read; transformers and AI gives us new ways to think; and functional genomics (such as bridge editing) give us new ways to ways to write. This trio of discoveries span each part of this loop, and we’re hopeful that there’ll be compounding returns in improving each part. • Arc is a non-profit, which we hoped would make collaborating with others easier, since we can avoid worries about financial return. This is indeed proving important, and all three of these projects involved close partnership with others. Germinal was done in partnership with @SynBioGaoLab at Stanford; Evo 2 was trained in partnership with Nvidia. Bridge editing was jointly published with a structure from the @HNisimasu Lab at the University of Tokyo. Arc tries to make its discoveries useful (see the Evo 2 Designer[2]) for others, and the code behind the computational projects is open source, hopefully making it easy for others to spot new opportunities for collaboration and partnership in the future. Most of all, Arc itself is an ongoing collaboration with @UCSF, @UCBerkeley, and @Stanford. • With Arc, we wanted to enable better bottom-up and top-down work. With the fully flexible, no-strings-attached funding that we provide to investigators, we want to enable completely unexpected discoveries and avenues of investigation. With our institute initiatives (around creating a virtual cell and curing Alzheimer’s), we want to bring to bear a scale and level of coordination that’s usually difficult in basic science. Germinal is a “surprise” discovery that didn’t involve top-down coordination, whereas Evo 2 is the result of ambitious high-level planning and funding. • Humanity has never cured a complex disease (a category that includes most neurodegenerative diseases, most cancers, and most autoimmune diseases), and my hope is that Arc can help change this. It’s also clear that AI will revolutionize biology, and I hope that Arc can effectively aggregate the ingredients needed to fully capitalize on its promise. I’m biased, but I think some of the coolest biology in the world is currently being done at Arc. (They’re always hiring if you’re interested.) While I’m a cofounder of Arc, I spend almost all my time on Stripe, where we spend our time building economic infrastructure for the internet. All credit for Arc’s progress should go to the remarkable scientists and staff who’ve made Arc their home or who’ve chosen to collaborate with us. (You can read more about these particular discoveries in these threads: [3], [4], [5].) I’m also very grateful to the amazing Stripe employees who’ve built the company that makes Arc’s ongoing work possible, and to the millions of customers who’ve chosen to partner with Stripe. John and I feel fortunate to be able to support Arc’s work to the extent that we do. Maybe this is reading too much into it, but I sometimes feel that there’s a commonality between @arcinstitute and @stripe. Both biology and economic infrastructure involve reasoning about complex systems with many levels of emergent effects, and in both cases building the right tools can have almost unboundedly large benefits. Even though progress in both tends to take a long time, it also feels like the next five years in both will be some of the most interesting in living memory. (If economic infrastructure is your jam, we have a whole slew of fantastic announcements coming up at Stripe Tour in New York next week. Tune in!)

Huntington's disease has been successfully treated for the first time, doctors tell BBC bbc.in/46jnMiX

Trump administration to appoint @DanaFarber oncologist @DrTonyLetai to run National Cancer Institute (@theNCI) cnn.com/2025/09/24/politics/…

What's the difference between biological aging (body and organ clocks) vs chronological age? How can the former be useful clinically? My perspective @sciam, Q&A w/ @LaurenYoung scientificamerican.com/artic…

Reengineering the antigen optimization process for superior neoantigen vaccine design biorxiv.org/content/10.1101/… #biorxiv_bioinfo