Genomics technologist and bioinformatics hacker at the WEHI Advanced Genomics Facility. Views and opinions are my own.

Melbourne, Victoria
Joined August 2013
Tired of tedious RNA extractions for your transcriptomic studies? Introducing TIRE-seq, a novel protocol that streamlines bulk RNA-seq by integrating sample extraction with library preparation. The whole process can be completed in a day and a half. (1/6) biorxiv.org/content/10.1101/…
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Thrilled to share my freshly "baked" Baker lab paper. We took a bottom-up approach to designing transcriptional regulators. Instead of relying on natural activation domains, we designed compact proteins that recruit specific cofactors. Activators for now, repressors to come!
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Ever wondered what a cell has experienced in the past? This is in general hard to measure. Here we introduce the recordanelle, to equip cells with a tool that lets them record their own history. nature.com/articles/s41587-0… @NatureBiotech @ETH @randall_platt @ETH_BSSE
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Tally Portnoi and I are releasing zPocket, a pocket prediction tool based on co-folding features (MoML top-4 papers), with performance similar to the IsoDDE and AnewDDE pocket predictors but fully reproducible and open (when using ESMFold2). arxiv.org/abs/2610.17696
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Design and characterization of peptide-responsive biosensors doi.org/10.64898/2026.10.03.…
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Just out: our forward-looking essay on mRNA “delivery cells”—engineered cells that could locally manufacture mRNA at disease sites within the body and transfer it to target cells in extracellular vesicles.
Engineered cells as programmable mRNA delivery vehicles dlvr.it/TSBzt7
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Daniel V Brown retweeted
A small molecule turns an onco-protein into a polymer and thereby triggers its destruction. This striking route to targeted protein degradation is exemplified by BI-3802. It binds transcription factor B cell lymphoma 6 (BCL6) and induces its reversible assembly into filaments. The resulting supramolecular structure promotes ubiquitination by the SIAH1 E3 ligase and proteasomal degradation of BCL6. Cryo-EM revealed how the drug becomes part of the protein–protein interface that drives polymerization. 1/
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We’ve set up a molecular biology lab at Anthropic and we’re announcing our first discovery! Claude discovered a new CRISPR-like enzyme. 950 agents spent 21 hours searching through a database of DNA sequences until one of the agents found something striking: “[The DNA next to the RT] is spectacular: I can see by eye a tandem repeat array … that's a CRISPR-like … repeat array?!”. After analysis and testing in our lab, we found that the sequence is a previously uncharacterized enzyme system. We don’t know what it does yet, but it has features reminiscent of CRISPR. Our lab looks like a typical molecular biology lab. Our research only involves the lower-levels of biosafety risk level; we don’t handle pathogens that can infect humans, and all the lab work is performed by human scientists. We’re sharing these early findings with the community to show how Claude can be used to accelerate fundamental research in biology.
Claude has discovered a previously unknown enzyme system hidden in the DNA of bacteriophages. Beside the enzyme’s gene sits a long array of repeating DNA—a structure that looks somewhat similar to CRISPR. We don’t yet understand what this system does, but only a handful of known systems share its features, and all of them are able to cut, copy, and paste DNA. Historically, the discovery of such programmable systems has helped revolutionize medicine. CRISPR, for instance, is now the foundation of genetic medicines. But it will take much more work to learn what this system does, and whether it can be put to similar use. Read more: anthropic.com/news/claude-di…
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CRISPR screens are great at telling you what a gene is doing inside your cells. But what if you're interested in what's happening to its neighbors? Today, I'm excited to share match-seq, a new method that deciphers cell-cell interactions using barcoded RNA transfer. 1/9
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Cell–cell interactions are key to all biological processes, but there hasn't been a good way to perform inter-cellular screens for genetic regulators of these interactions in vivo. We developed match-seq (multiplexed associative tagging of cell interaction histories) - using engineered virus-like particles to transfer barcoded mRNAs from 'sender' to neighboring 'receiver' cells, so you can reconstruct cellular interactions with sequencing alone, no imaging required. We coupled match-seq with CRISPR + scRNA-seq in cancer, revealing cancer-immune interaction neighborhoods at scale, and new cancer genes that can be targeted for tumor rejection via immune cell interactions. Led by the brilliant @peterdu_ with Bassik lab! biorxiv.org/content/10.64898…
CRISPR screens are great at telling you what a gene is doing inside your cells. But what if you're interested in what's happening to its neighbors? Today, I'm excited to share match-seq, a new method that deciphers cell-cell interactions using barcoded RNA transfer. 1/9
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Let the generative model propose, let physics decide: finding rare molecular transitions Rare molecular events such as protein folding or membrane transport can take microseconds or longer, while most simulation time is spent waiting for something interesting to happen. Gen-COMPAS changes what the AI is asked to do. A diffusion model does not replace molecular dynamics. It proposes plausible intermediate structures where a transition might occur. Molecular dynamics then tests and refines those proposals under the actual molecular Hamiltonian. The loop becomes: generate candidates → test them with physics → keep the promising transition states → retrain → repeat. A learned committor focuses the search on structures that have roughly equal probability of falling into either end state, exactly where the rare transition is happening. For the Trp-cage protein, the workflow recovered the known folding landscape and two competing folding pathways using 594 ns of aggregate unbiased sampling, compared with a 208 μs conventional molecular-dynamics trajectory used for validation, about a 350× difference in trajectory length. The authors correctly caution that this is a sampling-budget comparison, not a direct wall-clock speedup. Don’t ask a generative model to replace the physics. Use it to decide where the expensive simulation should look next. That turns brute-force simulation into an iterative discovery loop in which AI proposes and physics validates. Tang et al., Nature (2026), journal license. doi.org/10.1038/s41586-026-1…
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ColabFold 1.6.3 is out! 2.5x faster, pip-installable, ipSAE+pDockQ2 scores. Thanks Choonghwan Lee, Marielle Russo, Gyuri Kim, Martin Steinegger, Milot Mirdita 🐍 pip install colabfold[alphafold] (1/3)
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Finally a more intuitive way to learn pLDDT/pAE? 😎 sokrypton.github.io/protein_… (Character idea from @HannesStaerk & Alex Waldherr)
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Could the folding of synthetic gene circuits in 3D shape how genes are expressed? Today @ScienceMagazine we report on the role of gene syntax in shaping feedback between transcriptional activity and genome folding for advanced circuit design🧵 (1/n)
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This is a "detective story" paper that shows how setting up the right negative controls can lead to real discoveries. Basically, the authors wanted to reproduce a published result showing that a specific CRISPR tool, called Csm, could chop up RNA molecules and lower their levels in a cell. They measured this with RT-qPCR, a common experiment that quantifies how much of a given RNA is present before and after the CRISPR knockdown. When they went to reproduce this result, they found that Csm did reduce RNA levels. But a negative control, a mutated version of Csm that shouldn't be able to cut anything, also knocked down RNA by roughly the same amount. Weird! So the authors started varying the experimental parameters to solve this dilemma. First, they tried different RNA extraction methods and tested several more CRISPR tools. They confirmed, too, that the mutant Csm couldn't cut RNA. The RT-qPCR results persisted anyway. Second, they built an mCherry reporter to measure protein levels directly. Normal Csm reduced protein levels, which makes sense since it's cutting the RNA. Mutant Csm did not change protein levels at all. This was another good sign that the mutant Csm was producing some kind of artifact. To figure out why, we need to talk about RT-qPCR. This is a method that, first, converts RNA into DNA using reverse transcriptase, an enzyme that builds a matching DNA copy. qPCR then counts how many DNA copies got made from a short stretch of that RNA, called an amplicon. More copies of DNA = more RNA was present at the start. Normally, scientists place this amplicon so that it overlaps with the guide RNA's binding site, since that's the region expected to get cut. But with mutant Csm, an amplicon located at the guide RNA site showed false knockdowns. When the amplicon was moved to *before* the binding site, the artifact disappeared and RNA levels appeared unchanged. Why was this happening? The reason, oddly, was that the guide RNAs (without any protein!) were blocking reverse transcription, making it look like mutant Csm was reducing RNA levels. The researchers tested this by spiking synthetic guide RNA, made with no CRISPR protein at all, directly into the reverse transcription reaction. This reproduced the false knockdown. If a guide RNA can bind a target without a CRISPR protein at all, then we should also see similar gene silencing in cells. Right? Well, no; it turns out guide RNAs break down quickly in cells unless a CRISPR protein binds and stabilizes them. The proteins protect the guide RNA. So TL;DR: guide RNAs can bind a target and block reverse transcription in test tubes. This causes weird experimental artifacts and likely means dozens of CRISPR papers that used RT-qPCR are wrong, in either a minor or possibly major way.
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New pub from @diffUSEproject! Ordered water molecules mediate protein stability, ligand binding, and catalysis. Predicting them has lagged behind protein structure prediction. We developed WaterFlow, a new state-of-the-art method for placing ordered water. biorxiv.org/content/10.64898…
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One of the puzzles in perturbation prediction, and the cause of many food fights, is that deep learning models often lose to the simplest possible baseline-- just predict the average response across all perturbations. This summer, multiple labs converged on an answer to this.
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CRISPR made gene editing programmable, eventually scaling to genome-wide screens! 🧬 What if we could do this for proteins? 🤔 We built a mammalian screen using language model-designed peptide guides to screen protein "knockdowns" across disease targets. 📜: biorxiv.org/content/10.64898… 👇🧵
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Can molecular glues be designed rather than discovered by chance? We built EvoBind-multimer to design small (6-10 AAs) cyclic peptide glues directly from protein sequences. We validate de novo VHL-KRAS and VHL-BRD4 glues in cells and patient-derived tumoroids.
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Why do T-cell therapies work in blood cancers but struggle in solid tumors? A @Nature study from @jccarnevale’s lab, led by Qi Liu (@GladstoneInst @UCSF), ran genome-wide CRISPR screens on human T cells inside tumors, not in a dish. Two especially interesting hits emerged, each blocking a different step: P2RY8 keeps T cells from getting into the tumor. GNAS is where many of the tumor’s “stand down” signals converge. Delete GNAS, and T cells become resistant to several of those suppressive cues at once. Delete both: in one preclinical model, 4 of 6 mice cleared their tumors, vs 1 of 6 with GNAS deletion alone. A clever demonstration that the tumor itself can help reveal what to engineer into better T-cell therapies.
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Two proteins can bind the same receptor with similar affinity and only one may actually stop the signal. That is the part of this new bioRxiv study I find most interesting. TSLP is an inflammatory cytokine involved in diseases such as asthma and atopic dermatitis. It works by bringing together two receptor components, TSLPR and IL-7Rα, creating a signaling complex that activates JAK–STAT pathways. The researchers designed ultra-compact protein binders against TSLPR using two very different strategies. One built entirely new helical proteins around the receptor surface. The other copied something more subtle: the binding geometry used by the natural TSLP cytokine itself, grafting key contact elements onto a designed scaffold. Both approaches produced nanomolar binders. But only the interface-mimetic designs actually blocked receptor assembly. That distinction matters. Binding affinity tells us how strongly two molecules associate. It does not tell us whether the binder occupies the right molecular geometry to disable function. After maturation, the lead design, TRB5.1, reached 9.7 nM affinity and remained stable near 97°C. In cell models, it suppressed TSLP-induced JAK1/STAT5 signaling and reversed inflammatory transcriptional programs. This is a useful reminder for protein design: the objective is not always to make molecules bind harder. Sometimes the real problem is to bind in exactly the right way. Affinity gets you to the target. Geometry decides what happens next. Preprint: biorxiv.org/content/10.64898…
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