14/ Huge congrats to Jingzhou Yang and our team whole team. This was an “all hands-on deck” collaboration to get the selections done in 26 days and is a testament to team science. Also, our @uchicagomedicine.bsky.social collaborators were essential to success.

13/ Also, if selectivity is your jam – check out our other recent paper on isoform/epitope selectivity in PANCS-binders:
www.biorxiv.org/content/10.1...
bsky.app/profile/chem...

14/ On the other hand, we recently showed that our PANCS-binder data can improve ML-based PPI prediction. So while computation is not perfect yet, our high-quality data can keep moving things forward:
www.biorxiv.org/content/10.1...
bsky.app/profile/chem...

13/ Think of it like this – in those 26 days we comprehensively tested all pairwise combinations (experimentally) of 6 target proteins against 40,000,000,000 protein variants. Maybe someone can do the math of how much electricity and time this would take by the state-of-the-art MD/ML/AI methods…

12/ I think there is a bias that computational methods – despite their inherent limitations - are ultimately faster and cheaper than experiments. We challenge that assumption. Experiments can be high fidelity while also being faster AND cheaper than computation.

11/ We also tested computational design (BindCraft) retrospectively. 0/4 designs showed detectable binding. Not a knock on computation—but a reminder that experimental validation remains essential (we are not in the “one design-one binder” era).
www.nature.com/articles/s41...

10/ The bigger picture: This isn't just about one degrader. It's a workflow—from gene name → binder → functional tool → therapeutic hypothesis → to new biology. No protein purification. No massive compute. Just phage, E. coli, and ~$0.60 in water bottles.

9/ Then we went hunting. We profiled NSD3 degradation across ovarian cancer models and found something unexpected: some lines (ES-2) were exquisitely sensitive while others (CAOV-3) were completely resistant—independent of NSD3 expression levels. New biology to explore.

8/ We swapped RNF8's substrate-recognition domain for our NSD3 binder → a mini-protein degrader that potently depleted endogenous NSD3 in colorectal cancer cells and completely blocked proliferation in NSD3-dependent lines.

7/ But binders are just the beginning. We next asked: can we turn these into degraders? We screened 9 E3 ligases and found RNF8—previously unexplored for TPD—was the most potent, driving 90% target depletion.

6/ Key outcome: The binders are all selective and worked in mammalian cells, not just E. coli. We could use them to relocalize proteins in live mammalian cells.

5/ The timeline: Day 1: design constructs. Day 8: genes arrive. Day 17: start selections. Day 20: all 6 selections showed high titers (!). Day 26: sequence-verified, function-validated binders for ALL THREE targets. Affinities ranged from 58 nM to 1.8 µM.

4/ The targets: NSD3 (histone methyltransferase), NMNAT2 (NAD+ biosynthesis), and CSF1R (macrophage receptor)—structurally diverse, clinically relevant, and with few existing targeting tools. A real test.

3/ The setup: We asked oncologist and head of @uchicagocancer.bsky.social Kunle Odunsi to pick 3 cancer targets without telling us in advance. At 5pm on a Tuesday, he emailed us three gene names. The clock started. No cherry-picking. No optimization. Just: can we get binders?

2/ The problem: Protein binders are essential tools for research and therapeutics, but discovering them is slow, expensive, and unpredictable. We wanted to know—can our recently developed PANCS-Binders tech deliver on real-world timelines for targets that matter?
www.nature.com/articles/s41...

While the White Paper gives many important recommendations, this sentence especially struck a cord with me: "We need selfless leaders who unite individuals towards creating a shared vision." I definitely felt the willingness of this great cohort of NextGen Leaders to put this into reality. (10/11)

Yes - bluetorial is definitely better =)

Finally, this was the thesis project of an undergraduate(!!), Josh, now a grad student at Harvard, in close collaboration with our rockstar postdoc, Matt. We also worked with our amazing collaborators at Argonne to get structural data. Awesome team effort!
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