A wonderful article on PepMLM by @xiaofei_lin in @GENbio! 🌟 Really captures how we think about sequence vs. structure and how, via experimental collaboration, we've gotten biologists (like Ray Truant, @DeLisaGroup, and @AguilarVirology) to believe in sequence-based design. 💚

PepMLM is out in @NatureBiotech! 🎭Want a short binder for any target? Just input its sequence + desired binder length → PepMLM unmasks it. Simple? No way? The results speak for themselves. 👇 📜: nature.com/articles/s41587-0… 💻: github.com/programmablebio/p… 🤗: huggingface.co/ChatterjeeLab… A few years ago, I told my undergrad @SophieVincoff (now my PhD student) to try the obvious: given the target sequence, use autoregressive next-token prediction to generate the cognate peptide binder. After tons of work, we still couldn't get it to work too well. 😔Then, my Masters student (now Harvard PhD student) @LeoTZ03 said: what if we just attach the peptide sequence to the end of its target sequence and fully fine-tune an encoder (in this case, ESM-2-650M) to unmask it? Basically, a BERT-like take on conditional (short) sequence generation. I said to @LeoTZ03: sounds kind of silly.😅 Well, he did what any good student would do: he ignored me, trained the model on ~10k putative peptide-protein sequence pairs, and the in silico results were pretty amazing! PepMLM consistently outperformed RFDiffusion on held-out/structured targets, with a higher hit rate (38% to 29%) and low perplexities that closely matched real binders, with generated sequences showing target specificity even in stringent permutation tests! 🦾 But the proof was in the experiments, and we did a LOT of them! 🧪 In our lab, Zach showed that PepMLM peptides achieved nM binding affinity on disease-related receptor targets, like NCAM1 and AMHR2 (RFDiffusion peptides failed to bind). With the Truant Lab @McMasterU, we showed PepMLM peptides, when fused to E3 ubiquitin ligases (our uAb architecture), not only degraded MSH3 but completely eliminated Huntington protein in HD cells! 🧫 We showed similar results by identifying a PepMLM uAb that degraded MESH1, a protein controlling ferroptosis, in collaboration with @TsanJen's group @DukeU. Again, no hits for RFDiffusion there. And with @AguilarVirology (led by @MadeleineDumas2), in collaboration with @DeLisaGroup @Cornell, PepMLM-derived peptides (in uAbs) bound and reduced levels of viral phosphoproteins from Nipah, Hendra, and HMPV, and in live HMPV infection models, they almost completely cleared the P protein! 🦠 I guess sometimes the "silly" ideas win. 😂 Even though my lab is theoretically-inclined (discrete diffusion/flow matching/Schrodinger bridges) to now design more specialized/optimized/specific molecules @Penn, PepMLM is still a go-to model for my experimentalists. Remember @lauren_hong11's duAbs in @NatureComms that stabilized THE tumor suppressor p53? Yep, PepMLM. 💁‍♂️ In fact, PepMLM has averaged ~600 downloads a month on @huggingface over the last year! It's so easy to use (takes seconds to run on Colab, with just a few lines of transformers code), you don't need to input (or go through) structure, you can enter almost any target sequence (disordered/stable, short/long), and you get a binder. And again, don't believe me, look at the diversity of proteins we've gone after! If you're an experimentalist, it's super worth it to try PepMLM. 😇 The code/model is now fully open-sourced for both academia and industry! 🔓 Btw, this was an incredibly collaborative undertaking: 5 labs, across multiple amazing universities, led by co-first authors @LeoTZ03 (who led all model design, training, and validation), and Zach (@DukeU), Madeleine (@Cornell), Christina (@McMasterU ) who led each of the experimental efforts!! I'm so proud and grateful for everybody's belief that yes, sequence can be all you need for binder design! 😉