Magnus Bauer

@kinasekid.bsky.social

Enjoying life one molecule at a time! / Postdoc @UWproteindesign / ex @Stanford / PhD @LMU_Muenchen / tweeting in English, thinking in Bavarian, coding in Python

Can we program a kinase like a switch? Inspired by natural autoinhibitory complexes, we designed miniproteins against active- and inactive-like conformations of Focal Adhesion Kinase. Depending on the targeted state, the resulting binders either activated or inhibited the kinase.

Not new, but a new to us update: The first preprint out of my lab! We joined forces with @kinasekid.bsky.social @jasonzxzhang.bsky.social and David Baker to study protein phosphorylation! Congrats to Isabella from my lab on her first first author paper! tinyurl.com/43jwwfua

De novo design of phosphotyrosine peptide binders

Phosphorylation on tyrosine is a key step in many signaling pathways. Despite recent progress in de novo design of protein binders, there are no current methods for designing binders that recognize phosphorylated proteins and peptides; this is a challenging problem as phosphate groups are highly charged, and phosphorylation often occurs within unstructured regions. Here we introduce RoseTTAFold Diffusion 2 for Molecular Interfaces (RFD2-MI), a deep generative framework for the design of binders for protein, ligand, and covalently modified protein targets. We demonstrate the power and versatility of this method by designing binders for four critical phosphotyrosine sites on three clinically relevant targets: Cluster of Differentiation 3 (CD3ε), Epidermal Growth Factor Receptor (EGFR), Insulin Receptor (INSR) and Signal Transducer and Activator of Transcription 5 (STAT5). Experimental characterization shows that the designs bind their phosphotyrosine containing targets with affinities comparable to native binding sites and have negligible binding to non-phosphorylated targets or phosphopeptides with different sequences. X-ray crystal structures of generated binders to CD3ε and EGFR are very close to the design models, demonstrating the accuracy of the design approach. A designed binder to an EGFR intracellular region phosphorylated upon EGF activation co-localizes with the receptor following EGF stimulation in single-particle tracking (SPT) experiments, demonstrating pY specific recognition in living cells. RFD2-MI provides a generalizable all-atom diffusion framework for probing and modulating phosphorylation-dependent signaling, and more generally, for developing research tools and targeted therapeutics against post-translationally modified proteins. ### Competing Interest Statement The authors have declared no competing interest. NIH NCI, 1K99CA293001

biorxiv.org

It was such a fun journey working with Krishna’s lab and @kinasekid.bsky.social! Really excited to see where this phospho-binder technology goes!

Krishna Mudumbi@krishnamudumbi.bsky.social · 6mo ago

Not new, but a new to us update: The first preprint out of my lab! We joined forces with @kinasekid.bsky.social @jasonzxzhang.bsky.social and David Baker to study protein phosphorylation! Congrats to Isabella from my lab on her first first author paper! tinyurl.com/43jwwfua

Thrilled to announce our new preprint, “Protein Hunter: Exploiting Structure Hallucination within Diffusion for Protein Design,” in collaboration with @Griffin, @GBhardwaj8 and @sokrypton.org 🧬Code and notebooks will be released by the end of this week. 🎧Golden- Kpop Demon Hunters

(1/7) Training biomolecular foundation models shouldn't be so hard. And open-source structure prediction is important. So today we're releasing two software packages: AtomWorks and RosettaFold3 (RF3) [https://www.biorxiv.org/content/10.1101/2025.08.14.670328v2](www.biorxiv.org/content/10.1...)

Accelerating Biomolecular Modeling with AtomWorks and RF3

Deep learning methods trained on protein structure databases have revolutionized biomolecular structure prediction, but developing and training new models remains a considerable challenge. To facilita...

biorxiv.org

Update on the Chroma vs RfDiffusion analysis. ProteinMPNN just doesn't like Chroma's backbones (poor prediction of proteinMPNN generated sequences by ESMFold). Interestingly, Chroma's own sequence design method (which was trained in the context of partially noise backbones) loves it! (1/3)

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