@KortemmeLab

Our lab @UCSF focuses on protein design to engineer advanced functions and understand cellular processes! Tweets by lab members unless signed with TK.

 San Francisco, CA
kortemmelab.ucsf.edu/
Joined November 2016
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  • Following47
  • Followers1.1K
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We’re excited to share our new perspective in @MolecularCell on designing programmable biological behavior! (1/3) doi.org/10.1016/j.molcel.202…
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AI-guided de novo protein design has advanced remarkably in recent years, especially for binder design. We propose that the next frontier lies in integrating designed molecular functions into reaction networks, so we can program emergent cellular behaviors. (2/3)
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Getting there will require overcoming key challenges, including designable protein dynamics, multi-step functionalities, and non-equilibrium systems. The field is advancing quickly, and we’re excited for what comes next! (3/3)
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Kortemme Lab retweeted
Yilun Qi@Yilun_QiJul 23
Excited to share our preprint from @KortemmeLab, where we establish protein shape as a programmable design layer using CAD blueprint-guided diffusion. By programming shape across scales, we explore a path toward designing proteins more like machines. (1/7) biorxiv.org/content/10.64898…
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Kortemme Lab retweeted
Alex J Li@alex_j_li19 Oct 2025
Excited to finally release ProteinZen, an SE(3) equivariant all-atom protein generative model enabling unconditional all-atom monomer generation and SoTA all-atom motif scaffolding performance! Preprint here: biorxiv.org/content/10.1101/… 🧵1/n

All-atom protein design via SE(3) flow matching with ProteinZen

The advent of generative models of protein structure has greatly accelerated our ability to perform de novo protein design, especially concerning design at coarser physical scales such as backbone...

biorxiv.org
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We are excited to share our new preprint, “An improved model for prediction of de novo designed proteins with diverse geometries”. (1/9) biorxiv.org/content/10.1101/…

An improved model for prediction of de novo designed proteins with diverse geometries

Nature uses structural variations on protein folds to fine-tune the geometries of proteins for diverse functions, yet deep learning-based de novo protein design methods generate highly regular,...

biorxiv.org
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This work highlights how augmenting existing models with informative experimental data, as presented here, could expand our exploration of designable protein space and ultimately enable more challenging design problems to be addressed than currently possible. (8/9)
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Model weights have been uploaded to zenodo. Fine-tuning and analysis code to be released soon. Work by @BenOrr_, Stephanie Crilly, @DenizAkpinaro, Eleanor Zhu, @keiser_lab, and Tanja Kortemme. (9/9) zenodo.org/records/15580792

An improved model for prediction of de novo designed proteins with diverse geometries

Fine-tuned AF2 models for "Benjamin Orr*, Stephanie E. Crilly*, Deniz Akpinaroglu, Eleanor Zhu, Michael J. Keiser, Tanja Kortemme. An improved model for prediction of de novo designed proteins with...

zenodo.org
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We’re thrilled to share our new pre-print exploring how we can design and control dynamic proteins! (1/6) biorxiv.org/content/10.1101/…
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Physics-based simulations further uncovered the molecular origins of the observed dynamics and modulation, which we used to further tune the conformational landscape (5/6)
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We showed new modes of motions are accessible to de novo design, enabling new functions & compatibility with natural states, allowing one to engineer non-native dynamics into existing biological systems! Work by @amyguo1997, @DenizAkpinaro, MJS Kelly & T Kortemme (6/6)
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