Tanja Kortemme

Generative Design of New-to-nature Biosynthetic Assembly Lines with Genomic Language Modeling

This paper uses a genomic language model to design and optimize a synthetic biosynthetic pathway for producing a non-natural molecule, achieving a 9.4-fold increase in production.

Deep Learning-based Modeling Enhances Efficacy of Natural Ligand CAR Binders Targeting CD70

This study uses deep learning and physics-based modeling to engineer a CD27 variant that improves the efficacy of CD70-targeting CAR T-cells across multiple cancer models.

Programming protein shape as an explicit design layer via CAD blueprint-guided diffusion

The paper integrates CAD blueprints with diffusion models to explicitly program protein shapes, demonstrating emergent functions through molecular design.

A Hybrid Physics-Deep Learning Framework for Combinatorial De Novo Design of Small-Molecule Binding Proteins

This paper introduces a hybrid physics-deep learning framework for designing small-molecule binding proteins, achieving experimental success in binding affinity and structural accuracy.

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

This paper introduces ProteinZen, a method for all-atom protein design using SE(3) flow matching, achieving high sequence-structure consistency and competitive performance in design tasks.

Structure-guided engineering of CCL27 enhances natural ligand CAR T-cells against CCR10 for multiple myeloma

This paper engineers CCL27 mutants to enhance CAR T-cell efficacy against multiple myeloma by targeting CCR10, achieving results comparable to BCMA CAR-Ts.

A combinatorial mutational map of active non-native protein kinases by deep learning guided sequence design

The paper uses deep learning to design novel protein kinase sequences with multiple mutations, measures their activity, and identifies key sequence determinants of function.

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

The paper presents a fine-tuned Alphafold2 model that better captures geometric diversity in de novo designed proteins compared to standard methods.

Deep learning guided design of dynamic proteins

This paper designs dynamic proteins with switch-like behavior using deep learning, validated by structure solving and simulations showing controllable conformational changes.

Targeting high-risk multiple myeloma genotypes with optimized anti-CD70 CAR-T cells

Structure-optimized CD27-based CAR-T cells targeting CD70 show >80-fold improved expansion in vivo and offer a potential therapy for high-risk multiple myeloma patients.

An integrative approach to protein sequence design through multiobjective optimization

This paper introduces an evolutionary multiobjective optimization framework for protein sequence design, combining AlphaFold2 and ProteinMPNN to improve sequence recovery in complex design problems.

Multi-input drug-controlled switches of mammalian gene expression based on engineered nuclear hormone receptors

The paper demonstrates multi-input drug-controlled gene expression using engineered nuclear hormone receptors, showing tunable outputs with multiple drugs.

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

The paper presents a framework for ensuring reproducibility in bio-macromolecular modeling through automated benchmarks and a test server, demonstrating its utility for Rosetta and other tools.

Biophysical basis of cellular multi-specificity encoded in a model molecular switch

The paper investigates how the GTPase Ran/Gsp1 achieves functional multi-specificity through biophysical and genetic perturbations, revealing that its effects are linked to kinetic parameters of the switch cycle rather than direct interaction sites.

Controlling CRISPR-Cas9 with ligand-activated and ligand-deactivated sgRNAs

Inserting theophylline aptamers into the sgRNA yields ligand-activated and ligand-deactivated guides that tune CRISPRi in E. coli over 11x to 13x dynamic ranges, enabling two-ligand multiplexing.

Extending Chemical Perturbations Of The Ubiquitin Fitness Landscape In A Classroom Setting

The paper uses deep mutational scanning to explore how chemical perturbations affect ubiquitin's mutational tolerance, revealing sensitization of most positions except two.