Tanja Kortemme

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.

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.

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

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

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.

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

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

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

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

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.

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

This paper introduces Frame2seq, a structure-conditioned masked language model for protein sequence design that generates novel, stable proteins with low sequence identity to native sequences.

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

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.

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.

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.

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