D. Tischer

Small-molecule binding and sensing with a designed protein family

This paper designs a protein family that binds and senses small molecules using deep learning and physics-based methods, achieving nanomolar sensitivity for cortisol.

Robust deep learning based protein sequence design using ProteinMPNN

A deep learning method for protein sequence design shows high performance in both simulations and experiments, applicable to various design challenges.

Deep learning methods for designing proteins scaffolding functional sites

This paper introduces two deep learning methods for designing proteins with specific functional sites, showing they can generate diverse proteins with desired properties.

Design of proteins presenting discontinuous functional sites using deep learning

This paper designs proteins with discontinuous functional sites using deep learning, eliminating the need for pre-defined secondary structures and enabling diverse scaffold generation.

Light-based tuning of ligand half-life supports kinetic proofreading model of T cell activation

The paper directly tests the kinetic proofreading model of T cell activation by controlling ligand binding half-life with light, showing that lifetime, not occupancy, dominates activation.