Sharrol T. Bachas

The paper uses language modeling to create a comprehensive representation of protein biology, enabling structure prediction and discovery of high-affinity protein interactions.

This paper develops an iterative epitope-expansion strategy to design IgE-binding miniproteins targeting the FcεRI interface, improving affinity and validating structural models through experimental testing.

IgDesign designs antibody CDRs using deep learning and validates them in vitro, showing high success rates and improved affinities for multiple therapeutic antigens.

Deep generative models combined with high-throughput screening design de novo anti-HER2 antibodies without further optimization, yielding nanomolar binders functionally comparable or superior to trastuzumab.

Language models trained on high-throughput affinity data predict KD of unseen antibody variants across three orders of magnitude, and a "naturalness" score can be optimised alongside affinity.