Chris Bailey‐Kellogg

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.

This paper introduces glyBERT, a deep learning model that captures local and global context in glycan structures to improve prediction and generation of glycan functional properties.

This study identifies global determinants of lectin specificity by analyzing 221 physicochemical and geometric features of lectin-glycan interactions and demonstrates predictive modeling of glycan recognition.

The paper develops a method combining computational and experimental approaches to identify binding modes and improve affinity of an LRR protein binder without structure determination.

The paper develops a deep learning framework to predict antibody-antigen binding interfaces using graph convolutions, attention layers, and transfer learning, achieving state-of-the-art performance.

The paper evaluates how CDR3 sequence similarity can balance sensitivity and specificity in distinguishing TCR groups, showing it effectively classifies repertoires by antigen specificity and pathology.