Nathaniel R. Felbinger

This study determines the activation pathway of the human voltage-gated proton channel hHV1 using computational methods, revealing voltage-driven structural changes and lipid effects validated by experiments.

This study benchmarks deep learning models' ability to predict mutation-induced fold switching in proteins using an NMR-characterized dataset of fold-switching mutants.

This study benchmarks AlphaFold and related methods for modeling antibody and TCR antigen recognition, showing improvements with AlphaFold3 and increased sampling but varying success across complex types.

This study improves HIV broadly neutralizing antibodies through multistate engineering, resulting in enhanced breadth and potency against diverse Env variants.

This study evaluates two deep learning methods for designing T cell receptors by comparing their performance to physics-based approaches in binding target antigens.