Todd O. Yeates

This paper designs full-length agonist and antagonist antibodies for hard targets using a computational pipeline, achieving nanomolar affinities and functional activity.

The paper designs and validates self-assembling protein cages using helical fusion and machine learning, achieving precise structures with high resolution and molecular mass accuracy.

The paper identifies structural determinants of co-translational protein complex assembly and shows how intertwined subunits require synchronized proteostasis, with implications for protein stability and mRNA localization.

The paper designs a symmetry-broken tetrahedral protein cage by modifying subunits to display a single copy of an outward-facing domain.

The paper designs new protein cages using machine learning and fragment-based methods, reporting their structures and insights into assembly success and failure.

The paper introduces designed fluorescent protein cages that target specific subcellular locations in mammalian cells for imaging, demonstrating their ability to penetrate cells and be detected with confocal microscopy.

The paper introduces MCPdb, a database cataloging bacterial microcompartment structures and their assemblies, annotated for assembly state, structure, and metabolic function.

The paper introduces Nanohedra, a program for designing symmetric protein assemblies by encoding construction rules and using empirical fragment-pair associations to generate native-like interface arrangements.

The paper demonstrates that small proteins can be visualized at near-atomic resolution using cryo-EM when attached to a symmetric protein scaffold.