B. I. M. Wicky

This paper introduces ProteinMPNN, a deep learning model that improves protein expression, stability, and function by incorporating evolutionary and structural information.

The paper introduces a method to design pseudosymmetric protein hetero-oligomers by redesigning symmetric homo-oligomers and recombining them to form new hetero-oligomers with high structural specificity.

Deep network-based protein design generates symmetric homo-oligomers with high structural accuracy, demonstrating the potential for creating novel nanomachines and biomaterials.

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

This paper designs protein sequences by optimizing an energy landscape to ensure they fold into a desired structure, showing improved performance over traditional methods.

This study shows that BAK/BAX spontaneous oligomerization is suppressed by MCL-1 hetero-dimerization, with BH3-only proteins disrupting these interactions to promote apoptosis.