We investigate the role of GPCR phosphorylation in modulating arrestin binding and conformation, revealing the structural basis for the long-standing “barcode” hypothesis.
We build a “dataset of datasets” to help explore the possibilities of three-dimensional molecular learning.
We use a novel class of neural network architectures to accurately predict the structures of 3D protein complexes.
We combine graph and geometric aspects of biomolecular structure within a single unified framework.
We present a 100x-sized protein interface prediction dataset and achieve state-of-the-art results through using 3DCNNs.
We investigate the structural mechanism of GPCR-mediated arrestin activation, providing a foundation for the design of functionally selective (‘biased’) GPCR-targeted ligands with desired effects on arrestin signalling.
A user-assisted geolocation system than can use natural skylines to often pinpoint a photo’s location to within 100m2.
