  Proteins are fundamental biological entities mediating key roles in cellular
function and disease. This paper introduces a multi-scale graph construction of
a protein -- HoloProt -- connecting surface to structure and sequence. The
surface captures coarser details of the protein, while sequence as primary
component and structure -- comprising secondary and tertiary components --
capture finer details. Our graph encoder then learns a multi-scale
representation by allowing each level to integrate the encoding from level(s)
below with the graph at that level. We test the learned representation on
different tasks, (i.) ligand binding affinity (regression), and (ii.) protein
function prediction (classification). On the regression task, contrary to
previous methods, our model performs consistently and reliably across different
dataset splits, outperforming all baselines on most splits. On the
classification task, it achieves a performance close to the top-performing
model while using 10x fewer parameters. To improve the memory efficiency of our
construction, we segment the multiplex protein surface manifold into molecular
superpixels and substitute the surface with these superpixels at little to no
performance loss.
