  Proteins play a central role in biology from immune recognition to brain
activity. While major advances in machine learning have improved our ability to
predict protein structure from sequence, determining protein function from
structure remains a major challenge. Here, we introduce Holographic
Convolutional Neural Network (H-CNN) for proteins, which is a physically
motivated machine learning approach to model amino acid preferences in protein
structures. H-CNN reflects physical interactions in a protein structure and
recapitulates the functional information stored in evolutionary data. H-CNN
accurately predicts the impact of mutations on protein function, including
stability and binding of protein complexes. Our interpretable computational
model for protein structure-function maps could guide design of novel proteins
with desired function.
