  Recent advances in protein function prediction exploit graph-based deep
learning approaches to correlate the structural and topological features of
proteins with their molecular functions. However, proteins in vivo are not
static but dynamic molecules that alter conformation for functional purposes.
Here we apply normal mode analysis to native protein conformations and augment
protein graphs by connecting edges between dynamically correlated residue
pairs. In the multilabel function classification task, our method demonstrates
a remarkable performance gain based on this dynamics-informed representation.
The proposed graph neural network, ProDAR, increases the interpretability and
generalizability of residue-level annotations and robustly reflects structural
nuance in proteins. We elucidate the importance of dynamic information in graph
representation by comparing class activation maps for hMTH1, nitrophorin, and
SARS-CoV-2 receptor binding domain. Our model successfully learns the dynamic
fingerprints of proteins and pinpoints the residues of functional impacts, with
vast untapped potential for broad biotechnology and pharmaceutical
applications.
