Characterizing the
structural dynamics of proteins with heterogeneous
conformational landscapes is crucial to understanding complex biomolecular
processes. To this end, dimensionality reduction algorithms are used
to produce low-dimensional embeddings of the high-dimensional conformational
phase space. However, identifying a compact and informative set of
input features for the embedding remains an ongoing challenge. Here,
we propose to harness the power of Residue Interaction Networks (RINs)
and their centrality measures, established tools to provide a graph
theoretical view on molecular structure. Specifically, we combine
the closeness centrality, which captures global features of the protein
conformation at residue-wise resolution, with EncoderMap, a hybrid
neural-network autoencoder/multidimensional-scaling like dimensionality
reduction algorithm. We find that the resulting low-dimensional embedding
is a meaningful visualization of the residue interaction landscape
that resolves structural details of the protein behavior while retaining
global interpretability. This feature-based graph embedding of temporal
protein graphs makes it possible to apply the general descriptive
power of RIN formalisms to the analysis of protein simulations of
complex processes such as protein folding and multidomain interactions
requiring no protein-specific input. We demonstrate this on simulations
of the fast folding protein Trp-Cage and the multidomain signaling
protein FAT10. Due to its generality and modularity, the presented
approach can easily be transferred to other protein systems.