Visualizing the Residue Interaction Landscape of Proteins by Temporal Network Embedding.

ABSTRACT: 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/mu

SUBMITTER: Franke L 

PROVIDER: S-EPMC10210247 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature