This paper highlights the utility of residue interaction networks in understanding protein structures, suggesting potential for AI and molecular dynamics applications.
The rise of AlphaFold and similar structure predictors has made it possible to determine the 3D structure of almost any protein from its amino acid sequence. Residue interaction networks (RINs), graphs where residues are represented as nodes and interactions as edges, provide a powerful framework for analyzing and interpreting this surge in structural data. Here, we provide a comprehensive introduction to RINs, exploring different approaches to constructing and analyzing them, including their integration with molecular dynamics (MD) simulations and artificial intelligence (AI). To illustrate their versatility, we present different case studies where RINs have been applied to investigate thermostability, allosterism, post-translational modifications (PTMs), homology, and evolution. Finally, we discuss future directions for RINs, emphasizing opportunities for refinement and broader integration into structural biology.
No takes yet. Share an insight, caveat, or question.
Begue et al. (2025) studied this question.
Synapse has enriched 2 closely related papers on similar clinical questions. Consider them for comparative context: