kontakteUR: transforming coordinates to chemical intuition to focus on essential interactions in biomolecular systems

Abstract

Molecular interactions govern cellular function, making them essential to discover biomolecular mechanisms by unravelling structure-function relationships. The rapid growth of AI-based prediction, experimental determination, and molecular dynamics simulations generates structural data at an unprecedented scale. However, structural information is typically represented as Cartesian coordinates, leaving chemical interactions and conformational relationships largely implicit. We introduce a high-throughput framework transforming structural geometry into a standardized, compact contact space. Moving beyond simple distance cutoffs, it provides a chemically and geometrically informed representation of various residue-residue interactions, their temporal changes, and conformations at residue-level resolution. Our contact-space representation enables systematic comparison and classification even for large-scale analysis. Case studies spanning structure comparison or studies of protein-protein, protein-ligand, protein-RNA, and antibody-antigen complexes, demonstrate how contact-space analysis reveals interaction patterns, identifies key mutation sites, and links structural features to experimental observations. With these and further applications, kontakteUR elucidates biomolecular function and assists targeted protein design, with results suited for further processing by artificial intelligence algorithms.