Dynamical Network Analysis of Protein:RNA Complexes Made Easy

Abstract

Molecular interactions are essential for regulation of cellular processes, from the formation of multi-protein complexes, to binding of ligands in active sites, and allosteric activation of enzymes. Identifying the essential residues and molecular features that regulate such interactions is paramount for understanding the biochemical process in question, allowing for suppression of a reaction through drug interventions, or optimization of a chemical process using bioengineered molecules. In order to identify important residues and information pathways within molecular complexes, the Dynamical Network Analysis method was developed and has since been broadly applied in the literature. In this work we provide an evolution of the method, application and interface, with all data processing and analysis, including visualization, conducted through a Jupyter notebook. It provides an optimized and parallel generalized correlation implementation that is linear with respect to the number of nodes in the system, automatic detection of solvent and ion residues stably bound to the target molecule, and subsequent community clustering, calculation of betweenness of contacts, and determination optimal paths for the entire system.The new implementation was applied to the leucine tRNA synthetase complexed with its cognate tRNA and adenylate. Our analysis was capable of identifying experimentally verified identity elements throughout the tRNA:protein interface, as well as essential catalytic residues in the active site. The hypothesis that a magnesium ion is required for the charging of the tRNA was investigated, and we observed significant destabilization of communities and contacts in the active site, indicating the ion does not create a favorable environment for the reaction. This enhanced and updated methodology will provide the community with an intuitive and interactive interface, that can be easily applied to large macro-molecular complexes.