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bioRxiv · 10.1101/2024.01.15.575718

Dynamically driven correlations in elastic net models reveal sequence of events and causality in proteins

Erkip, A.·Erman, B.
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Abstract

Protein dynamics orchestrate allosteric regulation, but elucidating the sequence of events and causal relationships within these intricate processes remains challenging. We introduce the Dynamically Perturbed Gaussian Network Model (DP-GNM), a novel approach that uncovers the directionality of information flow within proteins. DP-GNM leverages time-dependent correlations to achieve two goals: identifying driver and driven residues and revealing communities of residues exhibiting synchronized dynamics. Applied to wild type and mutated structures of Cyclophilin A, DP-GNM unveils a hierarchical network of information flow, where key residues initiate conformational changes that propagate through the protein in a directed manner. This directional causality illuminates the intricate relationship between protein dynamics and allosteric regulation, providing valuable insights into protein function and potential avenues for drug design. Furthermore, DP-GNMs potential to elucidate dynamics under periodic perturbations like the circadian rhythm suggests its broad applicability in understanding complex biological processes governed by environmental cycles.

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Erkip, A., Erman, B.. 2024-01-16. Dynamically driven correlations in elastic net models reveal sequence of events and causality in proteins. https://doi.org/10.1101/2024.01.15.575718

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