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Dynamically driven correlations in elastic net models reveal sequence of events and causality in proteins
  • Burak Erman,
  • Albert Erkip
Burak Erman
Koc Universitesi Kimya Bolumu

Corresponding Author:[email protected]

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Albert Erkip
Sabanci Universitesi Muhendislik ve Doga Bilimleri Fakultesi
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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-GNM’s potential to elucidate dynamics under periodic perturbations like the circadian rhythm suggests its broad applicability in understanding complex biological processes governed by environmental cycles.
Submitted to PROTEINS: Structure, Function, and Bioinformatics
06 Mar 2024Editorial Decision: Revise Major
30 Mar 20241st Revision Received
08 Apr 2024Assigned to Editor
08 Apr 2024Submission Checks Completed
08 Apr 2024Review(s) Completed, Editorial Evaluation Pending
08 Apr 2024Reviewer(s) Assigned
16 Apr 2024Editorial Decision: Accept