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

Erkip, Albert and Erman, Burak (2024) Dynamically driven correlations in elastic net models reveal sequence of events and causality in proteins. Proteins: Structure, Function and Bioinformatics, 92 (9). pp. 1113-1126. ISSN 0887-3585 (Print) 1097-0134 (Online)

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Abstract

An explicit analytic solution is given for the Langevin equation applied to the Gaussian Network Model of a protein subjected to both a random and a deterministic periodic force. Synchronous and asynchronous components of time correlation functions are derived and an expression for phase differences in the time correlations of residue pairs is obtained. The synchronous component enables the determination of dynamic communities within the protein structure. The asynchronous component reveals causality, where the time correlation function between residues i and j differs depending on whether i is observed before j or vice versa, resulting in directional information flow. Driver and driven residues in the allosteric process of cyclophilin A and human NAD-dependent isocitrate dehydrogenase are determined by a perturbation-scanning technique. Factors affecting phase differences between fluctuations of residues, such as network topology, connectivity, and residue centrality, are identified. Within the constraints of the isotropic Gaussian Network Model, our results show that asynchronicity increases with viscosity and distance between residues, decreases with increasing connectivity, and decreases with increasing levels of eigenvector centrality.
Item Type: Article
Uncontrolled Keywords: allosteric regulation; causality; Gaussian Network Model; information flow; perturbation-scanning; protein dynamics; synchronous and asynchronous correlations
Divisions: Faculty of Engineering and Natural Sciences
Depositing User: Albert Erkip
Date Deposited: 23 Sep 2024 13:24
Last Modified: 23 Sep 2024 13:24
URI: https://research.sabanciuniv.edu/id/eprint/50041

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