title: DIRECT-ID: An automated method to identify and quantify conformational variations--application to β2 -adrenergic GPCR.
abstract: The conformational dynamics of a macromolecule can be modulated by a number of factors, including changes in environment, ligand binding, and interactions with other macromolecules, among others. We present a method that quantifies the differences in macromolecular conformational dynamics and automatically extracts the structural features responsible for these changes. Given a set of molecular dynamics (MD) simulations of a macromolecule, the norms of the differences in covariance matrices are calculated for each pair of trajectories. A matrix of these norms thus quantifies the differences in conformational dynamics across the set of simulations. For each pair of trajectories, covariance difference matrices are parsed to extract structural elements that undergo changes in conformational properties. As a demonstration of its applicability to biomacromolecular systems, the method, referred to as DIRECT-ID, was used to identify relevant ligand-modulated structural variations in the β2 -adrenergic (β2 AR) G-protein coupled receptor. Micro-second MD simulations of the β2 AR in an explicit lipid bilayer were run in the apo state and complexed with the ligands: BI-167107 (agonist), epinephrine (agonist), salbutamol (long-acting partial agonist), or carazolol (inverse agonist). Each ligand modulated the conformational dynamics of β2 AR differently and DIRECT-ID analysis of the inverse-agonist vs. agonist-modulated β2 AR identified residues known through previous studies to selectively propagate deactivation/activation information, along with some previously unidentified ligand-specific microswitches across the GPCR. This study demonstrates the utility of DIRECT-ID to rapidly extract functionally relevant conformational dynamics information from extended MD simulations of large and complex macromolecular systems.
authors.affiliation: Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Maryland, 21201.
authors.forename: Sirish Kaushik
authors.initials: SK
authors.lastname: Lakkaraju
authors.affiliation: Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Maryland, 21201.
authors.forename: Justin A
authors.initials: JA
authors.lastname: Lemkul
authors.affiliation: Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Maryland, 21201.
authors.forename: Jing
authors.initials: J
authors.lastname: Huang
authors.affiliation: Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Maryland, 21201.
authors.forename: Alexander D
authors.initials: AD
authors.lastname: MacKerell
mesh_terms: D001331:Automation; D056004:Molecular Dynamics Simulation; D011487:Protein Conformation; D018343:Receptors, Adrenergic, beta-2 / Q000737:chemistry*
chemical_list: D018343:Receptors, Adrenergic, beta-2
keywords: conformational dynamics; covariance analysis; ligand efficacy; microswitches; norm of matrix; β2-adrenergic G-protein coupled receptor