A Molecular Dynamics Approach to Explore the Intramolecular Signal Transduction of PPAR-α

• Published in: International journal of molecular sciences Vol. 20; no. 7; p. 1666
• Main Authors: Basith, Shaherin, Manavalan, Balachandran, Shin, Tae Hwan, Lee, Gwang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 03.04.2019; MDPI
• Subjects: agonist; antagonist; apo; Arteriosclerosis; Atherosclerosis; betweenness centrality; Crystal structure; Crystallization; Deoxyribonucleic acid; DNA; Dyslipidemia; Eigenvalues; Electron microscopy; Fatty acids; Genes; Glucose metabolism; hydrogen bond; Hyperglycemia; Inflammation; Ligands; Lipid metabolism; Lipids; Metabolic disorders; Metabolism; Metabolites; Molecular dynamics; network theory; NMR; Nuclear magnetic resonance; Peroxisome proliferator-activated receptors; PPAR-α; Principal components analysis; Proteins; Signal transduction; apo; network theory; agonist; PPAR-α; molecular dynamics; antagonist; betweenness centrality; hydrogen bond
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms20071666

Dynamics and functions of the peroxisome proliferator-activated receptor (PPAR)-α are modulated by the types of ligands that bind to the orthosteric sites. While several X-ray crystal structures of PPAR-α have been determined in their agonist-bound forms, detailed structural information in their apo and antagonist-bound states are still lacking. To address these limitations, we apply unbiased molecular dynamics simulations to three different PPAR-α systems to determine their modulatory mechanisms. Herein, we performed hydrogen bond and essential dynamics analyses to identify the important residues involved in polar interactions and conformational structural variations, respectively. Furthermore, betweenness centrality network analysis was carried out to identify key residues for intramolecular signaling. The differences observed in the intramolecular signal flow between apo, agonist- and antagonist-bound forms of PPAR-α will be useful for calculating maps of information flow and identifying key residues crucial for signal transductions. The predictions derived from our analysis will be of great help to medicinal chemists in the design of effective PPAR-α modulators and additionally in understanding their regulation and signal transductions.