Characterization of the Interaction between Gallic Acid and Lysozyme by Molecular Dynamics Simulation and Optical Spectroscopy

Abstract

The binding interaction between gallic acid (GA) and lysozyme (LYS) was investigated and compared by molecular dynamics (MD) simulation and spectral techniques. The results from spectroscopy indicate that GA binds to LYS to generate a static complex. The binding constants and thermodynamic parameters were calculated. MD simulation revealed that the main driving forces for GA binding to LYS are hydrogen bonding and hydrophobic interactions. The root-mean-square deviation verified that GA and LYS bind to form a stable complex, while the root-mean-square fluctuation results showed that the stability of the GA-LYS complex at 298 K was higher than that at 310 K. The calculated free binding energies from the molecular mechanics/Poisson-Boltzmann surface area method showed that van der Waals forces and electrostatic interactions are the predominant intermolecular forces. The MD simulation was consistent with the spectral experiments. This study provides a reference for future study of the pharmacological mechanism of GA.

Binding of Mammea A/AA (MA) to Plasma Proteins: UV–visible Spectroscopy and Molecular Dynamics Simulations Study

“…At higher concentrations, this can introduce an inner filter effect that decreases the fluorescence emission of BSA/HSA and influences the quenching process. The fluorescence intensities were corrected for the inner filter effect using the following relationship where F cor and F obs are the corrected and observed fluorescence intensities and A ex and A em are the absorbances of MA at the excitation and emission wavelengths of albumin, respectively.…”

Lysozyme binding with sulfa group of antibiotics: Comparative binding thermodynamics and computational study

“…The RMSD values of the backbone atoms are a way to determine the stability of the system. [ 73 ] Figure 10 represents the RMSD graph for both complexes, the graph showed a minimal deviation ranging from 0.15 nm to 0.25 nm for the SMZ–Lyz complex and 0.1 nm to 0.28 nm for the SDZ–Lyz complex. The graph increased initially but after 2000 ps time, the structural stability was obtained and maintained till 8000 ps time.…”

Recent Developments and Applications of the MMPBSA Method

“…They also utilized a quantitative structure activity relationship (QSAR) model to improve the correlation to 0.88, combining affinities from MMPBSA, QM/MMGBSA, and Glide scoring (Slynko et al, 2014 ). There were other attractive targets including indoleamine 2,3-dioxygenase 1 (Zou et al, 2017 ), translationally controlled tumor protein (Kumar R. et al, 2017 ), estrogen receptor (Anbarasu and Jayanthi, 2017 ), MutT homolog 1 (Zhou et al, 2016 ), survivin (Sarvagalla et al, 2016 ), CD44 (Nguyen et al, 2016 ), calmodulin (Gonzalez-Andrade et al, 2016 ), androgen receptor (Liu H. L. et al, 2016 ), human topoisomerase I (Guruge et al, 2016 ), Mcl-1 (Zhao et al, 2015 ), vascular endothelial growth factor receptor-2 (Wu et al, 2015 ), tubulin (Liao et al, 2014a , b ; Santoshi and Naik, 2014 ; Suri and Naik, 2015 ; Suri et al, 2015 ), the Hsp70 protein family (Bhattacharjee et al, 2015 ; Schneider et al, 2016 ), the Hsp90 protein family (Arba et al, 2015 ), glucose 6-phosphate dehydrogenase (Obiol-Pardo et al, 2014 ; Zhao et al, 2014 ), lysozyme (Zhan et al, 2015 ), p53 (Verma S. et al, 2016 ), wheat germ agglutinin (Parasuraman et al, 2014 ), bromodomains (Muvva et al, 2014 ), matrix metalloproteinases (Zhou et al, 2014 ), protein arginine methyltransferases (Hong et al, 2014 ; Yan et al, 2014 ), human arsenic methyltransferase (Abro and Azam, 2016 ), Atox1 proteins (Wang X. L. et al, 2014 ), tyrosyl-DNA phosphodiesterase 2 (Kossmann et al, 2016 ), and urokinase-type plasminogen activator (Sa et al, 2014 ).…”