Decoding the Identification Mechanism of an SAM-III Riboswitch on Ligands through Multiple Independent Gaussian-Accelerated Molecular Dynamics Simulations

Abstract

S-Adenosyl-L-methionine (SAM)-responsive riboswitches play a central role in the regulation of bacterial gene expression at the level of transcription attenuation or translation inhibition. In this study, multiple independent Gaussian-accelerated molecular dynamics simulations were performed to decipher the identification mechanisms of SAM-III (S MK ) on ligands SAM, SAH, and EEM. The results reveal that ligand binding highly affects the structural flexibility, internal dynamics, and conformational changes of SAM-III. The dynamic analysis shows that helices P3 and P4 as well as two junctions J23 and J24 of SAM-III are highly susceptible to ligand binding. Analyses of free energy landscapes suggest that ligand binding induces different free energy profiles of SAM-III, which leads to the difference in identification sites of SAM-III on ligands. The information on ligand−nucleotide interactions not only uncovers that the π−π, cation−π, and hydrogen bonding interactions drive identification of SAM-III on the three ligands but also reveals that different electrostatic properties of SAM, SAH, and EEM alter the active sites of SAM-III. Meanwhile, the results also verify that the adenine group of SAM, SAH, and EEM is well recognized by conserved nucleotides G7, A29, U37, A38, and G48. We expect that this study can provide useful information for understanding the applications of SAM-III in chemical, synthetic RNA biology, and biomedical fields.

Exploration of the binding of antifungal drugs to human P450 2C9 based on docking and molecular dynamics simulation

“…MM‐GBSA is a powerful approach to evaluate binding affinities of a great many of ligand/drug to their target proteins and it has been reported for the reliable applications in previous studies [74–76]. Here, binding free energy (Δ G ) was investigated to obtain an in‐depth insight into the interaction between these antifungals and CYP2C9 based on this method.…”

“…for the reliable applications in previous studies [74][75][76]. Here, binding free energy (ΔG) was investigated to obtain an in-depth insight into the T A B L E 1 Different energy items and the binding free energy for each conformation computed by the MM-GBSA method.…”

Deciphering Selectivity Mechanism of BRD9 and TAF1(2) toward Inhibitors Based on Multiple Short Molecular Dynamics Simulations and MM-GBSA Calculations

“…It is highly important to achieve a good trade-off between accuracy and efficiency in calculations of inhibitor–target binding free energies for drug development, which has been discussed by Rizzuti et al in their work [ 90 ]. Recently, empirical equation-based MM-GBSA and molecular mechanics Poisson Boltzman surface area (MM-PBSA) methods have extensively been used to estimate the binding free energy for numerous ligand–protein and protein–protein interactions [ 91 , 92 , 93 , 94 , 95 , 96 , 97 ]. Furthermore, Hou’s group assessed the performance of the MM-GBSA and MM-PBSA approaches by calculating the binding free energies of various biological systems, and their results implied that the MM-GBSA approach could provide more reasonable conclusions [ 98 , 99 ].…”

Exploration of the Product Specificity of chitosanase CsnMY002 and Mutants Using Molecular Dynamics Simulations

“…Trajectory analyses were computed using Amber16′s CPPTRAJ module [ 15 ] and included RMSD, radius of gyration, RMSF, SASA and dictionary of secondary structures. The cross-correlation matrix of the trajectory was generated with Tcl script in VMD, and its eigenvector and eigenvalue were calculated [ 33 , 34 ].…”