Structure and Dynamics of tRNA^Met Containing Core Substitutions

Abstract

The fidelity of protein synthesis is largely dominated by the accurate recognition of transfer RNAs (tRNAs) by their cognate aminoacyl-tRNA synthetases. Aminoacylation of each tRNA with its cognate amino acid is necessary to maintain the accuracy of genetic code input. Aminoacylated tRNAMet functions in both initiation and elongation steps during protein synthesis. As a precursor to the investigation of a methionyl-tRNA synthetase–tRNAMet complex, presented here are the results of molecular dynamics (MD) for single nucleotide substitutions in the D-loop of tRNAMet (G15A, G18A, and G19A) probing structure/function relationships. The core of tRNAMet likely mediates an effective communication between the tRNA anticodon and acceptor ends, contributing an acceptor stem rearrangement to fit into the enzyme-active site. Simulations of Escherichia coli tRNAMet were performed for 1 μs four times each. The MD simulations showed changes in tRNA flexibility and long-range communication most prominently in the G18A variant. The results indicate that the overall tertiary structure of tRNAMet remains unchanged with these substitutions; yet, there are perturbations to the secondary structure. Network-based analysis of the hydrogen bond structure and correlated motion indicates that the secondary structure elements of the tRNA are highly intraconnected, but loosely interconnected. Specific nucleotides, including U8 and G22, stabilize the mutated structures and are candidates for substitution in future studies.

RNA modifications stabilize the tertiary structure of tRNAfMet by locally increasing conformational dynamics

“…In other words, increased fast local conformational dynamics lead to a stabilization of the tRNA’s tertiary structure. Such local structural fluctuations were also observed in molecular dynamic simulations ( 34 , 103 ), however they are not manifested as differences in the tertiary structures of the various tRNA fMet solved so far ( Supplementary Figure S11 ). Importantly, the local dynamics do not negatively impact the tertiary structure of the tRNA as the global structure remains intact.…”

Unraveling the Role of Hydrogen Bonds in Thrombin via Two Machine Learning Methods

“…Machine learning (ML) addresses this issue by enabling computers to learn from large data sets without explicit programming. Techniques such as decision trees, principal component analysis, clustering analysis, neural networks, and network analysis applied to data from molecular dynamics simulations are utilized to achieve this. ,− In this paper, we employ two supervised ML methods, decision trees and logistic regression, to analyze hydrogen bonding networks in proteins.…”

Allosteric Modulation of Thrombin by Thrombomodulin: Insights from Logistic Regression and Statistical Analysis of Molecular Dynamics Simulations

“…55 This matrix indicates how pairs of residues move in relation to each other, a critical aspect in understanding allosteric communication pathways in proteins and protein complexes. 22,23,26,[31][32][33][34][36][37][38]55 i j…”

“…These simulations were executed using the GPU-accelerated ACEMD3 simulation package on NVIDIA Titan GPUs housed within Metrocubo workstations. The simulation protocols described below are standard with ACEMD and have been successfully used in previous works on thrombin and other systems. − ,− …”

“…A correlation matrix was used to evaluate the correlation of motion between pairs of residues throughout the simulations . This matrix indicates how pairs of residues move in relation to each other, a critical aspect in understanding allosteric communication pathways in proteins and protein complexes. ,,,− ,− , where r i ( t ) is the position vector of the atom i at frame t , r̅ ( t ) is the average atom position over all T frames. These equations were implemented in in-house Python scripts, available via GitHub .…”