Ribosomal RNA–Aminoglycoside Hygromycin B Interaction Energy Calculation within a Density Functional Theory Framework

Abstract

We intend to investigate the drug-binding energy of each nucleotide inside the aminoglycoside hygromycin B (hygB) binding site of 30S ribosomal RNA (rRNA) subunit by using the molecular fractionation with conjugate caps (MFCC) strategy based on the density functional theory (DFT), considering the functional LDA/PWC, OBS, and the dielectric constant parametrization. Aminoglycosides are bactericidal antibiotics that have high affinity to the prokaryotic rRNA, inhibiting the synthesis of proteins by acting on the main stages of the translation mechanism, whereas binding to rRNA 16S, a component of the 30S ribosomal subunit in prokaryotes. The identification of the nucleotides presenting the most negative binding energies allows us to stabilize hygB in a suitable binding pocket of the 30S ribosomal subunit. In addition, it should be highlighted that mutations in these residues may probably lead to resistance to ribosome-targeting antibiotics. Quantum calculations of aminoglycoside hygromycin B–ribosome complex might contribute to further quantum studies with antibiotics like macrolides and other aminoglycosides.

RNA–ligand molecular docking: Advances and challenges

“…Among the tested methods, the activation barriers calculated from spincomponent scaled Møller-Plesset (SCS-MP2) method and hybrid MPW1K functional show the best agreement with those derived from the experimental rate constant data. Recently, Bezerra et al 68 have applied MFCC fragmentation scheme [287][288][289] within a density functional theory framework to calculate the interaction energy between ribosomal RNA and aminoglycoside hygromycin B. The calculation has revealed the regions where the drug molecule interacts strongly with the ribosome, and the result provides guidance for the improvement of drug-receptor affinity.…”

“…Third, the fact that we have a limited number of experimentally determined structures for RNA molecules and RNA–ligand complexes makes pure knowledge‐based approaches less effective for RNA–ligand predictions. In this regard, a physics‐based approach or a hybrid knowledge‐based and physics‐based approach can yield unique advantages 57,59–71 …”

“…In this regard, a physics-based approach or a hybrid knowledge-based and physics-based approach can yield unique advantages. 57,[59][60][61][62][63][64][65][66][67][68][69][70][71] Although successful computational tools have been developed for protein-ligand binding, [72][73][74][75] the difference in chemical structure and energetics between RNA and proteins demands new methods for RNA-ligand interactions. These new methods for small molecule selection, shown in Figure 1, are necessary for virtual screening, binding mode identification, and binding affinity prediction of specific RNA targets.…”

Quantum Biochemical Investigation of Lys49-PLA2 from Bothrops moojeni

“…moojeni homologous phospholipase A 2 is obtained using quantum mechanical algorithms within the MFCC-based scheme (see below). Proposed by Zhang and Zhang in 2003, the MFCC technique has been widely used to obtain the binding intermolecular energy of protein–ligand and protein–protein complexes with accuracy (see refs , and to cite just a few). The main idea of the technique is to decompose the protein into individual residues and shield the peptide bonds with a pair of conjugate caps …”

“…Presently, fragment-based approaches have been a good ally to reduce the computational cost, enabling working on a linear processing scale while preserving the precise molecular description. , In this sense, classes of linear scaling methods and schemes for protein decomposition were developed to obtain protein properties, including the molecular fractionation with conjugate caps (MFCC) method, the fragment molecular orbital (FMO) method, the molecular tailoring approach (MTA), and generalized energy-based fragmentation (GEBF). , For instance, the MFCC scheme has been successfully applied in proteins, allowing us to calculate the interaction energies in protein–ligand and protein–protein complexes, enabling the investigation of numerous amino acids with a low processing time and high accuracy. − …”