Computational Analysis of a Marine-Derived Drug From Rhizophora mucronata Against the Capsid Protein of Rubella Virus

Background Rubella, commonly known as German measles, is caused by a single-stranded RNA genome. Vaccination is currently the most effective method for preventing rubella and its complications. Molecular docking, a computer-based technique used in drug discovery and development, is used to investigate the interactions between potential drug candidates and their target proteins. It predicts the binding interactions between small molecules (ligands) and the target protein. In this study, we examined a marine-derived drug from <i>Rhizophora mucronata</i> for its potential antiviral properties against the rubella capsid virus. Our objective was to identify the active inhibitory sites of the capsid virus. Materials and methods Protein and ligand molecules were retrieved from Protein Data Bank (PDB) and PubChem databases. The Lamarckian genetic algorithm was used to calculate molecular docking using Autodock Tools 1.5.7. The docking parameters used for each docked molecule were determined from 100 separate docking experiments with a maximum of 2.5×10<sup>-6</sup> energy and a mutation rate of 2.0 and mass over ratio of 0.8. The results were recorded as docking parameter files (DPF). PyMOL was used to view and investigate the interactions between ligand fragments and rubella capsid protein. Results This approach plays a crucial role in the development of structure-based drugs. The results of the molecular docking suggest that Rhizophorin has the potential to bind with the rubella capsid protein. The strong binding affinity of -6.05 kcal/mol between the ligand and the protein further supports the potential of Rhizophorin as a therapeutic agent. The formation of hydrogen bonds between the ligand and amino acid residues Glu79, Arg82, and Thr118 indicates the significance of electrostatic interactions in the binding process. Furthermore, the hydrophobic interactions between the ligand and residues Ala81, Val84, Leu87, and Ile119 suggest the role of non-polar interactions in stabilizing the complex. The identified amino acid residues involved in these binding interactions could serve as potential targets for drug development. In future studies, experimental validation of the predicted interactions could provide further insights into the potential of Rhizophorin as an antiviral agent. Conclusion According to the findings of this study, the in silico investigation successfully identified a target for inhibiting the rubella virus (RuV) capsid receptor molecule. Future investigations on these compounds will require in vitro and in vivo studies using models that are more relevant to the medicinal potential of the capsid protein molecule.

Authors

Institutions

Publication Details

Journal
Cureus
Published
2024-08-20
DOI
https://doi.org/10.7759/cureus.67352
Citations
2
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.72

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Computational Analysis of a Marine-Derived Drug From Rhizophora mucronata Against the Capsid Protein of Rubella Virus

Elangovan Dilipan, KLG Afeeza, M Protim, S. Vasugi
2 citations
Cureus
Computational Drug Discovery Methods
0.72
article

Computational Analysis of a Marine-Derived Drug From Rhizophora mucronata Against the Capsid Protein of Rubella Virus

Elangovan Dilipan, KLG Afeeza, M Protim, S. Vasugi
article en
2 citations

Abstract

Background Rubella, commonly known as German measles, is caused by a single-stranded RNA genome. Vaccination is currently the most effective method for preventing rubella and its complications. Molecular docking, a computer-based technique used in drug discovery and development, is used to investigate the interactions between potential drug candidates and their target proteins. It predicts the binding interactions between small molecules (ligands) and the target protein. In this study, we examined a marine-derived drug from <i>Rhizophora mucronata</i> for its potential antiviral properties against the rubella capsid virus. Our objective was to identify the active inhibitory sites of the capsid virus. Materials and methods Protein and ligand molecules were retrieved from Protein Data Bank (PDB) and PubChem databases. The Lamarckian genetic algorithm was used to calculate molecular docking using Autodock Tools 1.5.7. The docking parameters used for each docked molecule were determined from 100 separate docking experiments with a maximum of 2.5×10<sup>-6</sup> energy and a mutation rate of 2.0 and mass over ratio of 0.8. The results were recorded as docking parameter files (DPF). PyMOL was used to view and investigate the interactions between ligand fragments and rubella capsid protein. Results This approach plays a crucial role in the development of structure-based drugs. The results of the molecular docking suggest that Rhizophorin has the potential to bind with the rubella capsid protein. The strong binding affinity of -6.05 kcal/mol between the ligand and the protein further supports the potential of Rhizophorin as a therapeutic agent. The formation of hydrogen bonds between the ligand and amino acid residues Glu79, Arg82, and Thr118 indicates the significance of electrostatic interactions in the binding process. Furthermore, the hydrophobic interactions between the ligand and residues Ala81, Val84, Leu87, and Ile119 suggest the role of non-polar interactions in stabilizing the complex. The identified amino acid residues involved in these binding interactions could serve as potential targets for drug development. In future studies, experimental validation of the predicted interactions could provide further insights into the potential of Rhizophorin as an antiviral agent. Conclusion According to the findings of this study, the in silico investigation successfully identified a target for inhibiting the rubella virus (RuV) capsid receptor molecule. Future investigations on these compounds will require in vitro and in vivo studies using models that are more relevant to the medicinal potential of the capsid protein molecule.

CureusVol. 16(8)
Saveetha University (IN)
Saveetha Dental College
Openalex Percentile: Top 27%
Computational Drug Discovery Methods
0.72
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.