Repurposing Anti-Dengue Compounds against Monkeypox Virus Targeting Core Cysteine Protease.
Mohd Imrannull AbidaNawaf M AlotaibiHamdy Khamees ThabetJamal Alhameedi AlruwailiLina EltaibAhmed AlshehriAhad Amer AlsaiariMehnaz KamalAbdulmajeed Mohammed Abdullah AlshammariPublished in: Biomedicines (2023)
The monkeypox virus (MPXV) is an enveloped, double-stranded DNA virus belonging to the genus Orthopox viruses. In recent years, the virus has spread to countries where it was previously unknown, turning it into a worldwide emergency for public health. This study employs a structural-based drug design approach to identify potential inhibitors for the core cysteine proteinase of MPXV. During the simulations, the study identified two potential inhibitors, compound CHEMBL32926 and compound CHEMBL4861364, demonstrating strong binding affinities and drug-like properties. Their docking scores with the target protein were -10.7 and -10.9 kcal/mol, respectively. This study used ensemble-based protein-ligand docking to account for the binding site conformation variability. By examining how the identified inhibitors interact with the protein, this research sheds light on the workings of the inhibitors' mechanisms of action. Molecular dynamic simulations of protein-ligand complexes showed fluctuations from the initial docked pose, but they confirmed their binding throughout the simulation. The MMGBSA binding free energy calculations for CHEMBL32926 showed a binding free energy range of (-9.25 to -9.65) kcal/mol, while CHEMBL4861364 exhibited a range of (-41.66 to -31.47) kcal/mol. Later, analogues were searched for these compounds with 70% similarity criteria, and their IC 50 was predicted using pre-trained machine learning models. This resulted in identifying two similar compounds for each hit with comparable binding affinity for cysteine proteinase. This study's structure-based drug design approach provides a promising strategy for identifying new drugs for treating MPXV infections.
Keyphrases
- public health
- binding protein
- machine learning
- molecular dynamics
- protein protein
- emergency department
- molecular dynamics simulations
- healthcare
- mass spectrometry
- amino acid
- climate change
- transcription factor
- dna binding
- deep learning
- drug delivery
- cell free
- cancer therapy
- molecular docking
- resistance training
- convolutional neural network