From Proteome to Potential Drugs: Integration of Subtractive Proteomics and Ensemble Docking for Drug Repurposing against Pseudomonas aeruginosa RND Superfamily Proteins.
Gabriela UrraElizabeth Valdés-MuñozReynier SuardíazErix W Hernández-RodríguezJonathan M PalmaSofía E Ríos-RozasCamila A Flores-MoralesMelissa Alegría-ArcosOsvaldo YáñezLuis Morales-QuintanaVivían D'AfonsecaDaniel BustosPublished in: International journal of molecular sciences (2024)
Pseudomonas aeruginosa ( P. aeruginosa ) poses a significant threat as a nosocomial pathogen due to its robust resistance mechanisms and virulence factors. This study integrates subtractive proteomics and ensemble docking to identify and characterize essential proteins in P. aeruginosa , aiming to discover therapeutic targets and repurpose commercial existing drugs. Using subtractive proteomics, we refined the dataset to discard redundant proteins and minimize potential cross-interactions with human proteins and the microbiome proteins. We identified 12 key proteins, including a histidine kinase and members of the RND efflux pump family, known for their roles in antibiotic resistance, virulence, and antigenicity. Predictive modeling of the three-dimensional structures of these RND proteins and subsequent molecular ensemble-docking simulations led to the identification of MK-3207, R-428, and Suramin as promising inhibitor candidates. These compounds demonstrated high binding affinities and effective inhibition across multiple metrics. Further refinement using non-covalent interaction index methods provided deeper insights into the electronic effects in protein-ligand interactions, with Suramin exhibiting superior binding energies, suggesting its broad-spectrum inhibitory potential. Our findings confirm the critical role of RND efflux pumps in antibiotic resistance and suggest that MK-3207, R-428, and Suramin could be effectively repurposed to target these proteins. This approach highlights the potential of drug repurposing as a viable strategy to combat P. aeruginosa infections.
Keyphrases
- pseudomonas aeruginosa
- molecular dynamics
- biofilm formation
- escherichia coli
- mass spectrometry
- cystic fibrosis
- staphylococcus aureus
- protein protein
- molecular dynamics simulations
- endothelial cells
- machine learning
- emergency department
- convolutional neural network
- deep learning
- human health
- binding protein
- transcription factor
- methicillin resistant staphylococcus aureus
- amino acid
- adverse drug
- tyrosine kinase
- induced pluripotent stem cells