Protein Therapeutic Target Candidates Against Acinetobacter baumannii , a Pathogen of Concern to Planetary Health: A Network-Based Integrative Omics Drug Discovery Approach.
Aishwarya SwainArchana PanPublished in: Omics : a journal of integrative biology (2023)
Acinetobacter baumannii , an opportunistic gram-negative pathogen responsible for several nosocomial infections, has developed resistance to various antibiotics. Proteins involved in the two-component system (TCS), virulence, and antibiotic resistance (AR), help this pathogen in regulating antibiotic susceptibility and virulence mechanisms. The present study reports a network-based integrative omics approach to drug discovery to identify key regulatory proteins as therapeutic candidates against A. baumannii . We collected data on the TCS, virulence, and AR proteins from various databases (P2CS, VFDB, ARDB, and PAIDB), which were subjected to network, host-pathogen, and gene expression data analysis. Network analysis identified 43 hubs, and 10 proteins were found to be interacting with human proteins associated with vital pathways. Of the 53 (43 + 10) pathogen proteins, 46 had no orthologs in the human host. Twelve proteins, namely, RpfC, Wzc, OmpR, EnvZ, BfmS, PilG, histidine kinase, ABC 3 transport family protein, outer membrane porin OprD family, CsuD, Pgm, and LpxA, were differentially expressed in the resistant strain. We propose these proteins as key regulators that warrant evaluation as therapeutic target candidates in the future. Furthermore, structure prediction of ABC 3 transport family protein was performed as a case study. The findings from this study are poised to facilitate and inform drug discovery and development against A. baumannii.
Keyphrases
- drug discovery
- acinetobacter baumannii
- multidrug resistant
- pseudomonas aeruginosa
- network analysis
- drug resistant
- gram negative
- staphylococcus aureus
- escherichia coli
- candida albicans
- endothelial cells
- public health
- healthcare
- biofilm formation
- antimicrobial resistance
- mental health
- social media
- single cell
- machine learning
- induced pluripotent stem cells
- climate change
- klebsiella pneumoniae