Identification of natural TLR4 modulators through network pharmacology and molecular modeling in SARS-CoV-2 Acinetobacter baumannii co-infection
TL;DR
These computational findings prioritize curcumin- and baicalin-derived scaffolds as potential TLR4/MD-2 modulators for further experimental investigation in the context of co-infection-associated inflammation.
Abstract
Viral–bacterial co-infection with SARS-CoV-2 and Acinetobacter baumannii exacerbates hyperinflammation via Toll-like receptor 4 (TLR4)-mediated immune pathways. Using network pharmacology, an integrated protein–protein interaction network identified 30 key inter-species hubs comprising viral, bacterial, and human host proteins, with TLR4 achieving a high MCC score (722), highlighting its high topological centrality as a host immune target. Virtual screen of 2,820 natural compounds against the TLR4/MD-2 complex identified three top candidates, CID5898023, CID5403474, and CID74977829, with docking scores of −10.46, −10.02, and −9.58 kcal/mol, respectively, compared with −9.10 kcal/mol for the reference antagonist Eritoran. CID5898023 (curcumin-derived) exhibited a more favorable docking score and stable binding behavior during the molecular dynamics simulation compared with the reference antagonist Eritoran. CID74977829 (baicalin-derived) showed stable binding but lower intestinal absorption, while CID5403474 (chrysin-derived) lacked conformational stability despite favorable docking scores. These computational findings prioritize curcumin- and baicalin-derived scaffolds as potential TLR4/MD-2 modulators for further experimental investigation in the context of co-infection-associated inflammation.