Integrated computational metabolomics and cheminformatic profiling of Pseudomonas aeruginosa LasR modulators from hybrid sunflower seed metabolites

Abstract Hybrid sunflower ( Helianthus annuus ) seeds and metabolome are underexplored relative to non-hybrid variants, and may hold potential health benefits for discovery of novel quorum sensing (QS) modulators. Hence, this study utilized computational metabolomics in profiling six hybrid H. annuus cultivars, and cheminformatics to model metabolites against the key sensing protein of Pseudomonas aeruginosa , LasR. Chemometrics modelling showed cultivar-related metabolite profiles [more chlorogenic acids and calceolariosides in Syngenta (SY), Pannar and P65 cultivars]. Findings revealed 60 putatively annotated metabolites with 12,13-DiHOME, xanthosine, calceolarioside A and B, farnesol, and cotinine reported for the first time in sunflower seeds. Three lead metabolites (calceolarioside B, rosmarinic and chlorogenic acids) formed thermodynamically stable interactions with key residues (Tyr88, Phe96, Thr110, and Ser124) following a 100 ns simulation at the LasR active site. Lead ligands-LasR complexes also showed significant hydrophobic and hydrogen bond interactions. All leads showed lowest binding free energy (∆G bind ) from − 53.46 kcal/mol (calceolarioside B) to – 45.51 kcal/mol (rosmarinic acid) compared to the autoinducer (– 41.12 kcal/mol) and reference standard, cinnamaldehyde (– 16.30 kcal/mol). Pharmacokinetics prediction revealed that leads were explorable within and beyond Lipinski’s rule of 5. Quantum reactivity attributes supported the streamlined selection of the top two metabolites, calceolarioside B and rosmarinic acid. This study generated first time, comprehensive metabolomic landscape/richness (terpenoids, phenolic acids, alkaloids, amino acids) of six H. annuus hybrid seed cultivars, thus giving credence to the agricultural development of these seed variants. The identified lead metabolites could be explorable as novel LasR modulators following added validation ( in vivo , in vitro ) steps.