Antimicrobial phytoconstituents from Azadirachta indica (neem) with potential inhibitor against FtsZ protein of Pseudomonas aeruginosa

Pseudomonas aeruginosa is a well-known pathogen for its rapid development of multi-drug antibiotic resistance. This pathogen is responsible for numerous human diseases, particularly affecting immunocompromised and elderly patients. Hence, discovering novel therapeutics has become necessary in the fight against antimicrobial resistance. This study is focused on evaluating the potential inhibitory activity of eleven phytocompounds from Azadirachta indica against the nucleotide-binding site of the FtsZ protein of P. aeruginosa through a cheminformatics approach. FtsZ is an indispensable and highly conserved protein in prokaryotic cell division. Docking studies revealed favourable binding energies (ΔG= - 8.3 to - 5.4 kcal/mol) for all selected phytoconstituents. Finally, we selected Nimbiol (CID 11119228), as a lead compound, exhibiting a binding energy (ΔG= -7.8 kcal/mol) for the target. Based on our findings, Nimbiol shows potential as an anti-FtsZ compound, making it a promising candidate for further in vitro and in vivo investigations to assess its antimicrobial activity.

Exploring the QSAR's predictive truthfulness of the novel N-tuple discrete derivative indices on benchmark datasets

Graph derivative indices (GDIs) have recently been defined over N-atoms (N = 2, 3 and 4) simultaneously, which are based on the concept of derivatives in discrete mathematics (finite difference), metaphorical to the derivative concept in classical mathematical analysis. These molecular descriptors (MDs) codify topo-chemical and topo-structural information based on the concept of the derivative of a molecular graph with respect to a given event (S) over duplex, triplex and quadruplex relations of atoms (vertices). These GDIs have been successfully applied in the description of physicochemical properties like reactivity, solubility and chemical shift, among others, and in several comparative quantitative structure activity/property relationship (QSAR/QSPR) studies. Although satisfactory results have been obtained in previous modelling studies with the aforementioned indices, it is necessary to develop new, more rigorous analysis to assess the true predictive performance of the novel structure codification. So, in the present paper, an assessment and statistical validation of the performance of these novel approaches in QSAR studies are executed, as well as a comparison with those of other QSAR procedures reported in the literature. To achieve the main aim of this research, QSARs were developed on eight chemical datasets widely used as benchmarks in the evaluation/validation of several QSAR methods and/or many different MDs (fundamentally 3D MDs). Three to seven variable QSAR models were built for each chemical dataset, according to the original dissection into training/test sets. The models were developed by using multiple linear regression (MLR) coupled with a genetic algorithm as the feature wrapper selection technique in the MobyDigs software. Each family of GDIs (for duplex, triplex and quadruplex) behaves similarly in all modelling, although there were some exceptions. However, when all families were used in combination, the results achieved were quantitatively higher than those reported by other authors in similar experiments. Comparisons with respect to external correlation coefficients (q²_ext) revealed that the models based on GDIs possess superior predictive ability in seven of the eight datasets analysed, outperforming methodologies based on similar or more complex techniques and confirming the good predictive power of the obtained models. For the q²_ext values, the non-parametric comparison revealed significantly different results to those reported so far, which demonstrated that the models based on DIVATI's indices presented the best global performance and yielded significantly better predictions than the 12 0-3D QSAR procedures used in the comparison. Therefore, GDIs are suitable for structure codification of the molecules and constitute a good alternative to build QSARs for the prediction of physicochemical, biological and environmental endpoints.