ABSTRACT. Tuberculosis continues to be a major cause of mortality worldwide despite significant advances in chemotherapy and development of the BCG vaccine. Although curable, the tuberculosis treatment period (6-9 months) presents many concerns, including patient noncompliance and the development of drug toxicity and drug resistance. This study aimed to understand the protein-protein interactions of key proteins involved in the Mycobacterium tuberculosis STPK signal transduction pathway (such as PknB, PknE, and PstP); in addition, we attempted to identify promising leads for the inhibition of protein-protein interactions. Interactome analyses revealed the interactions of these protein targets with several other proteins, including PknG and PbpA. Drug-like candidates were screened based on Lipinski's rule of five and the absorption digestion metabolism (2015) excretion toxicity. Molecular docking of the target proteins with the selected ligands identified cryptolepine HCl to be a common molecule interacting with all protein targets (with a good docking score). The generation of a pharmacophore model for cryptolepine HCl revealed three pharmacophoric regions: aromatic hydrocarbon, hydrogen bond acceptor, and hydrogen bond donor, which play important roles in its interaction with the protein targets. Therefore, cryptolepine HCl appears to be a promising drug candidate for further optimization and validation against M. tuberculosis.

A quantitative structure activity relationship (QSAR) study was performed on the fluroquinolones known to have anti-tuberculosis activity. The 3D-QSAR models were generated using stepwise variable selection of the four methods - multiple regression (MR), partial least square regression (PLSR), principal component regression (PCR) and artificial neural networks (kNN-MFA). The statistical result showed a significant correlation coefficient q2 (90%) for MR model and an external test set of (pred_r2) -1.7535, though the external predictivity showed to improve using kNN-MFA method with pred_r2 of -0.4644. Contour maps showed that steric effects dominantly determine the binding affinities. The QSAR models may lead to a better understanding of the structural requirements of anti-tuberculosis compounds and also help in the design of novel molecules.

Abstract:A quantitative structure activity relationship (QSAR) study was performed on the fluroquinolones known to have anti-tuberculosis activity. The 3D-QSAR models were generated using stepwise variable selection of the four methods -multiple regression (MR), partial least square regression (PLSR), principal component regression (PCR) and artificial neural networks (kNN-MFA). The statistical result showed a significant correlation coefficient q 2 (90%) for MR model and an external test set of (pred_r 2 ) -1.7535, though the external predictivity showed to improve using kNN-MFA method with pred_r 2 of -0.4644. Contour maps showed that steric effects dominantly determine the binding affinities. The QSAR models may lead to a better understanding of the structural requirements of anti-tuberculosis compounds and also help in the design of novel molecules.

The taxonomic status of Euryops jaberiana Abedin & Chaudhary (tribe Senecioneae, family Asteraceae), endemic to northern Saudi Arabia was evaluated based on molecular phylogenetic analyses of internal transcribed spacer sequence (ITS) of nuclear ribosomal DNA (nrDNA) in order to ascertain its position within the genus. The phylogenetic tree constructed by the Neighbour Joining, Maximum Parsimony and Maximum Likelihood analyses showed a clear resolution of taxon included in the analyses at the level of sections, and E. jaberiana nested within the clade of the section Angustifoliae. E. jaberiana showed proximity with the allied species E. arabicus; however, a total number of eight nucleotide differences were evident between E. jaberiana and E. arabicus, indicating E. jaberiana as distinct from its allied species.

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