COMPUTER–AIDED APPROACHES TO DISCOVERY OF NOVEL DRUGS AGAINST THE HUMAN HOOKWORM NECATOR AMERICANUS (NEMATODA: ANCYLOSTOMATIDAE)

ABSTRACT There is a crucial need to develop novel anthelminthic drugs due to the mounting disease burden and increasing evidence of hookworm resistance to drugs such as albendazole and mebendazole, which for decades have been used to treat the infection. Consequently, it is exigent to develop alternative drugs with improved therapeutic efficacy. Natural products due to their unique active ingredients have been shown to possess exceptional structures with chemical diversity that is unmatched by any synthetic libraries. It is imperative to leverage natural products to augment hookworm drug discovery. Therefore, this study aimed to: (i) identify potential novel anthelminthic lead compounds by screening African natural product-derived ligands against beta tubulin of Necator americanus, a known hookworm receptor and (ii) develop support vector machine-based proteochemometric modelling (PCM) for bioactivity profiling of beta tubulins receptors. The 3D structure of the beta tubulin of hookworm with UniProt entry W2T758, was generated using homology modelling. The model was subjected to molecular dynamics simulations and active site interactions prediction. The first set of ligand libraries comprising 885 natural product compounds obtained from African medicinal plants database (AfroDb) combined with Dichapetalin A, were screened against the receptor. ZINC14760755 and ZINC28462577 compounds were found to be potential leads due to promising binding affinity, active site interactions and pharmacokinetic profiles. Additionally, a second set comprising 2297 compounds derived from Northern African Natural Product Database (NANPDB) were virtually screened. The compound S,5Z,8Z,11Z,13E,17Z-15-hydroxy-1-(2,4,6-trihydroxyphenyl)-15-methylicosa5,8,11,13,17-pentaen-1-one exhibited plausible binding affinity, toxicity and  pharmacokinetic profile. The aforementioned natural compounds are potential leads which can be experimentally characterised for possible pre-clinical trials. Support vector machine based proteochemometric modelling was also developed to predict the bioactivity relations between beta tubulin variants and small compounds using an interaction dataset retrieved from BindingDB. The model achieved reasonably good performance with a ROC-AUC of 87%, an MCC of 0.75 and a classification error of approximately 4%, although it was trained on a small dataset. The model allows the prediction of the likelihood of interactions between query datasets comprising ligands in SMILES format and protein sequences of beta tubulin targets. In future, larger bioactive datasets of beta tubulins originating from high throughput experiments can be utilised to possibly enhance the performance of the hookworm PCM model.