Description
Malaria caused by the parasite Plasmodium falciparum (Pf), remains a significant global health concern due to its high mortality rate and widespread prevalence. The emergence of drug resistance to artemisinin-based combination therapies poses a serious threat to malaria treatment. To address this challenge, novel approaches for combating malaria are continuously explored, with Heat Shock Proteins (hsps) that play a vital role in parasite survival and growth and these hsps have been identified as potential drug targets.
While the 3-Dimensional (3D) crystal structure of the PfHsp70z protein has not been elucidated, homology modelling was performed using Modeller on the PRIMO server (http://primo.rubi.ru.ac.za) to generate a 3D structure. The resulting model underwent evaluation using the PROSA webserver (https://prosa.services.came.sbg.ac.at/prosa.php) and Ramachandran plots, showing a favourable Global model quality Z-score of -8.54, with 91.2% of residues falling within the allowed region of the Ramachandran Plot.
Molecular docking on ICM Molsoft, using ATP as the natural known substrate, revealed ATP binding in the nucleotide binding domain with a binding energy of -7.19 kcal/mol. The ICM pocket predictor identified the binding pocket, with the highest volume of 525.4A3 and a druggability score of 1.09.
The generated model underwent further optimization through 200ns molecular dynamics simulations using GROMACS for energy minimization. Structure-based virtual screening was conducted using ligand compounds from the Malaria Medicine Venture (MMV) and H3D datasets on ICM Molsoft. The compounds were ranked based on their binding energies relative to ATP (-7.19 kcal/mol), and the analysis of essential binding residues similar to those in the PfHsp70z-ATP complex (ASN206, ASN12, ASN10, ASP173, LYS305, LYS68, ASP399, TYR204, ASN201, and ALA205) forming hydrogen bonds within a distance of 3.3Å or less. As a result, 34 compounds from the MMV dataset and 16 compounds from the H3D dataset were identified as potential competitive inhibitors.
Absorption, distribution, metabolism, excretion and toxicity (ADMET) predictions were conducted to ensure the safety profiles of the selected PfHsp70z-ligand pairs. Among the compounds from the MMV and H3D datasets, 22 and 7 compounds, respectively, exhibited favorable safety profiles. Five compounds from the MMV dataset were chosen for further bioassay analysis.
These findings demonstrate the potential of targeting PfHsp70z as a non-conventional approach for antimalarial drug discovery and also artificial intelligence (AI) plays a significant role in enhancing and accelerating structure based drug design. Further studies and experimental validation of the selected compounds are necessary to evaluate their efficacy and potential as novel antimalarial agents.
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