Malaria is caused by apicomplexan parasites, has proven to be the deadliest infectious disease. It is an overwhelming global burden in the tropical and sub-tropical regions of the world. The World Health Organization estimated over 229 million cases and 4,09,000 casualties in 87 endemic countries in 2019. The treatment of malaria is becoming more challenging due to drug resistance of Plasmodium to traditional antimalarial drugs, and relapse of the disease. The only savior is Atermisinin; the resistance of the parasite towards Artemisinin and Artemisinin Combination Therapies has been observed. To overcome this drug resistance, validation of new drug targets and drug molecules is much needed. The ideal new target possesses a new mechanism of action, expedites reduction of the parasite in the erythrocyte stage, and retains efficacy in all stages of the life cycle of the parasite in the human host. The aminoacyl t-RNA synthetase (aaRS) are indispensable for the synthesis of proteins of the parasite and therefore are ideal drug targets. The aaRS render amino acids to cognate tRNAs, thereby enable elongation of the protein chain. Inhibiting aaRSs leads to cell growth disruption. Despite all the efforts, the success rate of drug discovery against aaRS remains low. Secondly, a designed inhibitor that is selectively inhibited to plasmodium rather than its homologous to human protein has been challenging to find. To overcome pitfalls, the investigations of cytosolic phenylalanine t-RNA synthetase (cFRS) as a drug target have been proposed which was earlier genetically and biochemically validated as a drug target. In this research study, we are probing scaffolds using pharmacophore screening against the proteins <i>P.vivax</i> and <i>Homo sapiens</i> to generate selectively binding novel inhibitors as antimalarials using an advanced computational biology approach. The top putative hits molecules will be further taken to the next phase of the drug discovery pipeline.

Identification of putative small molecules against cFRS for malaria using computational investigations

ACS Spring 2022

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