Prediction of Antimalarial Target Proteins in Plasmodium falciparum Using Network Pharmacology Approach

Background and Purpose: Resistance has developed to all known antimalarial medicines. Therefore, tracking the drug sensitivities of clinical infections is crucial for treatment strategies. This study was designed to predict antimalarial target proteins of Plasmodium falciparum that are commonly cited in the STRING database using the network pharmacology approach. Methods: Cytoscape (v3.9.1) and StringApp (v2.0.1) were used to construct and analyse Plasmodium falciparum protein interaction networks related to antimalarial resistance, based on a PubMed query via STRING. A confidence score of 0.7 and a limit of 50 proteins were applied, with results ranked by text-mining scores to identify candidate target proteins. Results: The PubMed query yielded 15,614 results, of which 9999 were downloaded, including target proteins from P. falciparum species that were often cited in published papers. The outcome displayed target proteins for P. falciparum, including PfCRT, PfMDR1, DHPS, PfMRP, MSP2, MSP1, PF14_0077, PF10_0344, DHFR-TS, and PfMDR2 with high text-mining scores arranged in descending order. The text-mining score showed how frequently the target proteins are mentioned in the STRING database, with the most mentioned (PfCRT) given the highest score. Conclusion: Using network Pharmacology technique, PfCRT, PfMDR1, DHPS, PfMRP, MSP2, MSP1, PF14_0077, PF10_0344, DHFR-TS, and PfMDR2 were identified as target proteins with high text-mining evidence for P. falciparum resistance. This can influence experimental and clinical decisions.