Prediction of antimalarial target proteins in Plasmodium falciparum using network pharmacology approach

Resistance has developed to all known antimalarials; 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, utilizing a network pharmacology approach. The Cytoscape program (v3.9.1) was used. StringApp (v2.0.1) was downloaded from the Cytoscape App Store and installed in Cytoscape. From the "File" menu in Cytoscape, we clicked on "Import" and then "Network from Public Database." To import STRING networks from public databases, the data source was STRING, PubMed query. The species chosen was Plasmodium falciparum, and the PubMed search term was "Antimalarial Resistance Plasmodium falciparum". The confidence score was set at 0.7, and the maximum number of P. falciparum resistance proteins to download was 50. The target proteins were subsequently ranked from top to bottom in terms of text-mining scores, providing a list of genes that could be identified following genome sequencing techniques. The PubMed query yielded 17,540 results, of which 8460 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 protein (PfCRT) having the highest score. Using the network Pharmacology technique, we identified PfCRT, PfMDR1, DHPS, PfMRP, MSP2, MSP1, PF14_0077, PF10_0344, DHFR-TS, and PfMDR2 as target proteins with high text-mining evidence for P. falciparum resistance.