Prediction of antibiotic target proteins of streptococcus pyogenes using network pharmacology approach

Abstract

Many extracellular components secreted by Group A Streptococcus, also called S. pyogenes, are believed to be virulence factors. Therefore, this study aimed to predict antibiotic target proteins from S. pyogenes, rank the proteins based on their text-mining scores using the network pharmacology approach and provide an array of genes that could be identified following genome sequencing techniques. Cytoscape software version 3.9.1 was employed in this process. STRING; PubMed query was used as the data source to import networks from public databases . The species selected was Streptococcus pyogenes, and the PubMed query was Antibiotics Resistance Streptococcus. The confidence score was set at 0.7 while the maximum number of S. pyogenes target proteins to be downloaded was 50. The PubMed query returned 15,614 results, of which 9999 were downloaded, including target Proteins from S. pyogenes species widely mentioned in published abstracts. Nodes in networks represent the proteins, and edges indicate interactions between the proteins. The outcome displayed target proteins for S. pyogenes, including Pbp1A, gyrA, parC, MefE, gyrB-2, folP, adhP, GlcK, pheS, AKZ49992.1, and scpB, 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 (pbp1A) having the highest score. Pbp1A, gyrA, parC, MefE, gyrB-2, folP, adhP, GlcK, pheS, AKZ49992.1, and scpB were predicted as proteins with high text-mining evidence in S.pyogenes resistance using the network pharmacology approach.

Keywords

• s. pyogenes, resistance, infection, network pharmacology, protein interaction