Computational analysis of common gene and design protein-drug interaction network for the target diseases based on protein-protein interaction network in bioinformatics

Abstract

Influenza virus and Streptococcus pneumoniae bacteria affect millions of people with high mortality rates. SARS-CoV-2, the virus that causes COVID-19, has posed a severe global threat in the 21st century. Bioinformatics tools have evolved as an effective platform for combating the disease in the pandemic since the release of SARS-COV-2 viral genome data. Identification of common genes using bioinformatics could reveal the genes involved in the cohesive interaction among diseases such as Influenza, Streptococcus pneumoniae, and Coronavirus. The primary purpose of this study is to trace genetic variants of three significant ailments and build the Protein-Protein Interaction (PPI) and Protein-Drug Interaction (PDI) networks, the Co-expression network, and the Structural Interaction network. We investigated a computational analysis of the target disorders' PPI and PDI networks. We retrieved the related genes for these three diseases from the National Center for Biotechnology Information gene repository. After recovering genes, we conducted an investigation that involved preprocessing, filtering, sorting, and gene mining on the collected genetic data for Influenza, Streptococcus pneumoniae, and Coronavirus using R to identify common associated genes through a reduction process. According to the findings, more than 80% of the genes acquired from several gene databases are responsible for three diseases. The number of genes identified for the three illnesses was reduced to 17% during preprocessing and screening and finally extracted 1% with the R toolkit. The intersection process decreases the commonly linked genes employed in gene extraction. The computational analysis of this study reveals twenty-one (21) common genes among the diseases. This information could be valuable for researchers in developing new therapeutic compounds that could help to reduce the impact of the three diseases in the future.