COMPARATIVE ANALYSIS OF DRUG METABOLITES ON the COMPLEX BRAIN DISORDER SCHIZOPHRENIA

Abstract

Schizophrenia is a chronic psychological disorder that is well known for its complexity and difficulty in diagnosis as well as treatment. Various healthcare Informatics data generated through in vivo, in vitro and in silico based approaches for this disease are available through biological databases and scientific search engines. Implementing traditional methods of browsing scientific information in literature databases like PUBMED to browse the molecular basis of a complex brain disorder like schizophrenia revealed the involvement of hundreds of gene with the disorder. It urged for the necessity to adopt some specialized experimental design including more relevant and reliable data mining technologies and methodologies to screen out the key player genes and proteins involved with our targeted disorder. In the present study, an attempt has been made to discover the most important genes and proteins involved in the Schizophrenia from all that have been reported till date by computing Average Normalized Database (AvNrD) scores of the genes. The Protein sequence dataset is prepared by merging molecular level data of Human diseases from 8 standard data repositories viz. GWAS, GLAD4U, UniProt, DisGeNET, GenAtlas, PharmGKb, GenCards, and DrugBank. The dataset is subjected to two levels of screening by performing STRING network analysis and then by calculating the Average Normalized Database Scores. After all tedious observations and analysis interestingly, it is revealed that the human chromosome No. 22 is highly enriched with schizophrenia associated genes, most of the genes are linked with more than one disorder along with schizophrenia, most of the proteins are membrane proteins and very less proteins are available with drugs approved for the disorder. Dopamine receptor 2 (DRD2), Serotonin receptor 2A (HTR2A) and Glutamate receptor 3 (GRM3) are found to be the most important proteins with significantly high AvNrD scores of 9.436725, 7.121672 and 7.709488 respectively out of 1229 genes which are likely to