Functional analysis of depression associated proteins: role of TRK, BDNF, CYP2B6, POLG, PICK1 Biomarkers for early detection and treatment of depression

Abstract

Psychiatric diseases relentlessly distress the ability of work also productivity of a person’s life. Considering the phenotypic unpredictability amongst patients, it is very hard to understand the pathogenesis. The study aims to identify the pathways responsible for psychiatric disease, especially for depression. Initially 62 depression associated proteins were listed from the UniProt and then hub genes of those proteins were identified. For functional annotation analysis, proteins UniProt IDs were submitted to the Database for Annotation, Visualization and Integrated Discovery shortly known as DAVID. Gene ontologies, protein domains, and pathways were analyzed using the GO enrichment and KEGG. The functional annotation clustering identified a total of 150 GO terms clustered into 23 groups. The pathways, identified from the clustering and KEGG, were overlapped to construct a Protein Protein Interaction (PPI) network. Finally, the common pathways were separated and 300 selected anti-depressants drugs from 5 classes were docked with depression associated proteins such as iGluR DELTA-2 (PDB ID- 5KC8), Dopamine Receptor, D2 (PDB ID- 6CM4), Sodium Dependent Serotonin Transporter (PDB ID- 2KS9), Glutamate receptor ionotropic, NMDA 2B (PDB ID- 5EWL) etc. Six anti-depressant drugs such as sertraline carbamoyl, norethindrone, and aripiprazole had good binding affinities (-10.5, -10.4 and -9.3 respectively) with the proteins of interest. The study also revealed that biomarkers like, TRK, BDNF, CYP2B6, POLG, PICK1 could be suitable for early detection of depression. Building on these findings, studies could be designed to target and examine protein clusters to understand depression and its transduction targets to identify functional biomarkers for early diagnosis.