Depression is a psychiatric disease marked by a pathologically low mood (hypothymia), negative self-esteem, and unfavourable outlook on one’s present and future. The word “depression” comes from the Latin word depression, which means “gloominess, oppression”.1 In other words, with a complex etiopathogenesis based on a wide range of factors that may function at different levels, such as psychological, biochemical, genetic, and social features, major depressive disorder is a challenging and varied illness.

An ion channel and glutamate receptor found in neurons is called the N-methyl-D-aspartate receptor (often referred to as the NMDA receptor or NMDAR). The NMDA receptors are composed of a common NMDA1 sub-unit and one of the four NMDA2 sub-units (2A, 2B, 2C, or 2D), linked in an undetermined ratio to create the receptor complex.

The NMDAR-NR2B (GRIN2B) gene has 13 exons and is 419 kb in size. It is localised at 12p12. Expression of this gene is in the cerebral cortex, basal ganglia, and hippocampus.2 It was linked to alcohol consumption habits, Alzheimer’s disease, schizophrenia risk, and obsessive-compulsive disorder susceptibility.3 A silent mutation caused by the GRIN2B polymorphism C2664T in exon 13, rs1806201, results in the substitution of the codon ACT for ACC, which code for threonine. Silent mutations do not change the functional aspects of proteins, although they are not necessarily evolutionary neutral. Codons being less stringent at the third position minimizes the chances of mutations. Splicing or transcriptional regulation may be impacted by silent alterations as well.

The neuronal N-methyl-D-aspartate receptor (NMDAR) is important in the pathophysiology of schizophrenia, bipolar disorder, and depression, according to Mundo et al and Weickert et al.4–5

A SNP (Single Nucleotide Polymorphism) is a single nucleotide mutation that alters the A, T, C, or G sequence in DNA. About 90% of human genetic diversity is accounted for by SNPs. SNPs with densities ranging from 100 to 300 bases apart are present in the 3 billion-base human genome.6 SNPs have an impact on both the coding and non-coding parts of the genome. SNPs can alter how drugs interact with the body, have no effect on cell function, or cause disease, among other outcomes. Non-synonymous SNPs (nsSNPs), which cause amino acid substitutions in protein products, account for nearly half of all known genetic variants associated with human inherited diseases. This makes them particularly significant.7 However, transcription factor binding, gene expression, and splicing can all be affected by non-coding and coding synonymous SNPs (nsSNPs).8–9

SNPs should be recognized because they elicit particular traits. As a result, SNP detection is crucial. Because it requires evaluating thousands of SNPs in potential genes, this is a challenging endeavor.10 Choosing which SNPs to include in a study examining the significance of SNPs in disease is always a challenging decision. Bioinformatics prediction algorithms may be used to isolate functional and neutral SNPs under these conditions. It can also provide an explanation for mutations’ structural aspects. Simply put, bioinformatics strategies arrange SNPs in order of their functional importance.11–12

When conducting in silico genetic analysis, bioinformatics methods eliminate the need to screen a large number of individuals in order to locate genetic disease associations with sufficient statistical significance. As a result, these techniques enable SNP preselection.10 Before using wet experimental methods, it is very convenient to be able to distinguish disease-associated SNPs from neutral SNPs. When subsequent independent studies fail to identify the disease state, in silico analysis is helpful.11 Consequently, independent evidence of the SNP function that has been discovered through the use of predictive algorithms can be used in conjunction with additional resources to distinguish between true positives and false positives.

The relationship between the GRIN2B gene and depression may be established through in silico analysis. The goal of the proposed study was to look at all GRIN2B SNPs that are caused by missense mutations that are linked to depression. The mutation’s structural foundation is also examined in this investigation. These bioinformatics tools are merely tools for putting SNPs in order of importance to their functions. For silico genetic analysis, bioinformatics software has the potential to study gene-disease associations at a statistically significant level without having to study a significant number of people. In a nutshell, these methods can be used for SNP preselection.

The objective of the in silico study was to evaluate GRIN2B gene using bioinformatics softwares, sorting the intolerant from tolerant (SIFT), Polyphen-2, CADD, metaLR, mutation assessor and protein-protein interactions (PPIs) by string database so as to identify the probable deleterious effect of mutations as well as protein-protein interactions of these genes in the pathobiology of depression.