Identification and in silico analysis of functional SNPs of human TAGAP protein: A comprehensive study

Abstract

Genetic polymorphisms in TAGAP gene have been associated with many diseases including rheumatoid arthritis, multiple sclerosis and other autoimmune disorders. Identifying functional SNPs in such disease associated genes is an uphill task hence before planning larger population study, it is better to scrutinize putative functional SNPs. In this study we used various computational approaches to identify nsSNPs which are deleterious to the structure and/or function of TAGAP protein that might be causing these diseases. Computational analysis was performed by five different in silico tools including SIFT, PROVEAN, PolyPhen-2, PhD-SNP and SNPs&GO. The study concludes that mutations of Glycine → Glutamic Acid at position 120, Glycine → Tryptophan at position 141 and Valine → Methionine at position 151 are major mutations in native TAGAP protein which might contribute to its malfunction and ultimately causing disease. The study also proposed 3D structures of native TAGAP protein and its three mutants. Future studies should consider these nsSNPs as main target mutations in various diseases involving TAGAP malfunction. This is the first comprehensive study, where TAGAP gene variants were analyzed using in silico tools hence will be of great help while considering large scale studies and also in developing precision medicines for cure of diseases related to these polymorphisms. Furthermore, animal models of various autoimmune diseases and having these mutations might be of help in exploring their precise roles.

In silico/In vivo analysis of high-risk papillomavirus L1 and L2 conserved sequences for development of cross-subtype prophylactic vaccine

“…Conservancy analysis showed that five regions of HPV16,18 L1 protein (8-22, 95-132, 307-342, 398-425 and 449-473) and four regions of HPV16,18 L2 protein (11-40, 54-76, 96-120 and 278-305) were more conserved among other subtypes and could be analyzed as an immunoinformatics input. In B-cell epitope prediction, L1 [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] , L1 408-421 , L1 404-417 , L2 [22][23][24][25][26][27][28][29][30][31][32][33][34][35] , L2 100-113 , L2 94-107 and L2 57-70 had the highest epitope prediction scores. Unfortunately, a reliable method for prediction of B-cell epitope has not been revealed up to now and the sensitivity and specificity of existing methods were very low (the specificity and sensitivity of this method were 0.57 and 0.58, respectively).…”

“…In L1 protein, L1 [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] (EATVYLPPVPVSKVV-type16), L1 408-421 (PPPGGTLEDTYRFV-type16) and L1 404-417 (NFGVPPPPTTSLVD-type 18) epitopes had the best B cell epitope identification scores. For L2 protein, L2 [22][23][24][25][26][27][28][29][30][31][32][33][34][35] (KQSGTCPPDVVPKV-type18), L2 100-113 (PSDPSIVSLVEETS-type16), L2 94-107 (EPVGPTDPSIVTLI-type18) and L2 [57][58][59][60][61][62][63][64][65][66][67][68][69][70] (GLGIGTGSGTGGRT-type16) epitopes showed the highest epitope identification score between their own protein sequences.…”

“…The selected helper T Cell epitopes of HPV L2 protein, based on binding affinity. *lower rates show better binding affinity, **Higher rates show better binding affinity.Panahi and colleagues used a two-step method (consist of molecular docking and sequence-based approach) to determine immunogenic epitopes for induction of immune system against the oncoproteins of HPV 16, 18, 31 and 45 types31 . In 2016, Wang and coworkers suggested the regions 51-58, 87-97, 214-220, 290-296, 335-341, 351-366, 408-418, 430-442 and 475-496 as putative B-cell epitopes for HPV16 L1 protein 32 .…”

Computational Analysis of Single Nucleotide polymorphisms (SNPs) in Human TCell Acute Lymphocytic Leukemia Protein 1 (TAL1) Gene/Comprehensive Study

“…This approach has been done for many disorders especially for cancer related genes [35][36][37][38]. Our methods in bioinformatics analysis were in conjunction with previous papers analyzing disease-related genes such as VCAM-1, MSH6, GRM4, MYC and TAGAP genes [39][40][41][42][43].…”

Identification of most damaging nsSNPs in human CCR6 gene: In silico analyses

“…In a study, association of lymphoedema with nsSNPs in FOXC2 gene has been shown which could be a key factor in the said disease (Nimir et al, ). One of the recent studies has shown nsSNPs in TAGAP gene which could have deleterious effect on protein structure and function and could possibly be associated with multiple sclerosis, rheumatoid arthritis and other autoimmune diseases (Arshad, Bhatti, & John, ). There are many studies that have reported nsSNP effect of signalling protein and their role in diseases which include obesity (melanocortin‐4‐receptor), thyroid diseases (thyroid‐stimulating hormone receptor) and precocious puberty (lutropin–choriogonadotropic hormone receptor) (Stoy & Gurevich, ; Tao, ).…”