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Bioinformatics and Biology Insights, volume 16, pages 117793222211043

Exploring the Structural and Functional Effects of Nonsynonymous SNPs in the Human Serotonin Transporter Gene Through In Silico Approaches

Md Arzo Mia 1
Md Nasir Uddin 1
Yasmin Akter 1
Jesmin 2
Lolo Wal Marzan 1
1
 
Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chittagong, Bangladesh
2
 
Department of Genetic Engineering and Biotechnology, University of Dhaka, Dhaka, Bangladesh
Publication typeJournal Article
Publication date2022-01-01
Quartile SCImago
Q1
Quartile WOS
Impact factor5.7
ISSN11779322
Biochemistry
Computer Science Applications
Molecular Biology
Computational Mathematics
Applied Mathematics
Abstract

The sodium-dependent serotonin transporter SLC6A4 (solute carrier family 6 member 4) gene encodes an intrinsic membrane protein that transmits the serotonin neurotransmitter from synaptic clefts into presynaptic neurons. The product of the SLC6A4 gene is related to the regulation of mood and social behavior, sleep, appetite, memory, digestion, and sexual desire. This protein is a target for antidepressant and psychostimulant drugs, thus prolonged neurotransmitter signaling remains blocked. In this study, the functional consequences of nsSNPs in the human SLC6A4 gene were explored through computational tools: PhD-SNP, SIFT, Align GVGD, PROVEAN, PMut, nsSNP Analyzer, SNPs&GO, SNAP2, PolyPhen2, and PANTHER to identify the most deleterious and damaging nsSNPs. Then the mutant protein stabilities were assessed using I-Mutant, MUpro, and MutPred2; amino acid conservation using ConSurf, and posttranslational modification analysis using MusiteDEEP and PROSPER. Furthermore, the 3-dimensional (3D) model of the mutated proteins was predicted and validated using SPARKS-X, Verify3D, and PROCHECK. The protein–ligand binding sites were analyzed using the COACH meta-server. Results from this study predicted that T192M, G342E, R607C, W282S, R104C, P131L, P156L, and N351S were the most structurally and functionally significant nsSNPs in the human SLC6A4 gene. Arg607 and Pro156 were the predicted sites for posttranslational modifications, and Thr192 and Try282 were the ligand-binding sites in the human SLC6A4 gene. The analyzed data also suggested that R104C, P131L, P156L, T192M, G342E, and W282S mutants might affect the binding of sodium ions with this protein. Taken together, this study provided important information on structurally and functionally important nsSNPs of the human SLC6A4 gene for further experimental validation. In the future, these damaging nsSNPs of the SLC6A4 gene have the potential to be evaluated as prognostic biomarkers for SLC6A4-related disorder diagnosis and research.

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Mia M. A. et al. Exploring the Structural and Functional Effects of Nonsynonymous SNPs in the Human Serotonin Transporter Gene Through In Silico Approaches // Bioinformatics and Biology Insights. 2022. Vol. 16. p. 117793222211043.
GOST all authors (up to 50) Copy
Mia M. A., Nasir Uddin M., Akter Y., Jesmin, Marzan L. W. Exploring the Structural and Functional Effects of Nonsynonymous SNPs in the Human Serotonin Transporter Gene Through In Silico Approaches // Bioinformatics and Biology Insights. 2022. Vol. 16. p. 117793222211043.
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RIS Copy
TY - JOUR
DO - 10.1177/11779322221104308
UR - https://doi.org/10.1177/11779322221104308
TI - Exploring the Structural and Functional Effects of Nonsynonymous SNPs in the Human Serotonin Transporter Gene Through In Silico Approaches
T2 - Bioinformatics and Biology Insights
AU - Mia, Md Arzo
AU - Akter, Yasmin
AU - Nasir Uddin, Md
AU - Jesmin
AU - Marzan, Lolo Wal
PY - 2022
DA - 2022/01/01
PB - SAGE
SP - 117793222211043
VL - 16
SN - 1177-9322
ER -
BibTex
Cite this
BibTex Copy
@article{2022_Mia,
author = {Md Arzo Mia and Yasmin Akter and Md Nasir Uddin and Jesmin and Lolo Wal Marzan},
title = {Exploring the Structural and Functional Effects of Nonsynonymous SNPs in the Human Serotonin Transporter Gene Through In Silico Approaches},
journal = {Bioinformatics and Biology Insights},
year = {2022},
volume = {16},
publisher = {SAGE},
month = {jan},
url = {https://doi.org/10.1177/11779322221104308},
pages = {117793222211043},
doi = {10.1177/11779322221104308}
}
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