Abstract
Schizophrenia (SZ) is a severe manifesting psychiatric neural disorder with abnormal behavior, disorganized speech and figment of the imagination. The Synapsin II (SYN2) and Trace Amine Associated Receptor (TAAR6) genes has direct association with SZ. In the current study, the 3-dimensional structure of SYN2 and TAAR6 protein is proposed and the protein-protein docking analysis was applied to explore the binding interactions of the candidate proteins. The comparative modeling was performed with the suitable template (Q86VA8 for SYN2 and H0YF79 for TAAR6) which represents the query coverage (71%, 87%), sequence identity (67%, 34%) and the e-value (0.0, 1e-43) respectively. The structure quality of the predicted model of SYN2 and TAAR6 presents 90.7%, and 96.5% residues in the favored region of Ramachandran plot analysis respectively, suggests the good quality models construction. The phylogenetic analysis suggests that the TAAR6 sequence is conserved in chimpanzee and gorilla (>80% homology) whereas the SYN2 is closely related with macaque. The protein docking analysis of SYN2 shows five ionic interactions with Lys-256, Lys-539, Arg-475, Gln-536 and Gln-529 with His-121, Glu-467, Glu-472, Arg-458 and Asp-477 of CAPON. The TAAR6 have two interactions of Glu-33 and Gly-171 with Arg-85 and Lys-52 of the PPP3CC. Current computational study may play a significant role to recruit, analyze and cure the mysteries of schizophrenia neurodegenerative disorder.
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Acknowledgements
We thank Indiana State University for providing support to complete the manuscript. This work was supported by the Indiana State University start-up funds to YB. The work was also supported by the NIH Grant 5P30GM114737, the NIH Grant P20GM103466, and the NIH Grant U54 MD007584.
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Khattak, N.A., Sehgal, S.A., Bai, Y., Deng, Y. (2017). Structure Modeling and Molecular Docking Studies of Schizophrenia Candidate Genes, Synapsins 2 (SYN2) and Trace Amino Acid Receptor (TAAR6). In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_26
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