标题：Computational Modeling of complete HOXB13 protein for predicting the functional effect of SNPs and the associated role in hereditary prostate cancer
摘要：The human HOXB13 gene encodes a 284 amino acid transcription factor belonging to the homeobox gene family containing a homeobox and a HoxA13 N-terminal domain. It is highly linked to hereditary prostate cancer, the majority of which is manifested as a result of a Single Nucleotide Polymorphism (SNP). In silico analysis of 95 missense SNP's corresponding to the non-homeobox region of HOXB13 predicted 21 nsSNP's to be potentially deleterious. Among 123 UTR SNPs analysed by UTRScan, rs543028086, rs550968159, rs563065128 were found to affect the UNR_BS, GY-BOX and MBE UTR signals, respectively. Subsequent analysis by PolymiRTS revealed 23 UTR SNPs altering the miRNA binding site. The complete HOXB13_M26 protein structure was modelled using MODELLER v9.17. Computational analysis of the 21 nsSNP's mapped into the HOXB13_M26 protein revealed seven nsSNP's (rs761914407, rs8556, rs138213197, rs772962401, rs778843798, rs770620686 and rs587780165) seriously resulting in a damaging and deleterious effect on the protein. G84E, G135E, and A128V resulted in increased, while, R215C, C66R, Y80C and S122R resulted in decreased protein stability, ultimately predicted to result in the altered binding patterns of HOXB13. While the genotype-phenotype based effects of nsSNP's were assessed, the exact biological and biochemical mechanism driven by the above predicted SNPs still needs to be extensively evaluated by in vivo and GWAS studies.
