Shreya Bhattacharya, Pragati Prasad Sah, Arundhati Banerjee and Sujay Ray* Pages 860 - 873 ( 14 )
Background: Integrin αV, encoded by ITGAV gene, is one of the most studied protein subunits, closely associated with liver, pancreatic and stomach cancer progression and metastasis via regulation of angiogenesis. The occurrence of Single Nucleotide Polymorphisms (SNPs) in cancer- associated proteins is a key determinant for varied susceptibility of an individual towards cancer.
Methodology: The study investigated the deleterious effects of these cancer-associated SNPs on the protein’s structure, stability and cancer causing potential using an in silico approach. Numerous computational tools were employed that identified the most deleterious cancer-associated SNPs and those to get actively involved in post-translational modifications. The impact of these SNPs on the protein structure, function and stability was also examined.
Conclusion and Future Scope: A total 63 non-synonymous SNPs in ITGAV gene were observed to be associated in these three gastrointestinal cancers and among this, 63, 19 were the most deleterious ones. The structural and functional importance of residues altered by most damaging SNPs was analyzed through evolutionary conservation and solvent accessibility. The study also elucidated three-dimensional structures of the 19 most damaging mutants. The analysis of conformational variation identified 5 SNPs (D379Y, G188E, G513V, L950P, and R540L) in integrin αV, which influence the protein’s structure. Three calcium binding sites were predicted at residues: D379, G384 and G408 and a peptide binding site at residue: R369 in integrin αV. Therefore, SNPs D379Y, G384C, G408R and R369W have the potential to alter the binding properties of the protein. Screening and characterization of deleterious SNPs could advance novel biomarker discovery and therapeutic development in the future.
Integrin αV, deleterious single nucleotide polymorphisms, post translational modifications, evolutionary conservation, three-dimensional modelling, ligand binding site prediction.
Amity Institute of Biotechnology, Amity University, Kolkata, Amity Institute of Biotechnology, Amity University, Kolkata, Department of Biochemistry and Biophysics, University of Kalyani, Kalyani, Nadia, Amity Institute of Biotechnology, Amity University, Kolkata