Identification and structural characterization of deleterious non-synonymous single nucleotide polymorphisms in the human SKP2 gene. (April 2019)

• Record Type: Journal Article
• Authors: Hosen, S.M. Zahid; Dash, Raju; Junaid, Md.; Mitra, Sarmistha; Absar, Nurul
• Is Part Of: Computational biology and chemistry. Volume 79(2019)
• Page Start: 127
• Page End: 136
• Publication Date: 2019-04
• Subjects: nsSNP -- SKP2 -- Cancer -- Molecular dynamics simulations -- SIFT -- PolyPhen-2; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2019.02.003
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Identification of deleterious nsSNPs in SKP2 gene has been done by publicly available bioinformatics tools. Among the 172 nsSNPs, 3 nsSNPs were found to be decreased the stability of SKP2 protein. P101L (rs761253702) and Y346C (rs755010517) were identified as the highly conserved and functional disrupting mutations. Molecular modelling and molecular dynamics simulation have been performed for the detailed characterization of mutants. Both these mutations increased the flexibility and changed the structural dynamics. Abstract: In SCF (Skp, Cullin, F-box) ubiquitin-protein ligase complexes, S-phase kinase 2 (SKP2) is one of the major players of F-box family, that is responsible for the degradation of several important cell regulators and tumor suppressor proteins. Despite of having significant evidence for the role of SKP2 on tumorgenesis, there is a lack of available data regarding the effect of non-synonymous polymorphisms. In this communication, the structural and functional consequences of non-synonymous single nucleotide polymorphisms (nsSNPs) of SKP2 have been reported by employing various computational approaches and molecular dynamics simulation. Initially, several computational tools like SIFT, PolyPhen-2, PredictSNP, I-Mutant 2.0 and ConSurf have been implicated in this study to explore the damaging SNPs. In total of 172 nsSNPs, 5 nsSNPs were identified as deleterious and 3 of them were predicted to be decreased the stability of protein. Guided from ConSurf analysis, P101L (rs761253702) and Y346C (rs755010517) were categorized as the highly conserved and functional disrupting mutations. Therefore, these mutations were subjected to three dimensional model building and molecular dynamics simulation study for the detailed structural consequences upon the mutations. The study revealed that P101L and Y346C mutations increased the flexibility and changed the structural dynamics. As both these mutations are located in the most functional regions of SKP2 protein, these computational insights might be helpful to consider these nsSNPs for wet-lab confirmatory analysis as well as in rationalizing future population based studies and structure based drug design against SKP2.