scholarly journals Computational and Mass Spectrometry-Based Approach Identify Deleterious Non-Synonymous Single Nucleotide Polymorphisms (nsSNPs) in JMJD6

Molecules ◽  
2021 ◽  
Vol 26 (15) ◽  
pp. 4653
Author(s):  
Tianqi Gong ◽  
Lujie Yang ◽  
Fenglin Shen ◽  
Hao Chen ◽  
Ziyue Pan ◽  
...  

The jumonji domain-containing protein 6 (JMJD6) gene catalyzes the arginine demethylation and lysine hydroxylation of histone and a growing list of its known substrate molecules, including p53 and U2AF65, suggesting a possible role in mRNA splicing and transcription in cancer progression. Mass spectrometry-based technology offers the opportunity to detect SNP variants accurately and effectively. In our study, we conducted a combined computational and filtration workflow to predict the nonsynonymous single nucleotide polymorphisms (nsSNPs) present in JMJD6, followed by a liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis and validation. The computational approaches SIFT, PolyPhen-2, SNAP, I-Mutant 2.0, PhD-SNP, PANTHER, and SNPS&GO were integrated to screen out the predicted damaging/deleterious nsSNPs. Through the three-dimensional structure of JMJD6, H187R (rs1159480887) was selected as a candidate for validation. The validation experiments showed that the mutation of this nsSNP in JMJD6 obviously affected mRNA splicing or the transcription of downstream genes through the reduced lysyl-hydroxylase activity of its substrates, U2AF65 and p53, further indicating the accuracy of this prediction method. This research provides an effective computational workflow for researchers with an opportunity to select prominent deleterious nsSNPs and, thus, remains promising for examining the dysfunction of proteins.

2007 ◽  
Vol 05 (06) ◽  
pp. 1297-1318 ◽  
Author(s):  
CATHERINE L. WORTH ◽  
G. RICHARD J. BICKERTON ◽  
ADRIAN SCHREYER ◽  
JULIA R. FORMAN ◽  
TAMMY M. K. CHENG ◽  
...  

The prediction of the effects of nonsynonymous single nucleotide polymorphisms (nsSNPs) on function depends critically on exploiting all information available on the three-dimensional structures of proteins. We describe software and databases for the analysis of nsSNPs that allow a user to move from SNP to sequence to structure to function. In both structure prediction and the analysis of the effects of nsSNPs, we exploit information about protein evolution, in particular, that derived from investigations on the relation of sequence to structure gained from the study of amino acid substitutions in divergent evolution. The techniques developed in our laboratory have allowed fast and automated sequence-structure homology recognition to identify templates and to perform comparative modeling; as well as simple, robust, and generally applicable algorithms to assess the likely impact of amino acid substitutions on structure and interactions. We describe our strategy for approaching the relationship between SNPs and disease, and the results of benchmarking our approach — human proteins of known structure and recognized mutation.


Author(s):  
Shreya Bhattacharya ◽  
Pragati Prasad Sah ◽  
Arundhati Banerjee ◽  
Sujay Ray

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. 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. 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 5SNPs (D379Y, G188E, G513V, L950P, and R540L) in integrin αV, which influence 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 future.


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