In silico analysis and high-risk pathogenic phenotype predictions of non-synonymous single nucleotide polymorphisms in human Crystallin beta A4 gene associated with congenital cataract

Abstract

In order to provide a cost-effective method to narrow down the number of pathogenic Crystallin beta A4 (CRYBA4) non-synonymous single nucleotide polymorphisms (nsSNPs), we collected nsSNP information of the CRYBA4 gene from SNP databases and literature, predicting the pathogenicity and possible changes of protein properties and structures using multiple bioinformatics tools. The nsSNP data of the CRYBA4 gene were collected from 4 databases and published literature. According to 12 criteria, six bioinformatics tools were chosen to predict the pathogenicity. I-Mutant 2.0, Mupro and INPS online tools were used to analyze the effects of amino acid substitution on protein stability by calculating the value of ΔΔG. ConSurf, SOPMA, GETAREA and HOPE online tools were used to predict the evolutionary conservation of amino acids, solvent accessible surface areas, and the physical and chemical properties and changes of protein structure. All 157 CRYBA4 nsSNPs were analyzed. Forty-four CRYBA4 high-risk pathogenic nsSNPs (predicted to be pathogenic by all six software tools) were detected out of the 157 CRYBA4 nsSNPs, four of which (c.283C>T, p.R95W; c.449T>A, p.V150D; c.475G>A, p.G159R; c.575G>C, p.R192P) should be focused on because of their high potential pathogenicity and possibility of changing protein properties. Thirty high-risk nsSNPs were predicted to cause a decrease of protein stability. Twenty-nine high-risk nsSNPs occurred in evolutionary conserved positions. Twenty-two high-risk nsSNPs occurred in the core of the protein. It is predicted that these high-risk pathogenic nsSNPs can cause changes in the physical and chemical properties of amino acids, resulting in structural changes of proteins and changes in the interactions between domains and other molecules, thus affecting the function of proteins. This study provides important reference value when narrowing down the number of pathogenic CRYBA4 nsSNPs and studying the pathogenesis of congenital cataracts. By using this method, we can easily find 44 high-risk pathogenic nsSNPs out of 157 CRYBA4 nsSNPs.

ADGRL3 genomic variation implicated in neurogenesis and ADHD links functional effects to the incretin polypeptide GIP

“…Molecular protein stability assesses how changes in specific amino acids can affect the function and activity of biological molecules 41 , 96 . We used I-Mutant 2.0 ( https://folding.biofold.org/i-mutant/i-mutant2.0.html ) 92 and MUpro ( http://mupro.proteomics.ics.uci.edu/ ) 96 , 97 to evaluate free energy of protein unfolding in a mutated protein. This in-silico technique identifies changes in protein stability and predicts changes in stability by evaluating thermodynamic free energy changes (ΔΔG) and the direction of the change after a single point mutation of protein.…”

Genetic analysis of 39 erythrocytosis and hereditary hemochromatosis‐associated genes in the Slovenian family with idiopathic erythrocytosis

“…The pathogenicity of variants in coding regions was assessed using in silico prediction tools CADD score (https://cadd.gs.washington.edu/snv), 25 PolyPhen‐2 (http://genetics.bwh.harvard.edu/pph2/), 26 SIFT (https://sift.bii.a-star.edu.sg/), 27 MutPred2 (http://mutpred.mutdb.org/index.html), 28 SNPs&GO (https://snps.biofold.org/snps-and-go/snps-and-go.html), 29 PANTHER (http://www.pantherdb.org/tools/csnpScoreForm.jsp), 30 PhD‐SNP (https://snps.biofold.org/phd-snp/phd-snp.html), 31 PROVEAN (http://provean.jcvi.org/index.php) 32 and Mutation Taster 2 (http://www.mutationtaster.org/) 33 as described by Schiemann and Stowell, 2016, 34 Bris et al 2018, 35 Wang et al 2020 36 . The pathogenicity prediction of intron variants and impact on splicing features were analysed using tools CADD score, 25 the RegSNP‐intron (https://regsnps-intron.ccbb.iupui.edu/), Human Splicing Finder (http://www.umd.be/HSF/), 37 IntSplice (https://www.med.nagoya-u.ac.jp/neurogenetics/IntSplice/index.html) 38 as reviewed in Ohno et al 2018 39 and Lin et.…”

“…panth erdb.org/tools/ csnpS coreF orm.jsp),30 PhD-SNP (https://snps. biofo ld.org/phd-snp/phd-snp.html),31 PROVEAN (http://prove an.jcvi.org/index.php)32 and Mutation Taster 2 (http://www.mutat ionta ster.org/)33 as described by Schiemann and Stowell, 2016,34 Bris et al 2018,35 Wang et al 2020 36. The pathogenicity prediction of intron variants and impact on splicing features were analysed using tools CADD score,25 the RegSNP-intron (https://regsn psintron.ccbb.iupui.edu/), Human Splicing Finder (http://www.umd.be/HSF/), 37 IntSplice (https://www.med.nagoy a-u.ac.jp/neuro genet ics/IntSp lice/index.html) 38 as reviewed in Ohno et al 2018 39 and Lin et.…”

Analysis of mutations in the fusion protein and hemagglutinin-neuraminidase in twelve strains of NDV isolated in Madagascar by bioinformatics methods

“…MUpro predicted the change in stability of protein structures, by calculating the thermodynamic free energy value (ΔΔG) after a single mutation: the value of ΔΔG > 0 indicates an increase in stability while the value of ΔΔG < 0 indicates a decrease in structural stability [21]. In addition, the Mutpred2 server was used with a threshold value of 0.5 to predict the effects of mutation on global protein structures.…”