A novel computational and structural analysis of nsSNPs in CFTR gene

Doss, C. George Priya; Rajasekaran, R.; Sudandiradoss, C.; Ramanathan, K.; Purohit, Rituraj; Sethumadhavan, Rao

doi:10.1007/s11568-008-9019-8

Cited by 56 publications

(35 citation statements)

References 74 publications

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“…Several groups have predicted CF from CFTR missense mutation using computational approaches [Dorfman et al, 2010; Gaucher et al, 2006; George Priya Doss et al, 2008]. Priya-Doss et al (2008) examined publically available computational tools.…”

Section: Introductionmentioning

confidence: 99%

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Phenotype-optimized sequence ensembles substantially improve prediction of disease-causing mutation in cystic fibrosis

et al. 2012

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Cystic fibrosis transmembrane conductance regulator (CFTR) mutation is associated with a phenotypic spectrum that includes cystic fibrosis (CF). The disease liability of some common CFTR mutations is known, but rare mutations are seen in too few patients to categorize unequivocally, making genetic diagnosis difficult. Computational methods can predict the impact of mutation, but prediction specificity is often below that required for clinical utility. Here, we present a novel supervised learning approach for predicting CF from CFTR missense mutation. The algorithm begins by constructing custom multiple sequence alignments called phenotype-optimized sequence ensembles (POSEs). POSEs are constructed iteratively, by selecting sequences that optimize predictive performance on a training set of CFTR mutations of known clinical significance. Next, we predict CF disease liability from a different set of CFTR mutations (test-set mutations). This approach achieves improved prediction performance relative to popular methods recently assessed using the same test-set mutations. Of clinical significance, our method achieves 94% prediction specificity. Because databases such as HGMD and locus-specific mutation databases are growing rapidly, methods that automatically tailor their predictions for a specific phenotype may be of immediate utility. If the performance achieved here generalizes to other systems, the approach could be an excellent tool to help establish genetic diagnoses.

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Section: Introductionmentioning

confidence: 99%

“…Priya-Doss et al (2008) examined publically available computational tools. Their analysis combined sequence-based approaches, including SIFT and PolyPhen, and structure-based methods, including NOMAD, SRide, and FoldX.…”

Section: Introductionmentioning

confidence: 99%

Phenotype-optimized sequence ensembles substantially improve prediction of disease-causing mutation in cystic fibrosis

et al. 2012

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“…Even with The development these technologies is still challenging to differentiate neutral/beneficial amino acid substitution from the pool of single nucleotide polymorphism. 5 Thus, use of computer to determine harmful non-synonymous amino acid substitution of variant in the sequence exome is important. 6 A large number of computer determinations for amino acid substitutions depend on the basis that protein sequences notice in all living organisms have survived natural selection.…”

Section: Introductionmentioning

confidence: 99%

Computational Algorithm to Assess Genetic Relationship and Functional Analysis of Non-Synonymous Substitution of HSP 70 Gene of Cattle, Sheep and Goat

Ih¹

2017

JDVAR

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This study was carry out using heat shock protein 70 (HSP 70) sequences comprising goat (5), sheep (5) and cattle (5) which was obtained from the GenBank (NCBI). Protein Variant Effect Analyzer (PROVEAN) tool was used to check the nonsynonymous single nucleotide polymorphism of the HSP 70 gene of amino acid substitution for cattle, goat and sheep. The study observed that some amino acid substitution returned neutral/beneficial while some deleterious/harmful. Molecular evolutionary genetics analysis (MEGA) 7.0 was used to perform both Tajima's test and phylogenetic relationship for cattle, goat and sheep. The Tajima's test (D) for all the species revealed positive values, an indication of purifying selection. The phylogenetic relationship followed bovidae family speciation. The study concluded that information emanating from this research is significant for further dry and wet laboratory experiment.

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“…The combination of different algorithms will improve the accuracy of results or predicted effects of particular mutations. The computational tools successfully predicted the consequences of nsSNPs associated with genes such as CFTR (George Priya Doss et al, 2008), IGF1R (de Alencar and Lopes, 2010), BRAF (Hussain et al, 2012), ATM (George Priya Doss and Rajith, 2012), TYRP1 (Kamaraj and Purohit, 2013), and GalNAc-T1 (Mohamoud et al, 2014). Aided by the HapMap project, most studies have been conducted in humans to identify genomic variations (http://www.hapmap.org).…”

Section: Introductionmentioning

confidence: 99%

Identification and prediction of the consequences of nonsynonymous SNPs in glyceraldehyde 3-phosphate dehydrogenase (GAPDH) gene of zebrafish Danio rerio

Rasal¹,

Chakrapani²,

Patra³

et al. 2016

Turk J Biol

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The recent progress in DNA sequencing and computational algorithms dramatically heightened the value of single nucleotide polymorphism (SNP) databases. The experimental studies of mutation for the particular gene of interest are laborious and timeconsuming. In this study, we used several computational algorithms to investigate the structural and functional consequences of SNPs on the glyceraldehyde 3-phosphate dehydrogenase (GAPDH) gene of zebrafish (Danio rerio). The GAPDH gene is involved in multiple cellular activities, in addition to its association with classical glycolytic pathway. It has most commonly been used as the housekeeping gene for differential gene expression profiling, including western blots. The computational analysis, which used sequence-based tools such as SIFT, PANTHER, PROVEAN, and I-Mutant2.0, predicted deleterious nonsynonymous SNPs (nsSNPs) at p.W194R in the GAPDH gene. The SWISS Model tool generated a homology-modeled 3D structure of GAPDH by applying homologous protein structures retrieved from the RCSB PDB databank (PDB ID: 1U8F_O, 1ZNQ_O, 1J0X_O, 3GPD_R, and 1SZJ_G). The sequence identities of the above selected models were 86%, 86%, 86%, 83%, and 75%, respectively. The Procheck quality assessment in the Ramachandran plot showed that the incorporated mutation (p.W194R) led to a shifting of amino acid residues from the most favored regions towards the disallowed region, as compared to the native counterpart. The lowered ERRAT and PROSA Z scores in the mutant confirmed the overall deteriorating quality of mutant GAPDH. We also predicted an effect of mutation on increased H-bonding patterns due to mutant residue. The molecular docking analysis, while binding GAPDH to its ligand (NAD +), also revealed elevated global energy in the mutant. Altogether, this study demonstrated the impact of nsSNPs on the protein structure and function. The results of work provide an insight/ roadmap for future investigations with regard to the pathological consequences of nsSNPs using in vivo methods.

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A novel computational and structural analysis of nsSNPs in CFTR gene

Cited by 56 publications

References 74 publications

Phenotype-optimized sequence ensembles substantially improve prediction of disease-causing mutation in cystic fibrosis

Phenotype-optimized sequence ensembles substantially improve prediction of disease-causing mutation in cystic fibrosis

Computational Algorithm to Assess Genetic Relationship and Functional Analysis of Non-Synonymous Substitution of HSP 70 Gene of Cattle, Sheep and Goat

Identification and prediction of the consequences of nonsynonymous SNPs in glyceraldehyde 3-phosphate dehydrogenase (GAPDH) gene of zebrafish Danio rerio

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