Roadmap to determine the point mutations involved in cardiomyopathy disorder: A Bayesian approach

Kumar, Ambuj; Rajendran, Vidya; Sethumadhavan, Rao; Purohit, Rituraj

doi:10.1016/j.gene.2013.01.056

Cited by 16 publications

(11 citation statements)

References 42 publications

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“…These limitations in predicting the genotype-phenotype correlation with disease-association makes it costly and highly challenging [29] . To overcome above restrictions, recently bioinformatics tools have emerged as a valuable option for mutation analysis [18] , [20] , [25] , [26] , [27] , [28] , [45] , [47] . Aberrant activation of AKT-1 has been implicated in various human cancers and is also associated with drug resistance [2] , [55] , [63] .…”

Section: Discussionmentioning

confidence: 99%

“…For this purpose, various bioinformatics tools, designed on the basis of recent findings in protein structure research and evolutionary biology, may prove useful in predicting the functional importance of nsSNPs [6] , [13] , [24] , [37] (Conde et al, 2006). Over past few years, several in silico studies have attempted to screen missense/ nsSNPs within the protein coding region of a gene and have shown these bioinformatics tools to be efficient and effective platform to prioritize SNPs for their association in disease pathology [18] , [20] , [25] , [26] , [27] , [28] , [31] .Over past few years, several in silico studies have attempted to screen missense/non-synonymous single nucleotide polymorphisms (nsSNPs) within the protein coding region of a gene and have shown these bioinformatics tools to be efficient and effective platform to prioritize SNPs for their association in disease pathology [18] , [20] , [25] , [26] , [27] , [28] , [45] . These nsSNPs within the coding region alter the encoded amino acid and further resulting in altered physiochemical properties of native protein [43] , [44] , [61] , [67] .…”

Section: Introductionmentioning

confidence: 99%

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Prediction of a highly deleterious mutation E17K in AKT-1 gene: An in silico approach

Khan

Ansari

2017

Biochemistry and Biophysics Reports

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The AKT1 (v-akt murine thymoma viral oncogene homologue 1) kinase is a member of most frequently activated proliferation and survival signaling pathway in cancer. Recently, hyperactivation of AKT1, due to functional point mutation in the pleckstrin homology (PH) domain of AKT1 gene, has been found to be associated with human colorectal, breast and ovarian cancer. Thus, considering its crucial role in cellular signaling pathway, a functional analysis of missense mutations of AKT1 gene was undertaken in this study. Twenty nine nsSNPs (non-synonymous single nucleotide polymorphism) within coding region of AKT1 gene were selected for our investigation and six SNPs were found to be deleterious by combinatorial predictions of various computational tools. RMSD values were calculated for the mutant models which predicted four substitutions (E17K, E319G, D32E and A255T) to be highly deleterious. The insight of the structural attribute was gained through analysis of, secondary structures, solvent accessibility and intermolecular hydrogen bond analysis which confirmed one missense mutation (E17K) to be highly deleterious nsSNPs. In conclusion, the investigated gene AKT1 has twenty nine SNPs in the coding region and through progressive analysis using different bioinformatics tools one highly deleterious SNP with rs121434592 was profiled. Thus, results of this study can pave a new platform to sort nsSNPs for several important regulatory genes that can be undertaken for the confirmation of their phenotype and their correlation with diseased status in case control studies.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of a highly deleterious mutation E17K in AKT-1 gene: An in silico approach

Khan

Ansari

2017

Biochemistry and Biophysics Reports

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“…Three basic prediction tools utilized in this study produced consistent and reliable results, suggesting that they are useful in identifying disease associated mutations. More advanced approaches such as molecular dynamics simulation have emerged as powerful methods with high accuracy and have already shown promising results in mutation analysis of cancer, neurodegenerative disorder, cardiomyopathy disease, and so on [ 19 – 21 ]. Further investigation of identified SMAD3 mutations by these advanced tools is warranted to shed light on their pathogenic mechanisms.…”

Section: Discussionmentioning

confidence: 99%

A Novel Mutation ofSMAD3Identified in a Chinese Family with Aneurysms-Osteoarthritis Syndrome

Zhang

Zhou

Liu

et al. 2015

BioMed Research International

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Aneurysms-osteoarthritis syndrome (AOS) is a recently delineated autosomal dominant disorder characterized by aneurysms, dissections, and tortuosity throughout the arterial tree in association with early onset osteoarthritis, mild craniofacial features, and skeletal and cutaneous anomalies. Previous studies have demonstrated that mutations in SMAD3, a key regulator of TGF-β signal transduction, contribute to AOS. Here, we investigated a family of three generations affected by AOS. A novel SMAD3 mutation, c.266G>A (p.C89Y), was identified and cosegregated with the affected individuals in this family. Our finding expands the mutation spectrum of SMAD3 gene and further strengthens the connection between the presence of aneurysms-osteoarthritis phenotype and SMAD3 mutations, which facilitates the understanding of the genotype-phenotype correlation of AOS.

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“…Together with MYBPC3 (the gene encoding myosin binding protein C), mutations in MYH7 are the major cause of HCM as well as being a cause of DCM and left ventricular non-compaction (LVNC) (Haas et al, 2014). In contrast to MYBPC3, where most pathogenic variants cause mRNA and protein truncation, the large majority of MYH7 variants are missense (Carrier et al, 1997;Richard et al, 2003) which often makes prediction of pathogenicity problematic (Walsh et al, 2010;Kumar et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

The structural effects of mutations can aid in differential phenotype prediction of beta-myosin heavy chain (Myosin-7) missense variants

Al‐Numair

Lopes²,

Syrris³

et al. 2016

Bioinformatics

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Motivation: High-throughput sequencing platforms are increasingly used to screen patients with genetic disease for pathogenic mutations, but prediction of the effects of mutations remains challenging. Previously we developed SAAPdap (Single Amino Acid Polymorphism Data Analysis Pipeline) and SAAPpred (Single Amino Acid Polymorphism Predictor) that use a combination of rule-based structural measures to predict whether a missense genetic variant is pathogenic. Here we investigate whether the same methodology can be used to develop a differential phenotype predictor, which, once a mutation has been predicted as pathogenic, is able to distinguish between phenotypes -in this case the two major clinical phenotypes (hypertrophic cardiomyopathy, HCM, and dilated cardiomyopathy, DCM) associated with mutations in the beta-myosin heavy chain (MYH7) gene product (Myosin-7). Results: A random forest predictor trained on rule-based structural analyses together with structural clustering data gave a Matthews' correlation coefficient (MCC) of 0.53 (accuracy, 75%). A post hoc removal of machine learning models that performed particularly badly, increased the performance (MCC=0.61, Acc=79%). This proof of concept suggests that methods used for pathogenicity prediction can be extended for use in differential phenotype prediction.

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Roadmap to determine the point mutations involved in cardiomyopathy disorder: A Bayesian approach

Cited by 16 publications

References 42 publications

Prediction of a highly deleterious mutation E17K in AKT-1 gene: An in silico approach

Prediction of a highly deleterious mutation E17K in AKT-1 gene: An in silico approach

A Novel Mutation ofSMAD3Identified in a Chinese Family with Aneurysms-Osteoarthritis Syndrome

The structural effects of mutations can aid in differential phenotype prediction of beta-myosin heavy chain (Myosin-7) missense variants

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