Domain Altering SNPs in the Human Proteome and Their Impact on Signaling Pathways

Liu, Yichuan; Tözeren, Aydın

doi:10.1371/journal.pone.0012890

Cited by 6 publications

(4 citation statements)

References 36 publications

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“…It is widely accepted that genetic changes such as somatic mutations are implicated in cancer development [40] . Also, some somatic mutations reveal the role of functional domains in hereditary disorders and complex diseases [41] . For example, tumors highly sensitive to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors often contain dominant mutations in exons that encode a portion of the tyrosine kinase (TK) domain of EGFR [42] .…”

Section: Resultsmentioning

confidence: 99%

CAERUS: Predicting CAncER oUtcomeS Using Relationship between Protein Structural Information, Protein Networks, Gene Expression Data, and Mutation Data

Zhang

Ouellette

2011

PLoS Comput Biol

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Carcinogenesis is a complex process with multiple genetic and environmental factors contributing to the development of one or more tumors. Understanding the underlying mechanism of this process and identifying related markers to assess the outcome of this process would lead to more directed treatment and thus significantly reduce the mortality rate of cancers. Recently, molecular diagnostics and prognostics based on the identification of patterns within gene expression profiles in the context of protein interaction networks were reported. However, the predictive performances of these approaches were limited. In this study we propose a novel integrated approach, named CAERUS, for the identification of gene signatures to predict cancer outcomes based on the domain interaction network in human proteome. We first developed a model to score each protein by quantifying the domain connections to its interacting partners and the somatic mutations present in the domain. We then defined proteins as gene signatures if their scores were above a preset threshold. Next, for each gene signature, we quantified the correlation of the expression levels between this gene signature and its neighboring proteins. The results of the quantification in each patient were then used to predict cancer outcome by a modified naïve Bayes classifier. In this study we achieved a favorable accuracy of 88.3%, sensitivity of 87.2%, and specificity of 88.9% on a set of well-documented gene expression profiles of 253 consecutive breast cancer patients with different outcomes. We also compiled a list of cancer-associated gene signatures and domains, which provided testable hypotheses for further experimental investigation. Our approach proved successful on different independent breast cancer data sets as well as an ovarian cancer data set. This study constitutes the first predictive method to classify cancer outcomes based on the relationship between the domain organization and protein network.

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Section: Resultsmentioning

confidence: 99%

CAERUS: Predicting CAncER oUtcomeS Using Relationship between Protein Structural Information, Protein Networks, Gene Expression Data, and Mutation Data

Zhang

Ouellette

2011

PLoS Comput Biol

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“…Genetic variability in the form of single nucleotide polymorphisms (SNPs), ASP, and allelic variants can have different effects at the proteome level (Figure ). For example, a SNP may occur within the coding region of a gene leading to a change in amino acid sequence of the corresponding protein (a so-called nonsynonymous SNP or nsSNP), which can be the mechanism of causing disease. , However, a SNP may also result in a different codon for the same amino acid, which does not change the amino acid sequence of the resulting protein, but may alter mRNA splicing, stability, and structure as well as protein folding, , all of which may be linked to disease . In addition allelic variation with different sequences in the coding regions will result in different protein variants, which may be subject to different transcriptional, translational, and post-translational regulatory mechanisms resulting in different activities. − …”

Section: Effect Of Genetic Variability On Protein Expressionmentioning

confidence: 99%

Proteomic Studies Related to Genetic Determinants of Variability in Protein Concentrations

2013

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Genetic variation has multiple effects on the proteome. It may influence the expression level of proteins, modify their sequences through single nucleotide polymorphisms, the occurrence of allelic variants, or alternative splicing (ASP) events. This perspective paper summarizes the major effects of genetic variability on protein expression and isoforms and provides an overview of proteomics techniques and methods that allow studying the effects of genetic variability at different levels of the proteome. The paper provides an overview of recent quantitative trait loci studies performed to explore the effect of genetic variation on protein expression (pQTL). Finally it gives a perspective view on advances in proteomics technology and the role of the Chromosome-Centric Human Proteome Project (C-HPP) by creating large-scale resources that may facilitate performing more comprehensive pQTL experiments in the future.

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“…They are responsible for a particular function or interaction which contributes to the overall role of a protein 24 . Protein domains can be highly altered by the presence of SNPs and proteins with these domain-altering SNPs contain highly connected nodes in various cellular pathways 60 . So, we intended to find out the nsSNPs that occur on the domains of the proteins encoded by the selected genes.…”

Section: Methodsmentioning

confidence: 99%

Pathogenic genetic variants from highly connected cancer susceptibility genes confer the loss of structural stability

Reza

Ferdous

Emon

et al. 2021

Sci Rep

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Genetic polymorphisms in DNA damage repair and tumor suppressor genes have been associated with increasing the risk of several types of cancer. Analyses of putative functional single nucleotide polymorphisms (SNP) in such genes can greatly improve human health by guiding choice of therapeutics. In this study, we selected nine genes responsible for various cancer types for gene enrichment analysis and found that BRCA1, ATM, and TP53 were more enriched in connectivity. Therefore, we used different computational algorithms to classify the nonsynonymous SNPs which are deleterious to the structure and/or function of these three proteins. The present study showed that the major pathogenic variants (V1687G and V1736G of BRCA1, I2865T and V2906A of ATM, V216G and L194H of TP53) might have a greater impact on the destabilization of the proteins. To stabilize the high-risk SNPs, we performed mutation site-specific molecular docking analysis and validated using molecular dynamics (MD) simulation and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) studies. Additionally, SNPs of untranslated regions of these genes affecting miRNA binding were characterized. Hence, this study will assist in developing precision medicines for cancer types related to these polymorphisms.

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Domain Altering SNPs in the Human Proteome and Their Impact on Signaling Pathways

Cited by 6 publications

References 36 publications

CAERUS: Predicting CAncER oUtcomeS Using Relationship between Protein Structural Information, Protein Networks, Gene Expression Data, and Mutation Data

CAERUS: Predicting CAncER oUtcomeS Using Relationship between Protein Structural Information, Protein Networks, Gene Expression Data, and Mutation Data

Proteomic Studies Related to Genetic Determinants of Variability in Protein Concentrations

Pathogenic genetic variants from highly connected cancer susceptibility genes confer the loss of structural stability

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