In‑silico analysis predicting effects of some Protein conding SNPs of human tumor suppressor protein53 (TP53 gene) by using different Bioinformatic tools (original) (raw)
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Cell Death & Differentiation
Infrequent and rare genetic variants in the human population vastly outnumber common ones. Although they may contribute significantly to the genetic basis of a disease, these seldom-encountered variants may also be miss-identified as pathogenic if no correct references are available. Somatic and germline TP53 variants are associated with multiple neoplastic diseases, and thus have come to serve as a paradigm for genetic analyses in this setting. We searched 14 independent, globally distributed datasets and recovered TP53 SNPs from 202,767 cancer-free individuals. In our analyses, 19 new missense TP53 SNPs, including five novel variants specific to the Asian population, were recurrently identified in multiple datasets. Using a combination of in silico, functional, structural, and genetic approaches, we showed that none of these variants displayed loss of function compared to the normal TP53 gene. In addition, classification using ACMG criteria suggested that they are all benign. Cons...
Analysis of TP53 Gene Using Bioinformatics Tools Analysis of TP53 gene
Background: The human TP53 gene, also known as p53, encodes for the tumor protein 53 (p53), regulates the cell cycle and hence functions as a tumor suppressor. This study aimed to investigate some properties of the TP53 gene and its products, such as the homologous protein sequences in different species, the common transcription factor binding sites on their promoters, their phylogenetic relationship, conserved domains, and their expression profiles by in silico biology approach. Methods: We investigated the homology, conserved domain, promoter and expression profiles of the TP53 gene in various species using bioinformatics approaches. Results: Our results revealed that which investigated p53 molecules among all organisms are conserved. They have three conserved domains (p53_TAD, p53 DNA_binding, and p53 tetramerization motif), some of which have full and truncated sub-domains. Human p53 proteins is similar to those of Pan troglodytes, Macaca mulatta, Macaca fascicularis and Chloroc...
Prediction of Deleterious Single Nucleotide Polymorphisms in Human p53 Gene
Prediction of Deleterious Single Nucleotide Polymorphisms in Human p53 Gene, 2018
With a variety of accessible Single Nucleotide Polymorphisms (SNPs) data on human p53 gene, this investigation is intended to deal with detrimental SNPs in p53 gene by executing diverse valid computational tools, including Filter, SIFT, PredictSNP, Fathmm, UTRScan, ConSurf, Phyre, Tm-Adjust, I-Mutant, Task Seek after practical and basic appraisal, dissolvable openness, atomic progression, and analysing the energy minimization. Of 581 p53 SNPs, 420 SNPs are found to be missense or non-synonymous and 435 SNPs are in the 3 prime UTR and 112 SNPs are of every 5 prime UTR from which 16 non synonymous SNPs (nsSNPs) as non-tolerable while PredictSNP package predicted 14 (taking consideration SNP colored green by two or more than 2 analyses is neutral). By concentrating on six bioinformatics tools of various dimensions a combined output is generated where 14 nsSNPs are prone to exert a deleterious effect. By using diverse SNP analysing tools we have found 5 missense SNPs in the 3 crucial amino acids position in the DNA binding domain. The underlying discoveries are fortified by I-Mutant and Project HOPE. The ExPASy-PROSITE tools characterized whether the mutations located in the functional part of the protein or not. This study provides a decisive outcome concluding the accessible SNPs information by recognizing the five harming nsSNPs: rs28934573 (S241F), rs11540652 (R248Q), rs121913342 (R248W), rs121913343 (R273C) and rs28934576 (R273H). The findings of this investigation recognize the detrimental nsSNPs which enhance the danger of various kinds of oncogenesis in patients of different populations' in genome-wide studies (GWS).
Bulletin of the National Research Centre, 2022
Background: A variety of accessible data, including those of single-nucleotide polymorphisms (SNPs) on the human p53 gene, are made widely available on a global scale. Owing to this, our investigation aimed to deal with the detrimental SNPs in the p53 gene by executing various valid computational tools, including-Filter, SIFT, PredictSNP, Fathmm, UTRScan, ConSurf, SWISS-MODEL, Amber 16 package, Tm-Adjust, I-Mutant, Task Seek, GEPIA2 after practical and basic appraisal, dissolvable openness, atomic progression, analyzing the energy minimization and assessing the gene expression pattern. Results: Out of the total 581 p53 SNPs, 420 SNPs were found to be missense or non-synonymous, 435 SNPs were in the three prime UTR, and 112 SNPs were in the five prime UTR from which 16 non-synonymous SNPs (nsSNPs) were predicted to be non-tolerable while PredictSNP package predicted 14. Concentrating on six bioinformatics tools of various dimensions, a combined output was generated, where 14 nsSNPs could exert a deleterious effect. We found 5 missense SNPs in the DNA binding domain's three crucial amino acid positions, using diverse SNP analyzing tools. The underlying discoveries were fortified by microsecond molecular dynamics (MD) simulations, TM-align, I-Mutant, and Project HOPE. The ExPASy-PROSITE tools characterized whether the mutations were located in the functional part of the protein or not. This study provides a decisive outcome, concluding the accessible SNPs' information by recognizing the five unfavorable nsSNPs-rs28934573 (S241F), rs11540652 (R248Q), rs121913342 (R248W), rs121913343 (R273C), and rs28934576 (R273H). By utilizing Heatmapper and GEPIA2, several visualization plots, including heat maps, box plots, and survival plots, were produced. Conclusions: These plots disclosed differential expression patterns of the p53 gene in humans. The investigation focused on recognizing the detrimental nsSNPs, which augmented the danger posed by various oncogenesis in patients of different populations, including within the genome-wide studies (GWS).
BMC Bioinformatics, 2007
The p53 gene family consists of the three genes p53, p63 and p73, which have polyhedral non-overlapping functions in pivotal cellular processes such as DNA synthesis and repair, growth arrest, apoptosis, genome stability, angiogenesis, development and differentiation. These genes encode sequence-specific nuclear transcription factors that recognise the same responsive element (RE) in their target genes. Their inactivation or aberrant expression may determine tumour progression or developmental disease. The discovery of several protein isoforms with antagonistic roles, which are produced by the expression of different promoters and alternative splicing, widened the complexity of the scenario of the transcriptional network of the p53 family members. Therefore, the identification of the genes transactivated by p53 family members is crucial to understand the specific role for each gene in cell cycle regulation. We have combined a genome-wide computational search of p53 family REs and microarray analysis to identify new direct target genes. The huge amount of biological data produced has generated a critical need for bioinformatic tools able to manage and integrate such data and facilitate their retrieval and analysis.
The IARC TP53 database: New online mutation analysis and recommendations to users
Human Mutation, 2002
Mutations in the tumor suppressor gene TP53 are frequent in most human cancers. Comparison of the mutation patterns in different cancers may reveal clues on the natural history of the disease. Over the past 10 years, several databases of TP53 mutations have been developed. The most extensive of these databases is maintained and developed at the International Agency for Research on Cancer. The database compiles all mutations (somatic and inherited), as well as polymorphisms, that have been reported in the published literature since 1989. The IARC TP53 mutation dataset is the largest dataset available on the variations of any human gene. The database is available at www.iarc.fr/P53/. In this paper, we describe recent developments of the database. These developments include restructuring of the database, which is now patient-centered, with more detailed annotations on the patient (carcinogen exposure, virus infection, genetic background). In addition, a new on-line application to retrieve somatic mutation data and analyze mutation patterns is now available. We also discuss limitations on the use of the database and provide recommendations to users. Hum Mutat 19:607614, 2002.
Sequence analysis of the tumour suppressor protein p53 and its implications
2012
The p53 is a transcription factor encoded by the tumour suppressor gene TP53, involved in regulating the cell responses to DNA damage to conserve genomic stability. p53 protein sequence analysis was carried out to unravel the structural details of normal and mutant forms. The tumour suppressor protein p53 is mutated in more than 50% of invasive cancers and 30% of these mutations are found in six major hot spot codons located in its DNA binding core domain. This study was conducted to gain insights into normal and mutant variants of p53 and to understand their clinical implications in the etiology of cancer. pBLAST analysis was performed between normal and two mutant p53 protein sequences and various types of mutations like substitutions of amino acids were identified. Importance of insilico study on p53 as a tool for prediction and diagnosis of mutations in human cancers and its medical significance are discussed.
Human Mutation, 2005
TP53 alteration is the most frequent genetic alteration found in human cancers. To date, more than 15,000 tumors with TP53 mutations have been published, leading to the description of more than 1,500 different TP53 mutants (http://p53.curie.fr). The frequency of these mutants is highly heterogeneous, with 11 hotspot mutants found more than 100 times, whereas 306 mutants have been reported only once. So far, little is known concerning the biological significance of these rare mutants, as the majority of biological studies have focused on classic hotspot mutants. In order to gain a deeper knowledge about the significance of all of these mutants, we have cross-checked each mutant of the TP53 mutation database for its activity, derived from a library of 2,314 TP53 mutants representing all possible amino acid substitutions caused by a point mutation. The transactivation activity of all of these mutant was analyzed with respect to eight transcription promoters [Kato S, et al., Proc Natl Acad Sci USA (2003)100:8424-8429]. Although the most frequent TP53 mutants sustain a clear loss of transactivation activity, more than 50% of the rare TP53 mutants display significant activity. Analysis in specific types of cancer or in normal skin patches demonstrates a similar distribution of TP53 loss of activity, with the exception of melanoma, in which the majority of TP53 mutants display significant activity. Our data indicate that TP53 mutants represent a highly heterogeneous population with a large diversity in terms of loss of transactivation activity that could account for the heterogeneous tumor phenotypes and the difficulty of clinical studies. Hum Mutat 25:6-17, 2005. r 2004 Wiley-Liss, Inc.
The UMD-p53 database: New mutations and analysis tools
Human Mutation, 2003
The tumor suppressor gene TP53 (p53) is the most extensively studied gene involved in human cancers. More than 1,400 publications have reported mutations of this gene in 150 cancer types for a total of 14,971 mutations. To exploit this huge bulk of data, specific analytic tools were highly warranted. We therefore developed a locus-specific database software called UMD-p53. This database compiles all somatic and germline mutations as well as polymorphisms of the TP53 gene which have been reported in the published literature since 1989, or unpublished data submitted to the database curators. The database is available at www.umd.necker.fr or at http://p53.curie.fr/. In this paper, we describe recent developments of the UMD-p53 database. These developments include new fields and routines. For example, the analysis of putative acceptor or donor splice sites is now automated and gives new insight for the causal role of ''silent mutations.'' Other routines have also been created such as the prescreening module, the UV module, and the cancer distribution module. These new improvements will help users not only for molecular epidemiology and pharmacogenetic studies but also for patient-based studies. To achieve theses purposes we have designed a procedure to check and validate data in order to reach the highest quality data. Hum Mutat 21:176-181,
A novel resource centre for TP53 mutations and mutants has been developed (http://p53.fr). TP53 gene dysfunction can be found in the majority of human cancer types. The potential use of TP53