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Computational screening and molecular dynamic simulation of breast cancer associated deleterious non-synonymous single nucleotide polymorphisms in TP53 gene.

Chitrala KN, Yeguvapalli S - PLoS ONE (2014)

Bottom Line: Among them, TP53 is one of the major genetic risk factor which is known to be mutated in many of the breast tumor types.We have predicted three deleterious coding non-synonymous single nucleotide polymorphisms rs11540654 (R110P), rs17849781 (P278A) and rs28934874 (P151T) in TP53 with a phenotype in breast tumors using computational tools SIFT, Polyphen-2 and MutDB.We have performed molecular dynamics simulations to study the structural and dynamic effects of these TP53 mutations in comparison to the wild-type protein.

View Article: PubMed Central - PubMed

Affiliation: Department of Zoology, Sri Venkateswara University, Tirupati, Andhra Pradesh, India.

ABSTRACT
Breast cancer is one of the most common cancers among the women around the world. Several genes are known to be responsible for conferring the susceptibility to breast cancer. Among them, TP53 is one of the major genetic risk factor which is known to be mutated in many of the breast tumor types. TP53 mutations in breast cancer are known to be related to a poor prognosis and chemo resistance. This renders them as a promising molecular target for the treatment of breast cancer. In this study, we present a computational based screening and molecular dynamic simulation of breast cancer associated deleterious non-synonymous single nucleotide polymorphisms in TP53. We have predicted three deleterious coding non-synonymous single nucleotide polymorphisms rs11540654 (R110P), rs17849781 (P278A) and rs28934874 (P151T) in TP53 with a phenotype in breast tumors using computational tools SIFT, Polyphen-2 and MutDB. We have performed molecular dynamics simulations to study the structural and dynamic effects of these TP53 mutations in comparison to the wild-type protein. Results from our simulations revealed a detailed consequence of the mutations on the p53 DNA-binding core domain that may provide insight for therapeutic approaches in breast cancer.

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Related in: MedlinePlus

Distribution of TP53 coding nonsynonymous SNPs (nSNPs), coding synonymous SNPs (sSNPs), 3′ UTR SNPs, 5′ UTR SNPs and intronic SNPs.
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pone-0104242-g002: Distribution of TP53 coding nonsynonymous SNPs (nSNPs), coding synonymous SNPs (sSNPs), 3′ UTR SNPs, 5′ UTR SNPs and intronic SNPs.

Mentions: dbSNP database contain a total of 14613 SNPs for TP53 gene, out of which 637 were found to be Human (active) SNPs (i.e., Active Human RS and not including those that have been merged). Among the 637 Human (active) SNPs, 100 were coding nSNPs, 31 were coding synonymous, 52 SNPs were in the mRNA 3′ UTR region, 38 were in the mRNA 5′ UTR region and 451 were in the intronic regions. It can be seen from the Fig. 2 that the vast majority of SNPs occur in the intronic region (70.8%) and more SNPs are nSNPs (15.6%) compared to synonymous SNPs (4.8%), SNPs occurring in the mRNA 3′ UTR (8.1%) and 5′ UTR (5.9%) regions. We selected coding nSNPs for our investigation.


Computational screening and molecular dynamic simulation of breast cancer associated deleterious non-synonymous single nucleotide polymorphisms in TP53 gene.

Chitrala KN, Yeguvapalli S - PLoS ONE (2014)

Distribution of TP53 coding nonsynonymous SNPs (nSNPs), coding synonymous SNPs (sSNPs), 3′ UTR SNPs, 5′ UTR SNPs and intronic SNPs.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4126775&req=5

pone-0104242-g002: Distribution of TP53 coding nonsynonymous SNPs (nSNPs), coding synonymous SNPs (sSNPs), 3′ UTR SNPs, 5′ UTR SNPs and intronic SNPs.
Mentions: dbSNP database contain a total of 14613 SNPs for TP53 gene, out of which 637 were found to be Human (active) SNPs (i.e., Active Human RS and not including those that have been merged). Among the 637 Human (active) SNPs, 100 were coding nSNPs, 31 were coding synonymous, 52 SNPs were in the mRNA 3′ UTR region, 38 were in the mRNA 5′ UTR region and 451 were in the intronic regions. It can be seen from the Fig. 2 that the vast majority of SNPs occur in the intronic region (70.8%) and more SNPs are nSNPs (15.6%) compared to synonymous SNPs (4.8%), SNPs occurring in the mRNA 3′ UTR (8.1%) and 5′ UTR (5.9%) regions. We selected coding nSNPs for our investigation.

Bottom Line: Among them, TP53 is one of the major genetic risk factor which is known to be mutated in many of the breast tumor types.We have predicted three deleterious coding non-synonymous single nucleotide polymorphisms rs11540654 (R110P), rs17849781 (P278A) and rs28934874 (P151T) in TP53 with a phenotype in breast tumors using computational tools SIFT, Polyphen-2 and MutDB.We have performed molecular dynamics simulations to study the structural and dynamic effects of these TP53 mutations in comparison to the wild-type protein.

View Article: PubMed Central - PubMed

Affiliation: Department of Zoology, Sri Venkateswara University, Tirupati, Andhra Pradesh, India.

ABSTRACT
Breast cancer is one of the most common cancers among the women around the world. Several genes are known to be responsible for conferring the susceptibility to breast cancer. Among them, TP53 is one of the major genetic risk factor which is known to be mutated in many of the breast tumor types. TP53 mutations in breast cancer are known to be related to a poor prognosis and chemo resistance. This renders them as a promising molecular target for the treatment of breast cancer. In this study, we present a computational based screening and molecular dynamic simulation of breast cancer associated deleterious non-synonymous single nucleotide polymorphisms in TP53. We have predicted three deleterious coding non-synonymous single nucleotide polymorphisms rs11540654 (R110P), rs17849781 (P278A) and rs28934874 (P151T) in TP53 with a phenotype in breast tumors using computational tools SIFT, Polyphen-2 and MutDB. We have performed molecular dynamics simulations to study the structural and dynamic effects of these TP53 mutations in comparison to the wild-type protein. Results from our simulations revealed a detailed consequence of the mutations on the p53 DNA-binding core domain that may provide insight for therapeutic approaches in breast cancer.

Show MeSH
Related in: MedlinePlus