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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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RMSF of Cα atoms as a function of amino acids.
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pone-0104242-g006: RMSF of Cα atoms as a function of amino acids.

Mentions: In order to understand how the mutants affect the dynamic behaviour of the residues and to examine the cause of conformational drifts observed in RMSD and secondary structure patterns, Cα-root mean square fluctuation (Cα-RMSF) of WT and MT amino acid residues were calculated and plotted in the Fig. 6a-d. Except P151T, in all cases the MT holo simulations had higher average Cα-RMSFs than the WT holo simulations (Fig. 6a) (Table 4). In the apo and holo WT, more than 50% of the residues have RMSF values >0.1 nm (Table 5) indicating a higher level of fluctuation. During the holo simulations, all the MTs showed a larger percentage of residues (i.e., Cα residues and residues in the protein core comprising secondary structural elements) with RMSF values >0.1 nm whereas during apo simulations less percentage of residues showed RMSF values >0.1 nm compared to the WT. These results indicate that compared to apo, holo simulations are associated with increase in flexibilities in MTs. Among the holo MTs, R110P have a higher percentage of residues with RMSF >0.1 nm thus indicating a higher effect on the overall flexibility of the p53C.


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)

RMSF of Cα atoms as a function of amino acids.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0104242-g006: RMSF of Cα atoms as a function of amino acids.
Mentions: In order to understand how the mutants affect the dynamic behaviour of the residues and to examine the cause of conformational drifts observed in RMSD and secondary structure patterns, Cα-root mean square fluctuation (Cα-RMSF) of WT and MT amino acid residues were calculated and plotted in the Fig. 6a-d. Except P151T, in all cases the MT holo simulations had higher average Cα-RMSFs than the WT holo simulations (Fig. 6a) (Table 4). In the apo and holo WT, more than 50% of the residues have RMSF values >0.1 nm (Table 5) indicating a higher level of fluctuation. During the holo simulations, all the MTs showed a larger percentage of residues (i.e., Cα residues and residues in the protein core comprising secondary structural elements) with RMSF values >0.1 nm whereas during apo simulations less percentage of residues showed RMSF values >0.1 nm compared to the WT. These results indicate that compared to apo, holo simulations are associated with increase in flexibilities in MTs. Among the holo MTs, R110P have a higher percentage of residues with RMSF >0.1 nm thus indicating a higher effect on the overall flexibility of the p53C.

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