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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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RMSD and DSSP changes in WT and MT structures during the 10 –ns holo MDS.A) Figure shown at the top represents WT and R110P DSSP plot. In the middle superimposed WT and R110P structures are shown. Yellow: WT, red: R110P. At the bottom, the Cα RMSD plot is shown as a function of time. Black: WT, red: R110P. B) Figure shown at the top represents WT and P151T DSSP plot. In the middle superimposed WT and P151T structures are shown. Yellow: WT, green: P151T. At the bottom, the Cα RMSD plot is shown as a function of time. Black: WT, green: P151T. C) Figure shown at the top represents WT and P278A DSSP plot. In the middle superimposed WT and P278A structures are shown. Yellow: WT, blue: P278A. At the bottom, the Cα RMSD plot is shown as a function of time. Black: WT, blue: P278A.
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pone-0104242-g005: RMSD and DSSP changes in WT and MT structures during the 10 –ns holo MDS.A) Figure shown at the top represents WT and R110P DSSP plot. In the middle superimposed WT and R110P structures are shown. Yellow: WT, red: R110P. At the bottom, the Cα RMSD plot is shown as a function of time. Black: WT, red: R110P. B) Figure shown at the top represents WT and P151T DSSP plot. In the middle superimposed WT and P151T structures are shown. Yellow: WT, green: P151T. At the bottom, the Cα RMSD plot is shown as a function of time. Black: WT, green: P151T. C) Figure shown at the top represents WT and P278A DSSP plot. In the middle superimposed WT and P278A structures are shown. Yellow: WT, blue: P278A. At the bottom, the Cα RMSD plot is shown as a function of time. Black: WT, blue: P278A.

Mentions: Since, RMSD of the Cα atoms is a central origin to compute the protein system [33], we have calculated the respective Cα RMSDs for both apo and holo simulations and plotted in the Fig. 4, 5. During the apo simulations, Cα-RMSD of R110P showed a sharp increase in the initial 2.5 ns followed by equilibrium around 4 ns and a sudden decrease after 9 ns (Fig. 4a). However, P151T and P278A showed a different trend of Cα-RMSD, with an equilibrium around 4 ns and a sudden decrease around 7 ns for P151T (Fig. 4b) whereas an equilibrium around 6 ns and a sudden decrease around 9 ns for P278A (Fig. 4c). During holo simulations on the other hand, Cα-RMSD of R110P showed a less variation in the initial 2 ns followed by equilibrium around 4 ns and a sudden decrease after 5 ns (Fig. 5a). However, P151T and P278A showed a different trend of Cα-RMSD, with an equilibrium around 5 ns and a sudden decrease around 9.5 ns for P151T (Fig. 5b) whereas an equilibrium around 4 ns and a sudden increase around 7 ns for P278A (Fig. 5c). A comparison of average Cα-RMSD values showed the following order of structural deviations (Table 4): apo; R110P > WT > P151T > P278A, holo; R110P > P278A > WT > P151T. These results indicate that a greatest change was observed in the R110P compared to the other mutants in both apo and holo simulations.


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)

RMSD and DSSP changes in WT and MT structures during the 10 –ns holo MDS.A) Figure shown at the top represents WT and R110P DSSP plot. In the middle superimposed WT and R110P structures are shown. Yellow: WT, red: R110P. At the bottom, the Cα RMSD plot is shown as a function of time. Black: WT, red: R110P. B) Figure shown at the top represents WT and P151T DSSP plot. In the middle superimposed WT and P151T structures are shown. Yellow: WT, green: P151T. At the bottom, the Cα RMSD plot is shown as a function of time. Black: WT, green: P151T. C) Figure shown at the top represents WT and P278A DSSP plot. In the middle superimposed WT and P278A structures are shown. Yellow: WT, blue: P278A. At the bottom, the Cα RMSD plot is shown as a function of time. Black: WT, blue: P278A.
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pone-0104242-g005: RMSD and DSSP changes in WT and MT structures during the 10 –ns holo MDS.A) Figure shown at the top represents WT and R110P DSSP plot. In the middle superimposed WT and R110P structures are shown. Yellow: WT, red: R110P. At the bottom, the Cα RMSD plot is shown as a function of time. Black: WT, red: R110P. B) Figure shown at the top represents WT and P151T DSSP plot. In the middle superimposed WT and P151T structures are shown. Yellow: WT, green: P151T. At the bottom, the Cα RMSD plot is shown as a function of time. Black: WT, green: P151T. C) Figure shown at the top represents WT and P278A DSSP plot. In the middle superimposed WT and P278A structures are shown. Yellow: WT, blue: P278A. At the bottom, the Cα RMSD plot is shown as a function of time. Black: WT, blue: P278A.
Mentions: Since, RMSD of the Cα atoms is a central origin to compute the protein system [33], we have calculated the respective Cα RMSDs for both apo and holo simulations and plotted in the Fig. 4, 5. During the apo simulations, Cα-RMSD of R110P showed a sharp increase in the initial 2.5 ns followed by equilibrium around 4 ns and a sudden decrease after 9 ns (Fig. 4a). However, P151T and P278A showed a different trend of Cα-RMSD, with an equilibrium around 4 ns and a sudden decrease around 7 ns for P151T (Fig. 4b) whereas an equilibrium around 6 ns and a sudden decrease around 9 ns for P278A (Fig. 4c). During holo simulations on the other hand, Cα-RMSD of R110P showed a less variation in the initial 2 ns followed by equilibrium around 4 ns and a sudden decrease after 5 ns (Fig. 5a). However, P151T and P278A showed a different trend of Cα-RMSD, with an equilibrium around 5 ns and a sudden decrease around 9.5 ns for P151T (Fig. 5b) whereas an equilibrium around 4 ns and a sudden increase around 7 ns for P278A (Fig. 5c). A comparison of average Cα-RMSD values showed the following order of structural deviations (Table 4): apo; R110P > WT > P151T > P278A, holo; R110P > P278A > WT > P151T. These results indicate that a greatest change was observed in the R110P compared to the other mutants in both apo and holo simulations.

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