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

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

Current Knowledge on Microarray Technology - An Overview

The completion of whole genome sequencing projects has led to a rapid increase in the availability of genetic information. In the field of transcriptomics, the emergence of microarray-based technologies and the design of DNA biochips allow high-throughput studies of RNA expression in cell and tissue at a given moment. It has emerged as one of the most important technology in the field of molecular biology and transcriptomics. Arrays of oligonucleotide or DNA sequences are being used for genome-wide genotyping and expression profiling, and several potential clinical applications have begun to emerge as our understanding of these techniques and the data they generate improves. From its emergence to date, several database, software and technology updates have been developed in the field of microarray technology. This paper reviews basics and updates of each microarray technology and serves to introduce newly compiled resources that will provide specialist information in this area

Variation of the secondary structure versus time for the Glucocorticoid receptor and Glucocorticoid Receptor.-MCDF complex during (A) 10 ns (B) 20 ns and (C) 30 ns MDS.

<p>Top represents the Glucocorticoid receptor and the bottom represents Glucocorticoid Receptor-MCDF complex.</p

Backbone rmsd values for WT, R110P, P151T and P278A during A) Apo simulations B) Holo simulations for p53C.

<p>Black: WT, red: R110P, green: P151T and blue: P278A.</p

RMSD and DSSP changes in WT and MT structures during the 10-ns apo MDS.

<p>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.</p

Backbone Radius of gyration (Rg) versus time plot during the 30 ns molecular dynamics simulation for Glucocorticoid Receptor (blue) and Glucocorticoid Receptor-MCDF complex (red).

<p>Backbone Radius of gyration (Rg) versus time plot during the 30 ns molecular dynamics simulation for Glucocorticoid Receptor (blue) and Glucocorticoid Receptor-MCDF complex (red).</p

Ligplot showing the interactions of metal ion (Zn) with the amino acid residues of the protein.

<p>An atom of Zn bound with a tetra-co-ordinate geometry to three Cysteines (Cys 176, 238 and 242) and one Histidine (His 179).</p

Computational Prediction and Analysis of Breast Cancer Targets for 6-Methyl-1, 3, 8-Trichlorodibenzofuran

<div><p>Breast cancer is one of the most known cancer types caused to the women around the world. Dioxins on the other hand are a wide range of chemical compounds known to cause the effects on human health. Among them, 6-Methyl-1,3,8-trichlorodibenzofuran (MCDF) is a relatively non toxic prototypical alkyl polychlorinated dibenzofuran known to act as a highly effective agent for inhibiting hormone-responsive breast cancer growth in animal models. In this study, we have developed a multi-level computational approach to identify possible new breast cancer targets for MCDF. We used PharmMapper Server to predict breast cancer target proteins for MCDF. Search results showed crystal Structure of the Antagonist Form of Glucocorticoid Receptor with highest fit score and AutoLigand analysis showed two potential binding sites, site-A and site-B for MCDF. A molecular docking was performed on these two sites and based on binding energy site-B was selected. MD simulation studies on Glucocorticoid receptor-MCDF complex revealed that MCDF conformation was stable at site-B and the intermolecular interactions were maintained during the course of simulation. In conclusion, our approach couples reverse pharmacophore analysis, molecular docking and molecular dynamics simulations to identify possible new breast cancer targets for MCDF.</p></div

Time averaged structural properties calculated for WT, R110P, P151T, P278A holo [with Zn²⁺ ion present] and apo [with Zn²⁺ ion absent] p53 core domain.

<p>Mean values—averaged over the trajectory or over the number of residues employed at each calculation—with standard deviations given in parentheses. Cα-rmsd: Cα-root-mean-square deviation, Rg: Radius of gyration; SASA: Solvent Accessible Surface Area</p

Respective breast cancer role for the potential targets of MCDF predicted using reverse pharmacophore approach.

<p>Respective breast cancer role for the potential targets of MCDF predicted using reverse pharmacophore approach.</p