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"Use Of Long Term Molecular Dynamics Simulation In Predicting Cancer Associated SNPs." and the language of the book is English.


“Use Of Long Term Molecular Dynamics Simulation In Predicting Cancer Associated SNPs.” Metadata:

  • Title: ➤  Use Of Long Term Molecular Dynamics Simulation In Predicting Cancer Associated SNPs.
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  • Language: English

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  • Internet Archive ID: pubmed-PMC3983272

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This article is from <a href="//archive.org/search.php?query=journaltitle%3A%28PLoS%20Computational%20Biology%29" rel="nofollow">PLoS Computational Biology</a>, <a href="//archive.org/search.php?query=journaltitle%3A%28PLoS%20Computational%20Biology%29%20AND%20volume%3A%2810%29" rel="nofollow">volume 10</a>.<h2>Abstract</h2>Computational prediction of cancer associated SNPs from the large pool of SNP dataset is now being used as a tool for detecting the probable oncogenes, which are further examined in the wet lab experiments. The lack in prediction accuracy has been a major hurdle in relying on the computational results obtained by implementing multiple tools, platforms and algorithms for cancer associated SNP prediction. Our result obtained from the initial computational compilations suggests the strong chance of Aurora-A G325W mutation (rs11539196) to cause hepatocellular carcinoma. The implementation of molecular dynamics simulation (MDS) approaches has significantly aided in raising the prediction accuracy of these results, but measuring the difference in the convergence time of mutant protein structures has been a challenging task while setting the simulation timescale. The convergence time of most of the protein structures may vary from 10 ns to 100 ns or more, depending upon its size. Thus, in this work we have implemented 200 ns of MDS to aid the final results obtained from computational SNP prediction technique. The MDS results have significantly explained the atomic alteration related with the mutant protein and are useful in elaborating the change in structural conformations coupled with the computationally predicted cancer associated mutation. With further advancements in the computational techniques, it will become much easier to predict such mutations with higher accuracy level.

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