Can I read A Graph Theoretic Approach to Utilizing Protein Structure to Identify Non-Random Somatic Mutations on EtoBox?

A Graph Theoretic Approach to Utilizing Protein Structure to Identify Non-Random Somatic Mutations by Ryslik, Gregory; Cheng, Yuwei; Cheung, Kei-Hoi; Modis, Yorgo; Zhao, Hongyu is a scholarly article available to read on EtoBox.

What is A Graph Theoretic Approach to Utilizing Protein Structure to Identify Non-Random Somatic Mutations about?

Background: It is well known that the development of cancer is caused by the accumulation of somatic mutations within the genome. For oncogenes specifically, current research suggests that there is a small set of "driver" mutations that are primarily responsible for tumorigenesis. Further, due to some recent pharmacological successes in treating these driver mutations and their resulting tumors, a variety of methods have been developed to identify potential driver mutations using methods such as machine learning and mutational clustering. We propose a novel methodology that increases our power to identify mutational clusters by taking into account protein tertiary structure via a graph theoretical approach. Results: We have designed and implemented GraphPAC (Graph Protein Amino Acid Clustering) to identify mutational clustering while considering protein spatial structure. Using GraphPAC, we are able to detect novel clusters in proteins that are known to exhibit mutation clustering as well as identify clusters in proteins without evidence of prior clustering based on current methods. Specifically, by utilizing the spatial information available in the Protein Data Bank (PDB) along wi

Author

Ryslik, Gregory; Cheng, Yuwei; Cheung, Kei-Hoi; Modis, Yorgo; Zhao, Hongyu

Published

2013

Language

EN