Quantifying the Structure of Misfolded Proteins Using Graph Theory
Institution: | East Tennessee State University |
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Department: | |
Year: | 2017 |
Keywords: | mathematical biology; graph theory; proteins; spectral clustering; computational biology; nest graph model; Other Applied Mathematics |
Posted: | 02/01/2018 |
Record ID: | 2151560 |
Full text PDF: | https://dc.etsu.edu/etd/3244 |
The structure of a protein molecule is highly correlated to its function. Some diseases such as cystic fibrosis are the result of a change in the structure of a protein so that this change interferes or inhibits its function. Often these changes in structure are caused by a misfolding of the protein molecule. To assist computational biologists, there is a database of proteins together with their misfolded versions, called decoys, that can be used to test the accuracy of protein structure prediction algorithms. In our work we use a nested graph model to quantify a selected set of proteins that have two single misfold decoys. The graph theoretic model used is a three tiered nested graph. Measures based on the vertex weights are calculated and we compare the quantification of the proteins with their decoys. Our method is able to separate the misfolded proteins from the correctly folded proteins.