Viruses are unique organisms that lack the protein machinery necessary for its propagation (like polymerase) yet possess other proteins that facilitate its propagation (like host cell anchoring proteins). This study explores seven different frameworks to assist rapid visualization of proteins that are common to viruses residing in a given host. The proposed frameworks rely only on protein sequence information. It was found that the sequence similarity-based framework with an associated profile hidden Markov model was a better tool to assist visualization of proteins common to a given host than other proposed frameworks based only on amino acid composition or other amino acid properties. The lack of knowledge of profile hidden Markov models for many protein structures limit the utility of the proposed protein sequence similarity-based framework. The study concludes with an attempt to extrapolate the utility of the proposed framework to predict viruses that may pose potential human health risks.
| Identifer | oai:union.ndltd.org:ndsu.edu/oai:library.ndsu.edu:10365/31716 |
| Date | January 2019 |
| Creators | Subramaniam, Rajesh |
| Publisher | North Dakota State University |
| Source Sets | North Dakota State University |
| Detected Language | English |
| Type | text/thesis |
| Format | application/pdf |
| Rights | NDSU policy 190.6.2, https://www.ndsu.edu/fileadmin/policy/190.pdf |
Page generated in 0.0118 seconds