Tegument Protein Subcellular Localization of Human Cytomegalovirus

To determine the subcellular localization of the tegument proteins pp65, pp71, pp150, and pp28 as fusions to one of several fluorescent proteins. Since these tegument proteins play pivotal roles in several stages of the viral life cycle, knowledge of where and the mechanism of how these proteins localize upon release could result in a better understanding of their function during a lytic infection as well as assist in the development of an effective, novel antiviral treatment.

Subcellular Localization of the Non-Structural Proteins 3C and 3CD of the Honeybee Virus Deformed Wing Virus

Apis mellifera L., the European honeybee, is a crucial pollinator of many important agricultural crops in the United States. Recently, honeybee colonies have been affected by Colony Collapse Disorder (CCD), a disorder in which the colony fails due to the disappearance of a key functional group of worker bees. Though no direct causalrelationship has been confirmed, hives that experience CCD have been shown to have a high incidence of Deformed Wing Virus (DWV), a common honeybee virus. While the genome sequence and gene-order of DWV has been analyzed fairly recently, few other studies have been performed to understand the molecular characterization of the virus.Since little is known about where DWV proteins localize in infected host cells, the objective of this project was to determine the subcellular localization of two of the important non-structural proteins that are encoded in the DWV genome. This project focused on the protein 3C, an autocatalytic protease which cleaves itself from a longer polyprotein and helps to cut all of the other proteins apart from one another so that they can become functional, and 3D, the RNA-dependent RNA polymerase (RdRp) which is critical for replication of the virus because it copies the viral genome. By tagging nested constructs containing these two proteins and tracking where they localized in living cells, this study aimed to better understand the replication of DWV and to elicit possible targetsfor further research on how to control the virus. Since DWV is a picorna-like virus, distantly related to human viruses such as polio, and picornavirus non-structural proteins aggregate at cellular membranes during viral replication, the major hypothesis was that the 3C and 3CD proteins would localize at cellular organelle membranes as well. Using confocal microscopy, both proteins were found to localize in the cytoplasm, but the 3CDprotein was found to be mostly diffuse cytoplasmic, and the 3C protein was found to localize more specifically on membranous structures just outside of the nucleus.

Mining for Class-Specific Motifs in Protein Sequence Classification

Background: In protein sequence classification, identification of the sequence motifs or n-grams that can precisely discriminate between classes is a more interesting scientific question than the classification itself. A number of classification methods aim at accurate classification but fail to explain which sequence features indeed contribute to the accuracy. We hypothesize that sequences in lower denominations (n-grams) can be used to explore the sequence landscape and to identify class-specific motifs that discriminate between classes during classification. Discriminative n-grams are short peptide sequences that are highly frequent in one class but are either minimally present or absent in other classes. In this study, we present a new substitution-based scoring function for identifying discriminative n-grams that are highly specific to a class. Results: We present a scoring function based on discriminative n-grams that can effectively discriminate between classes. The scoring function, initially, harvests the entire set of 4- to 8-grams from the protein sequences of different classes in the dataset. Similar n-grams of the same size are combined to form new n-grams, where the similarity is defined by positive amino acid substitution scores in the BLOSUM62 matrix. Substitution has resulted in a large increase in the number of discriminatory n-grams harvested. Due to the unbalanced nature of the dataset, the frequencies of the n-grams are normalized using a dampening factor, which gives more weightage to the n-grams that appear in fewer classes and vice-versa. After the n-grams are normalized, the scoring function identifies discriminative 4- to 8-grams for each class that are frequent enough to be above a selection threshold. By mapping these discriminative n-grams back to the protein sequences, we obtained contiguous n-grams that represent short class-specific motifs in protein sequences. Our method fared well compared to an existing motif finding method known as Wordspy. We have validated our enriched set of class-specific motifs against the functionally important motifs obtained from the NLSdb, Prosite and ELM databases. We demonstrate that this method is very generic; thus can be widely applied to detect class-specific motifs in many protein sequence classification tasks. Conclusion: The proposed scoring function and methodology is able to identify class-specific motifs using discriminative n-grams derived from the protein sequences. The implementation of amino acid substitution scores for similarity detection, and the dampening factor to normalize the unbalanced datasets have significant effect on the performance of the scoring function. Our multipronged validation tests demonstrate that this method can detect class-specific motifs from a wide variety of protein sequence classes with a potential application to detecting proteome-specific motifs of different organisms.