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- Creators: Barrett, The Honors College
- Creators: Kavazanjian, Edward
- Creators: Allenby, Braden
Though schizophrenia was categorized as a mental illness over 100 years ago, there is a plethora of knowledge that continues to perplex the scientific and medical community alike. This tragic mental disorder affects approximately 1% of the general population, and many of these individuals are homeless if left untreated. Each schizophrenia patient has a different set of symptoms, so all of these patients experience a variety of positive and negative symptoms. Negative symptoms are called so as they are in absence, and some examples include apathy, anhedonia, lack of motivation, reduced social drive, and reduced cognitive functioning. Positive behavior, on the other hand, is a change in behavior or thoughts such as visual or auditory hallucinations, delusions, confused thoughts, disorganized speech, and trouble concentrating. Because schizophrenia patients do not share the exact same set of symptoms, research in schizophrenia requires a tremendous amount of medical resources. Over the last few years, new studies have started in the field of schizophrenia involving proteomics, or the study of proteins and their function. This new frontier gives doctors and scientists alike a new opportunity to improve the quality of life of schizophrenia patients by providing a potential method through which patients would receive individualized treatment based on their specific symptoms.
Oceanic life is facing the deleterious aftermath of coral bleaching. To reverse the damages introduced by anthropological means, it is imperative to study fundamental properties of corals. One way to do so is to understand the metabolic pathways and protein functions of corals that contribute to the resilience of coral reefs. Although genomic sequencing and structural modeling has yielded significant insights for well-studied organisms, more investigation must be conducted for corals. Better yet, quantifiable experiments are far more crucial to the understanding of corals. The objective is to clone, purify, and assess coral proteins from the cauliflower coral species known as Pocillopora damicornis. Presented here is the pipeline for how 3-D structural modeling can help support the experimental data from studying soluble proteins in corals. Using a multi-step selection approach, 25 coral genes were selected and retrieved from the genomic database. Using Escherischia coli and Homo sapiens homologues for sequence alignment, functional properties of each protein were predicted to aid in the production of structural models. Using D-SCRIPT, potential pairwise protein-protein interactions (PPI) were predicted amongst these 25 proteins, and further studied for identifying putative interfaces using the ClusPro server. 10 binding pockets were inferred for each pair of proteins. Standard cloning strategies were applied to express 4 coral proteins for purification and functional assays. 2 of the 4 proteins had visible bands on the Coomassie stained gel and were able to advance to the purification step. Both proteins exhibited a faint band at the expected migration distance for at least one of the elutions. Finally, PPI was carried out by mixing protein samples and running in a native gel, resulting in one potential pair of PPI.
Characterization and Manipulation of Microbiomes From Arid Landfills for Improved Methane Production
(LC-MS/MS) is used to identify and quantify peptides and proteins. LC-MS/MS produces mass spectra, which must be searched by one or more engines, which employ
algorithms to match spectra to theoretical spectra derived from a reference database.
These engines identify and characterize proteins and their component peptides. By
training a convolutional neural network on a dataset of over 6 million MS/MS spectra
derived from human proteins, we aim to create a tool that can quickly and effectively
identify spectra as peptides prior to database searching. This can significantly reduce search space and thus run time for database searches, thereby accelerating LCMS/MS-based proteomics data acquisition. Additionally, by training neural networks
on labels derived from the search results of three different database search engines, we
aim to examine and compare which features are best identified by individual search
engines, a neural network, or a combination of these.