Month: September 2026

Discovering novel N acyl amides to bind to GPCRs

N acyl amides are a newly discovered lipid class that exists within both animals and humans. It has been previously studied that N-acyl amides can interact with GPCRs as either agonist or antagonists depending on what GPCRs are being looked at. From data collected from the American Gut paper which focused on gut samples taken from individuals different countries we were able to come up with predicted N acyl amide structures. We can use in silico docking predictions to test these proposed structures to come up with novel N acyl amides that can later be developed into GPCR agonists or antagonists.

Novel antifungal metabolism products from the interactions between Bacillus subtilis and Setophoma terrestris

The diversity of soil microbes results in extensive interspecies interactions. These communities play a major role in the ecosystem’s health and agricultural production. Bacillus subtilis, a diverse Gram-positive bacterial species found in the upper soil and plant rhizosphere, has been observed to interact with the soil fungus Setophoma terrestris, a major plant pathogen that affects economically important crops. In subtropical and tropical regions, S. terrestris has caused pink root disease in onions. Interestingly, the interactions between S. terrestris and B. subtilis ALBA01 resulted in metabolites associated with antifungal activity, such as surfactin and plipastatin. Using LC-MS and GC-MS based metabolomics, we can further look into the production of antifungal compounds whose nature remains unexplained. This project is a collaboration with Dr. Andrea Albarracin Orio at the Universidad Nacional de Córdoba.

Geisha Coffee flavor profiling on a molecular level

Coffee is one of the world’s most valuable beverages and agricultural commodities. Recent studies have revealed the diversity of microorganisms present during the process of coffee fermentation. This, along with the soil quality, influences the coffee’s flavor. Although the microbial diversity in the coffee fermentation process has been widely studied, the geisha variety (one of the most expensive and valuable coffees in the world) has been unexplored. Our goal is to determine the chemical differences between the different microbial communities within the endosperm of the coffee, and how fermentation influences the flavor profile. This project is a collaboration with the Dalling lab at the University of Illinois Urbana-Champaign and Hacienda la Esmeralda in Palmira, Panama.

GC-MS High Resolution In-Silico Prediction Model

Recently, mass spectrometry chemists have implemented in-silico machine learning algorithms to analyze their data for molecular determination. As of now, open-access models that exist perform on either liquid chromatography-mass spectrometry (LC-MS) data, or low resolution gas chromatography-mass spectrometry (GC-MS) electron ionization (EI) data. Since LC-MS is a relatively recent technique, it consistently delivers high-resolution data. In contrast, GC-MS, a hard ionization technique with significant fragmentation, traditionally yielded low-resolution data since it was developed in the 1950s. Over the past two decades, enhancements to GC-MS have yielded high-resolution data. Consequently, there is a need to develop
high-resolution GC-MS spectrum libraries to initiate the machine learning process
for such data. With our own instrumentation and publicly available datasets, we can gather millions of spectral information to construct a local database to train the algorithm on high resolution GC-MS data.