Including undergraduate research in STEM education is a well-supported and growing high-impact practice that has been made much more scalable through integrating these experiences into the classroom. Here we describe a new biochemistry Course-based Undergraduate Research Experience (CURE) that follows a design-to-data workflow with a strong connection to a worldwide community of protein modeling software developers. Analysis of psychosocial developments in association with participating in this CURE from the first set of students formally participating in the course suggest a beneficial effect on attributes associated with STEM persistence. To increase successful propagation, the design of the CURE's curriculum, supporting learning materials, and instructor resources are provided to make it facile for faculty at any institution to join this network and implement the CURE. With this foundation, we expect student participation and the data set to continue to grow.

A key goal of protein engineering is to accurately model the stability and catalytic activity of enzymes. However, the limitations of functional predictive abilities pose a major challenge for modeling algorithm design, and can be attributed to the lack of large data sets quantifying the functional properties of enzymes. Here, the thermal stability (TM) and Michaelis-Menten constants (kcat, KM, and kcat/KM) of six new variants of the β-glucosidase B (BglB) protein are quantitatively characterized. Molecular stability of the enzyme variants were hypothesized using the Foldit software and BglB was synthesized in E. coli cells. Testing was done through a colorimetric kinetic assay and thermal stability fluorescence-based protein unfolding assay. Results from the assays suggest that all mutations, with the exception of variant Y169M, all experienced reduced catalytic efficiency compared to the wildtype. Assay results indicate that variant W123R is more thermally stable compared to the wildtype, while the differences in thermal stability between the other variants, and the wildtype are negligible. The collected thermal stability and catalytic efficiency data has been added to a data set with the aim of improving Rosetta algorithms for modeling and predicting the functional interactions between biomolecules through a machine learning algorithm and facilitate the precise engineering of protein catalysts.

We present here the draft genome sequences for nine strains of Vibrio (V. cyclitrophicus, V. splendidus, V. tasmaniensis, and three unidentified) and one Shewanella strain. Strains were isolated from red (Haliotis rufescens) and white (Haliotis sorenseni) abalone, with and without exposure to “Candidatus Xenohaliotis californiensis,” the causative agent of abalone withering syndrome.

The Rickettsiales-like prokaryote and causative agent of Withering Syndrome (WS)—Candidatus Xenohaliotis californiensis (Ca. Xc)—decimated black abalone populations along the Pacific coast of North America. White abalone—Haliotis sorenseni—are also susceptible to WS and have become nearly extinct in the wild due to overfishing in the 1970s. Candidatus Xenohaliotis californiensis proliferates within epithelial cells of the abalone gastrointestinal tract and causes clinical signs of starvation. In 2012, evidence of a putative bacteriophage associated with Ca. Xc in red abalone—Haliotis rufescens—was described. Recently, histologic examination of animals with Ca. Xc infection in California abalone populations universally appear to have the phage-containing inclusions. In this study, we investigated the current virulence of Ca. Xc in red abalone and white abalone at different environmental temperatures. Using a comparative experimental design, we observed differences over time between the two abalone species in mortality, body condition, and bacterial load by quantitative real time PCR (qPCR). By day 251, all white abalone exposed to the current variant of Ca. Xc held in the warm water (18.5 °C) treatment died, while red abalone exposed to the same conditions had a mortality rate of only 10%, despite a relatively heavy bacterial burden as determined by qPCR of posterior esophagus tissue and histological assessment at the termination of the experiment. These data support the current status of Ca. Xc as less virulent in red abalone, and may provide correlative evidence of a protective phage interaction. However, white abalone appear to remain highly susceptible to this disease. These findings have important implications for implementation of a white abalone recovery program, particularly with respect to the thermal regimes of locations where captively-reared individuals will be outplanted.

In response to the limited research experiences for young scholars during the COVID-19 pandemic and community interest, we developed the Pre-College Rosetta Internship Opportunity (PCR-IO). The mission of PCR-IO was to offer a program to increase equitable access to computational biomolecular research. The PCR-IO program engaged rising senior high school students in a protein therapeutic design project in which they produced novel structural models using the PyRosetta and Foldit software packages. The program comprised a year-long series of activities, with an immersive summer internship that involved students in research as the cornerstone. These activities aimed to support the overarching goal of the program by expanding participating students’ social capital and technical skills, making them more likely to consider and succeed in STEM in their future endeavors. Here we describe the program’s components and rollout and discuss successes and challenges in implementing a remote computational research-based educational high school program. We observed considerable student skill development and conclude that the program created real added value to student participants’ education. We also uncovered issues associated with curriculum pace and found that the required mentorship effort exceeded our expectations. This perspective is intended to offer insight, share recommendations, and create dialog to increase propagation of research-based computational internships, and to shed light on how much novice students can accomplish with mentorship, structured curricula, and access to the research community.