Resumen de Increasing Computational Protein Design Literacy through Cohort-Based Learning for Undergraduate Students
Erin C. Yang, Robby Divine, Christine S. Kang, Sidney Chan, Elijah Arenas, Zoe Subol, Peter Tinker, Hayden Manninen, Alicia Feichtenbiner, Talal Mustafa, Julia Hallowell, Isiac Orr, Hugh Haddox, Brian Koepnick, Jacob O'Connor, Ian C. Haydon, Karla-Luise Herpoldt, Kandise Van Wormer, Celine Abell, David Baker, Alena Khmelinskaia, Neil P. King
Undergraduate research experiences can improve student success in graduate education and STEM careers. During the COVID-19 pandemic, undergraduate researchers at our institution and many others lost their work–study research positions due to interruption of in-person research activities. This imposed a financial burden on the students and eliminated an important learning opportunity. To address these challenges, we created a paid, fully remote, cohort-based research curriculum in computational protein design. Our curriculum used existing protein design methods as a platform to first educate and train undergraduate students and then to test research hypotheses. In the first phase, students learned computational methods to assess the stability of designed protein assemblies. In the second phase, students used a larger data set to identify factors that could improve the accuracy of current protein design algorithms. This cohort-based program created valuable new research opportunities for undergraduates at our institute and enhanced the undergraduates’ feeling of connection with the lab. Students learned transferable and useful skills such as literature review, programming basics, data analysis, hypothesis testing, and scientific communication. Our program provides a model of structured computational research training opportunities for undergraduate researchers in any field for organizations looking to expand educational access.
