Alford, Rebecca F, et al. A Cyber-linked Undergraduate Research Experience In Computational Biomolecular Structure Prediction and Design. 2017. https://doi.org/10.17615/e400-1y21
Alford, R., Leaver Fay, A., Gonzales, L., Dolan, E., & Gray, J. (2017). A cyber-linked undergraduate research experience in computational biomolecular structure prediction and design. https://doi.org/10.17615/e400-1y21
Alford, Rebecca F., Andrew Leaver Fay, Lynda Gonzales, Erin L Dolan, and Jeffrey J Gray. 2017. A Cyber-Linked Undergraduate Research Experience In Computational Biomolecular Structure Prediction and Design. https://doi.org/10.17615/e400-1y21
Other Affiliation: Department of Chemical and Biomolecular Engineering; Johns Hopkins University
Affiliation: School of Medicine, Department of Biochemistry and Biophysics
Other Affiliation: Texas Institute for Discovery Education in Science; University of Texas
Dolan, Erin L.
Other Affiliation: Department of Biochemistry and Molecular Biology; University of Georgia
Gray, Jeffrey J.
Other Affiliation: Institute for NanoBioTechnology; Johns Hopkins University
Computational biology is an interdisciplinary field, and many computational biology research projects involve distributed teams of scientists. To accomplish their work, these teams must overcome both disciplinary and geographic barriers. Introducing new training paradigms is one way to facilitate research progress in computational biology. Here, we describe a new undergraduate program in biomolecular structure prediction and design in which students conduct research at labs located at geographically-distributed institutions while remaining connected through an online community. This 10-week summer program begins with one week of training on computational biology methods development, transitions to eight weeks of research, and culminates in one week at the Rosetta annual conference. To date, two cohorts of students have participated, tackling research topics including vaccine design, enzyme design, protein-based materials, glycoprotein modeling, crowd-sourced science, RNA processing, hydrogen bond networks, and amyloid formation. Students in the program report outcomes comparable to students who participate in similar in-person programs. These outcomes include the development of a sense of community and increases in their scientific self-efficacy, scientific identity, and science values, all predictors of continuing in a science research career. Furthermore, the program attracted students from diverse backgrounds, which demonstrates the potential of this approach to broaden the participation of young scientists from backgrounds traditionally underrepresented in computational biology.