A cyber-linked undergraduate research experience in computational biomolecular structure prediction and design

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.


Student selection criteria
Student applications were evaluated using the following criteria. Specifically, we ranked each application on a scale of one to five, with: • 1 = Stellar, must-have student • 2 = Super, student would be good for the program • 3 = Acceptable for the program • 4 = Likely not a good match • 5 = Not a good match The student has demonstrated an interest in graduate school in their essay

Potential for scientific research
The student has previous experience working on a scientific research project in an academic or industry lab Potential in computer programming The student has previous experience or has taken one or more courses in computer programming Potential in biophysics and biochemistry The student has previous experience or has taken one or more courses in biophysics and/or biochemistry Motivation In the essay, the student has demonstrated interest and enthusiasm for the field of computational biology and biophysics Strength of recommendations Letters of recommendation indicate the student is motivated to learn and has the potential to succeed in a research lab GPA and transcript Transcript demonstrates strong academic performance in relevant courses Match quality to an open project The students' prior experience and skills are well matched to an available project. The student also indicated interest in that project. Contribution to diversifying our community The student is part of a group traditionally underrepresented in science and engineering.

Rosetta Boot Camp Learning objectives
Here we list learning objectives for Rosetta Boot Camp: a one-week workshop designed to orient students to software engineering and biomolecular modeling in Rosetta. The objectives are organized into three categories: (1) development of translational software engineering skills, (2) navigation of the Rosetta 3 software suite and (3) creation of new methods using the Rosetta 3 libraries.

Selected responses from student survey
Comments about the research community • "When the interns joined up again at the conference it was like no time had passed" • "The networking opportunity I had was invaluable. I know contacts all across the country I can go to for advice, and even a letter of recommendation. The fact that I was treated like a graduate student and given essentially free-reign on the project vastly improved my confidence in conducting scientific research" • "One of the highlights of my experience was how close-knit I became with the other graduate students in the lab" • "Science I'm the first in my family to go to graduate school, it was really helpful to get to know the faculty mentor and graduate student assistant." • "Rosetta was developed by a lot of different labs and there are constantly new things being added, so there has to be some way of managing that process. I learned how a large team can contribute and all maintain the same code base"

Regarding program outcomes
• "I feel more confident in my ability to be more independent in my projects" • "The research experience helped me learn about my own research and work style, and the improvements I need to make to my work ethic. It really clarified for me what a graduate school experience would look like, and showed me that I really would enjoy doing that. Because of this program I have a renewed motivation to participate in graduate school" • "I learned so much about graduate school and the process of research from all members of the lab, not just my mentor" • "It has taught me about the process of applying to graduate school and has really enforced my goals to pursue a PhD" • I was introduced to a new side of computational biology/bioinformatics, which I am interested in pursuing further as a possible career field" • "This summer really solidified my resolve to become a scientist" • "Before the experience I wasn't confident about graduate school because I didn't know what kind of degree I wanted. Now, I'm applying for graduate programs related to computational biology or biomedical informatics which I really enjoyed learning about over the summer"