| Literature DB >> 35916219 |
Dennis Della Corte1, Connor J Morris1, Wendy M Billings1, Jacob Stern1, Austin J Jarrett1, Bryce Hedelius1, Adam Bennion1.
Abstract
Effective mentoring of undergraduate students is a growing requirement for the promotion of faculty at many universities. It is often challenging for young investigators to define a successful mentoring strategy, partially due to the absence of a broadly accepted definition of what mentoring should entail. To overcome this, an outcome-oriented mentoring framework was developed and used with more than 25 students over three years. It was found that a systematic mentoring approach can help students quickly realize their scientific potential and result in meaningful contributions to science. This report especially shows how the Critical Assessment of Protein Structure Prediction (CASP14) challenge was used to amplify student research efforts. As a result of this challenge, multiple publications, presentations and scholarships were awarded to the participating students. The mentoring framework continues to see much success in allowing undergraduate students, including students from underrepresented groups, to foster scientific talent and make meaningful contributions to the scientific community. open access.Entities:
Keywords: CASP; deep learning; mentoring; protein structure prediction; undergraduates
Mesh:
Year: 2022 PMID: 35916219 PMCID: PMC9344475 DOI: 10.1107/S2059798322005861
Source DB: PubMed Journal: Acta Crystallogr D Struct Biol ISSN: 2059-7983 Impact factor: 5.699
Figure 1Outcome-oriented skill-based mentoring strategy for undergraduate research experiences.
Students involved in this study who contributed to CASP14
| Name | Joined research group | Gender | Major | Current program |
|---|---|---|---|---|
| Mary | October 2018 | Female | Chemistry | Chemistry PhD at Berkeley |
| Bill | October 2018 | Male | Applied Physics, pre-med | Bachelor of Physics BYU, MD/PhD UCLA |
| Joe | October 2018 | Male | Physics | MS Physics BYU |
| Kyle | December 2020 | Male | Computer Science | PhD Computer Science BYU |
Overview of community challenges in computational biology (adapted from Boutros et al., 2014 ▸)
| Challenge | Scope | Assessment type | Organizers | Website |
|---|---|---|---|---|
| CAFA | Protein function prediction | Objective scoring | Community collaboration |
|
| CAGI | Systems biology | Objective scoring | UC Berkeley/University of Maryland |
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| CAPRI | Protein docking | Objective scoring | Community collaboration |
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| CASP | Structure prediction | Objective scoring | Community collaboration |
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| CACHE | Drug discovery | Objective scoring | Community/industry collaboration |
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| ChaLearn | Machine learning | Objective scoring | ChaLearn Organization (not for profit) |
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| DREAM | Network inference and systems biology | Objective scoring | Community collaboration and Sage Bionetworks |
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| FlowCAP | Flow cytometry analysis | Objective scoring | Community collaboration |
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| IGCG–TCGA DREAM Somatic Mutation Calling | Sequence analysis | Objective evaluation | Community collaboration and Sage Bionetworks |
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| Kaggle | Topics in various industries | Objective scoring and evaluation by judges | Commercial platform |
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| X-Prize | Technology | Evaluation by judges | X-Prize Organization (not for profit) |
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| 2021 Ligand Model Challenge | Structure determination | Objective evaluation | Community collaboration |
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Data sources used for analysis
| Data source | Amount | Description |
|---|---|---|
| Laboratory journals | 3 | Digital or handwritten accounts of the work the participants engaged in as they moved through the CASP project as a part of the mentoring program |
| Project calendars | 6 | These calendars detail how often the laboratory group met and what the objective of each meeting was for a given semester |
| Semi-structured interviews | 5 | Interviews (∼25 min) of the undergraduates and mentor professor who participated in the research mentoring program. The interviews focused on learning about the skills developed and outcomes achieved because of the mentoring strategy. |
Skills reported by participants in post-experiment interviews
| Skill | Description | Example |
|---|---|---|
| Technical | Students gain technical skills that are discipline-specific as they work in various group projects |
|
| Understanding literature | Students learn how to read, interpret and write literature as they begin investigating their own field of interest. They are guided in this during the regular laboratory group meetings as they present and discuss their findings. |
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| Communication and networking | Students learn how to reach out to experts in the field and to discuss issues that are meaningful to their study. This often happens and is developed at conferences. |
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| Project development | Students learn how to ask questions, develop hypotheses, collect and analyze data, and other science practice-type skills as they develop their own projects |
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| Presenting and publishing | Students develop their ability to present and publish their research as they work toward the project goals and the competition deadlines |
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Perspectives on the benefits of community challenges
| Challenge features | Description | Example |
|---|---|---|
| Develop new methods | Participating in challenges allows your research group to be a part of cutting-edge science and the development of new methods |
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| Timelines | Community challenges provide real deadlines which can help motivate and move the work along |
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| Clear goals | The community challenge gives the research group clear goals to help focus the work and keep students on track |
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| Competition and gamification | Knowing your work will be compared with others in a contest can motivate students and it gamifies what normally might be seen as regular work |
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| Community integration | Community challenges give students the opportunity to expand their professional networks as they present their findings and interact with other scholars |
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| Career advancement | Community challenges give students the opportunity to present their work at a professional conference and can lead to publications in relevant journals |
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Figure 2Mapping the CASP timeline to our mentoring strategy.
Figure 3Visualization of successful refinement targets by the Della Corte Laboratory at CASP14. The goal of refinement is to move the pink start structure closer to the golden target structure. Blue is an improved submission, where improvement is expressed in delta root-mean-square-deviation (ΔRMSD) between the target and start versus target and submission Cα-atom positions of the protein structures.