Evidence map›Paper›PMID 29216185›Full record

ArticlePLoS computational biology2017

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

Rebecca F Alford, Andrew Leaver-Fay, Lynda Gonzales, Erin L Dolan, Jeffrey J Gray

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.6field-weighted citation impact, top 33% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
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  6. Computational Evolution Protocol for Peptide Design.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
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  8. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Rebecca F AlfordDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.
Andrew Leaver-FayDepartment of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.ORCID http://orcid.org/0000-0003-4609-4340
Lynda GonzalesTexas Institute for Discovery Education in Science, University of Texas, Austin, Texas, United States of America.ORCID http://orcid.org/0000-0003-3724-8408
Erin L DolanTexas Institute for Discovery Education in Science, University of Texas, Austin, Texas, United States of America.
Jeffrey J GrayDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.ORCID http://orcid.org/0000-0001-6380-2324
Johns Hopkins University · USUniversity of Georgia · USUniversity of North Carolina at Chapel Hill · US

Funding

ROSETTA An Integrated Protein Structure Modeling SuiteR01GM073151 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KUHLMAN, BRIAN A · 2005 to 2019
$7.8M
Prediction of the Structure of Therapeutic Antibodies with their AntigensR01GM078221 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI GRAY, JEFFREY J · 2006 to 2020
$4.0M
NIGMS NIH HHS R01 GM073151NIGMS NIH HHS R01 GM078221
6 · The paper itself

Abstract

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.

Indexed as

Computational BiologyMolecular ConformationStudentsAdultFemaleHumansInternetMaleResearchSelf EfficacyUnited StatesUniversitiesWorkforce

Identifiers

PMID29216185
PMCPMC5720517
OpenAlexW2775370318

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.