Evidence map›Paper›PMID 38935611›Full record

ArticlePLoS computational biology2024

Cracking AlphaFold2: Leveraging the power of artificial intelligence in undergraduate biochemistry curriculums.

Devon J Boland, Nicola M Ayres

Abstract read
In one paragraph

Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Isolation andPeerJ · 2025
    Article
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

2 authors.

Devon J BolandDepartment of Biochemistry & Biophysics, Texas A&M University, College Station, Texas, United States of America.ORCID 0000-0001-8903-9718
Nicola M AyresDepartment of Biochemistry & Biophysics, Texas A&M University, College Station, Texas, United States of America.ORCID 0009-0005-4340-3349

Funding

G. Rollie White TrustTexas A&M University College of Agriculture & Life Sciences Endowed Graduate Fellowship for the Borlaug Scholars Program
6 · The paper itself

Abstract

AlphaFold2 is an Artificial Intelligence-based program developed to predict the 3D structure of proteins given only their amino acid sequence at atomic resolution. Due to the accuracy and efficiency at which AlphaFold2 can generate 3D structure predictions and its widespread adoption into various aspects of biochemical research, the technique of protein structure prediction should be considered for incorporation into the undergraduate biochemistry curriculum. A module for introducing AlphaFold2 into a senior-level biochemistry laboratory classroom was developed. The module's focus was to have students predict the structures of proteins from the MPOX 22 global outbreak virus isolate genome, which had no structures elucidated at that time. The goal of this study was to both determine the impact the module had on students and to develop a framework for introducing AlphaFold2 into the undergraduate curriculum so that instructors for biochemistry courses, regardless of their background in bioinformatics, could adapt the module into their classrooms.

Indexed as

Artificial IntelligenceBiochemistryCurriculumAmino Acid SequenceComputational BiologyHumansProtein ConformationProteinsSoftwareStudentsUniversitiesProteins

Identifiers

PMID38935611
PMCPMC11210786

What OpenQuestion holds

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LicenceCC BY
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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.