Evidence map›Paper›PMID 42589211›Full record

ArticleInternational journal of molecular sciences2026

AI-Predicted Model-Guided Rebuilding of the Experimental Structure of Mouse δ-Aminolevulinic Acid Dehydratase.

Ki Hyun Nam

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Ki Hyun NamCollege of General Education, Kookmin University, Seoul 02707, Republic of Korea.ORCID 0000-0003-3268-354X

Funding

National Research Foundation of Korea RS-2026-25496931
6 · The paper itself

Abstract

Experimental macromolecular structures are foundational for elucidating molecular mechanisms and guiding drug design and protein engineering. However, inaccurate structural models are occasionally deposited in the Protein Data Bank (PDB), potentially confounding structural analyses and misleading subsequent studies. Although the integration of artificial intelligence (AI)-predicted models to improve experimentally determined structures has been proposed, the impact of AI-guided rebuilding on functional interpretation remains largely uncharacterized. Here, AI-predicted models of δ-aminolevulinic acid dehydratase (ALAD) were analyzed, and a misinterpreted region in its original crystal structure was rebuilt using an AlphaFold3 (AF3) model as a template. The incorrectly modeled region between Cys122 and Leu142 was corrected utilizing the AF3 main-chain conformation. This rebuilding decreased the R

Indexed as

Artificial IntelligencePorphobilinogen SynthaseAnimalsBinding SitesCatalytic DomainCrystallography, X-RayDatabases, ProteinMiceModels, MolecularProtein ConformationPorphobilinogen Synthasealphafoldartifact intelligentexperimental structurerebuildingδ-aminolevulinic

Identifiers

PMID42589211
PMCPMC13465085

What OpenQuestion holds

Textmetadata
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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.