Evidence map›Paper›PMID 41241904›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

Structure and Disorder Predictions of Microproteins: Usage, Applications, and Pitfalls.

Lars A Eicholt

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. 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. Review
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.

Lars A EicholtInstitute for Evolution and Biodiversity, University of Muenster, Muenster, Germany. lars.eicholt@uni-muenster.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the past decade, computational predictions of protein structure and disorder have become widely accessible, achieving accuracy that, in some cases, rivals experimental results. These advances have been instrumental in identifying structural homologies, guiding protein design, and enhancing functional annotation. However, most prediction models are trained on "classical proteins," favoring specific sequence lengths and patterns, leading to biases. Microproteins-small proteins with emerging roles in development and disease-function similarly across the tree of life and stand to benefit significantly from structure and disorder predictions. Yet, their short length and molecular interactions present unique challenges, making homology detection more difficult and requiring careful methodological considerations. Here, I outline workflows for predicting, analyzing, and refining microprotein structures and disorders, emphasizing key precautions to ensure reliable insights.

Indexed as

Computational BiologyIntrinsically Disordered ProteinsProteinsDatabases, ProteinHumansMicropeptidesModels, MolecularProtein ConformationSoftwareIntrinsically Disordered ProteinsMicropeptidesProteinsDisorder predictionMicroproteinsMolecular dynamics simulationsProtein structure predictionSequence feature annotationStructural annotation

Identifiers

What OpenQuestion holds

Textmetadata
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.