Deep learning reveals antimicrobial peptides within prions.

Marcelo D T Torres, Fangping Wan, Cesar de la Fuente-Nunez

Abstract read

In one paragraph

Article in Nature microbiology, 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

3 authors.

Marcelo D T Torres *Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0002-6165-9138

Fangping Wan *Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Cesar de la Fuente-NunezMachine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. cfuente@upenn.edu.ORCID http://orcid.org/0000-0002-2005-5629

Funding

Combining chemical and computational tools for predictive models of microbiome communitiesR35GM138201 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI DE LA FUENTE, CESAR · 2020 to 2024

$1.8M

NIGMS NIH HHS R35 GM138201United States Department of Defense | Defense Threat Reduction Agency (DTRA) HDTRA1-21-1-0014U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) R35GM138201

6 · The paper itself

Abstract

Prion and prion-like proteins are classically associated with protein misfolding, but amyloidogenic sequences can also participate in host defence. Here, using deep learning, we screened 19.3 million fragments from 2,897 curated prion-related proteins and identified 1,179 candidate antimicrobial peptides, which we term prionins. Among 75 synthesized prionins, 59 inhibited bacterial pathogens, 53 perturbed membranes and 2 reduced Acinetobacter baumannii infection burden in mice.

Indexed as

Antimicrobial PeptidesDeep LearningPrionsAcinetobacter baumanniiAnimalsMiceAntimicrobial PeptidesPrions

Identifiers

PMID42321536

DOI10.1038/s41564-026-02408-1

PMCPMC13423867

What Socratic 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 Socratic graph.