Evidence map›Paper›PMID 41301667›Full record

ArticleAntibiotics (Basel, Switzerland)2025

Machine Learning-Identified Potent Antimicrobial Peptides Against Multidrug-Resistant Bacteria and Skin Infections.

Gizem Babuççu, Nikitha Vavilthota, Colin Bournez, Leonie de Boer, Robert A Cordfunke, Peter H Nibbering, Gerard J P van Westen, Jan W Drijfhout, Sebastian A J Zaat, Martijn Riool

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Antibiotics (Basel, Switzerland), 2025. 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

10 authors.

Gizem BabuççuDepartment of Medical Microbiology and Infection Prevention, Amsterdam Institute for Infection and Immunity, Amsterdam University Medical Centre, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands.ORCID 0000-0002-6800-8143
Nikitha VavilthotaDepartment of Medical Microbiology and Infection Prevention, Amsterdam Institute for Infection and Immunity, Amsterdam University Medical Centre, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands.
Colin BournezComputational Drug Discovery, Medicinal Chemistry, Leiden Academic Center for Drug Research, Leiden University, 2300 RA Leiden, The Netherlands.ORCID 0000-0001-5568-4110
Leonie de BoerDepartment of Medical Microbiology and Infection Prevention, Amsterdam Institute for Infection and Immunity, Amsterdam University Medical Centre, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands.
Robert A CordfunkeDepartment of Immunology, Leiden University Medical Center, 2300 RC Leiden, The Netherlands.ORCID 0000-0002-7200-7434
Peter H NibberingDepartment of Infectious Diseases, Leiden University Medical Center, 2300 RC Leiden, The Netherlands.
Gerard J P van WestenComputational Drug Discovery, Medicinal Chemistry, Leiden Academic Center for Drug Research, Leiden University, 2300 RA Leiden, The Netherlands.ORCID 0000-0003-0717-1817
Jan W DrijfhoutDepartment of Immunology, Leiden University Medical Center, 2300 RC Leiden, The Netherlands.ORCID 0000-0003-4808-5296
Sebastian A J ZaatDepartment of Medical Microbiology and Infection Prevention, Amsterdam Institute for Infection and Immunity, Amsterdam University Medical Centre, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands.ORCID 0000-0001-9589-186X
Martijn RioolDepartment of Medical Microbiology and Infection Prevention, Amsterdam Institute for Infection and Immunity, Amsterdam University Medical Centre, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands.ORCID 0000-0002-5444-3179

Funding

Dutch Research Council 729.001.024European Commission 955664
6 · The paper itself

Abstract

backgroundThe escalating global crisis of antibiotic resistance necessitates the discovery of novel antimicrobial agents. Antimicrobial peptides (AMPs) represent a promising alternative to combat multidrug-resistant (MDR) pathogens. Because traditional AMP discovery is labour-intensive and costly, machine learning (ML) is applied to identify AMPs effective against MDR bacteria and skin infections.

methodsThe ML-based CalcAMP model predicts the antimicrobial activity of 16,384 unique 14-amino-acid peptide sequences, resulting in a novel Guided Designed Smart antimicrobial Therapeutic (GDST) peptide catalogue. Parent sequences and retro-inverso (RI) variants of two prime GDST peptides undergo extensive testing against MDR bacteria and in skin infection models.

resultsGDST-038 and GDST-045, along with their RI variants, show potent antimicrobial activity against

conclusionsML-driven screening enables rapid identification of two novel candidate AMPs, highlighting the therapeutic potential of GDST peptides for MDR bacterial infections.

Indexed as

3D human epidermal modelantimicrobial peptides (AMPs)antimicrobial resistancebiofilm eradicationmachine learning (ML)wound infection

Identifiers

PMID41301667
PMCPMC12649225

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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.