One-Step Design of Potent and Nonhemolytic Antimicrobial Peptides by Using a Database-Guided, Nonmachine Learning Approach.

Abraham F Mechesso, Arjun R Nair, Guangshun Wang

Abstract read

In one paragraph

Article in ACS infectious diseases, 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.

Abraham F MechessoDepartment of Pathology, Microbiology and Immunology, College of Medicine, University of Nebraska Medical Center, 985900 Nebraska Medical Center, Omaha, Nebraska 68198-5900, United States.

Arjun R NairDepartment of Pathology, Microbiology and Immunology, College of Medicine, University of Nebraska Medical Center, 985900 Nebraska Medical Center, Omaha, Nebraska 68198-5900, United States.

Guangshun WangDepartment of Pathology, Microbiology and Immunology, College of Medicine, University of Nebraska Medical Center, 985900 Nebraska Medical Center, Omaha, Nebraska 68198-5900, United States.ORCID 0000-0002-4841-7927

Funding

Novel antimicrobials to combat Gram-negative bacteriaR56AI175209 · NIAID · UNIVERSITY OF NEBRASKA MEDICAL CENTER · PI MURRY, DARYL, WANG, GUANGSHUN · 2023 to 2023

$384k

NIAID NIH HHS R56 AI175209

6 · The paper itself

Abstract

The search for antibiotics is urgent because of the global antibiotic resistance problem. While a variety of strategies are actively sought, interest in antimicrobial peptides persists due to high potency and low chance of resistance development. The establishment of the antimicrobial peptide database laid the foundation for peptide prediction and design. Both artificial intelligence and non-AI approaches have been demonstrated. Since AI remains a black box and does not teach us how to design peptide antibiotics, this study took a database-guided approach. Our peptide design benefited from the recent classification of peptides into hemolytic and nonhemolytic groups in the APD6. Our designed peptides rapidly killed Gram-negative bacteria

Indexed as

Anti-Bacterial AgentsAntimicrobial Cationic PeptidesAntimicrobial PeptidesDrug DesignArtificial IntelligenceBiofilmsGram-Negative BacteriaHemolysisMicrobial Sensitivity TestsAnti-Bacterial AgentsAntimicrobial Cationic PeptidesAntimicrobial Peptidesantibiofilmantimicrobial peptidesAPD6cytotoxicitypeptide design

Identifiers

PMID41558672

DOI10.1021/acsinfecdis.5c01022

PMCPMC12927767

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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.