Evidence map›Paper›PMID 31361941›Full record

ArticleProtein science : a publication of the Protein Society2020

The antimicrobial peptide database provides a platform for decoding the design principles of naturally occurring antimicrobial peptides.

Guangshun Wang

Abstract read
In one paragraph

Article in Protein science : a publication of the Protein Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing 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

36 citing papers in PubMed.

  1. Review
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  10. Heliyon · 2023
    Article
  11. Are Antimicrobial Peptides a 21st-Century Solution for Atopic Dermatitis?International journal of molecular sciences · 2023
    Review
  12. Review
  13. Review
  14. Article
  15. Article
  16. Review
  17. Article
  18. A review on antimicrobial peptides databases and the computational tools.Database : the journal of biological databases and curation · 2022
    Review
  19. Article
  20. Machine Learning Prediction of Antimicrobial Peptides.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
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.

Guangshun WangDepartment of Pathology and Microbiology, College of Medicine, University of Nebraska Medical Center, Omaha, Nebraska.ORCID 0000-0002-4841-7927

Funding

Database designed novel anti-MRSA peptidesR01AI105147 · NIAID · UNIVERSITY OF NEBRASKA MEDICAL CENTER · PI WANG, GUANGSHUN · 2015 to 2019
$1.9M
NIAID NIH HHS R01 AI105147NIH HHS R01AI105147
6 · The paper itself

Abstract

This article is written for the 2020 tool issue of Protein Science. It briefly introduces the widely used antimicrobial peptide database, initially online in 2003. After a description of the main features of each database version and some recent additions, the focus is on the peptide design parameters for each of the four unified classes of natural antimicrobial peptides (AMPs). The amino acid signature in AMPs varies substantially, leading to a variety of structures for functional and mechanistic diversity. Also, Nature is a master of combinatorial chemistry by deploying different amino acids onto the same structural scaffold to tune peptide functions. In addition, the single-domain AMPs may be posttranslationally modified, self-assembled, or combined with other AMPs for function. Elucidation of the design principles of natural AMPs will facilitate future development of novel molecules for various applications.

Indexed as

Amino Acid MotifsAnti-Infective AgentsDatabases, ProteinDrug DesignPeptidesProtein Structure, SecondaryAnti-Infective AgentsPeptidesantimicrobial peptidesclassificationdatabase toolpeptide designpeptide signature

Identifiers

PMID31361941
PMCPMC6933855

What OpenQuestion holds

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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.