Evidence map›Paper›PMID 35305010›Full record

ReviewDatabase : the journal of biological databases and curation2022

A review on antimicrobial peptides databases and the computational tools.

Shahin Ramazi, Neda Mohammadi, Abdollah Allahverdi, Elham Khalili, Parviz Abdolmaleki

Open access · goldAbstract readReview
In one paragraph

Review in Database : the journal of biological databases and curation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.

0numbers the graph read from it
0cells of the map it votes in
61citing papers in PubMed
17.1field-weighted citation impact, top 1% of its field
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

61 citing papers in PubMed, 135 citations in OpenAlex.

  1. Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027
    Review
  2. Review
  3. Article
  4. Review
  5. Review
  6. Article
  7. Review
  8. Mechanisms ofMedicina (Kaunas, Lithuania) · 2026
    Review
  9. Review
  10. Bacteriocins fromFrontiers in microbiology · 2026
    Review
  11. Review
  12. Review
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Review
  20. Article

1 more citing papers are in PubMed but not listed here.

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

5 authors at 2 institutions in 2 countries.

Shahin RamaziDepartment of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Jalal Ale Ahmad Highway, Tehran 14115-111, Iran.ORCID 0000-0003-4131-2919
Neda MohammadiDepartment of Medical Biotechnology, Faculty of Allied Medical Sciences, Iran University of Medical Sciences, Hemmat Highway, Tehran 1449614535, Iran.
Abdollah AllahverdiDepartment of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Jalal Ale Ahmad Highway, Tehran 14115-111, Iran.
Elham KhaliliDepartment of Plant Biology, Faculty of Biological Sciences, Tarbiat Modares University, Jalal Ale Ahmad Highway, Tehran 14115-111, Iran.
Parviz AbdolmalekiDepartment of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Jalal Ale Ahmad Highway, Tehran 14115-111, Iran.
Tarbiat Modares University · IRUniversity of Bonn · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antimicrobial Peptides (AMPs) have been considered as potential alternatives for infection therapeutics since antibiotic resistance has been raised as a global problem. The AMPs are a group of natural peptides that play a crucial role in the immune system in various organisms AMPs have features such as a short length and efficiency against microbes. Importantly, they have represented low toxicity in mammals which makes them potential candidates for peptide-based drugs. Nevertheless, the discovery of AMPs is accompanied by several issues which are associated with labour-intensive and time-consuming wet-lab experiments. During the last decades, numerous studies have been conducted on the investigation of AMPs, either natural or synthetic type, and relevant data are recently available in many databases. Through the advancement of computational methods, a great number of AMP data are obtained from publicly accessible databanks, which are valuable resources for mining patterns to design new models for AMP prediction. However, due to the current flaws in assessing computational methods, more interrogations are warranted for accurate evaluation/analysis. Considering the diversity of AMPs and newly reported ones, an improvement in Machine Learning algorithms are crucial. In this review, we aim to provide valuable information about different types of AMPs, their mechanism of action and a landscape of current databases and computational tools as resources to collect AMPs and beneficial tools for the prediction and design of a computational model for new active AMPs.

Indexed as

Antimicrobial Cationic PeptidesAntimicrobial PeptidesAnimalsDatabases, FactualMachine LearningMammalsAntimicrobial Cationic PeptidesAntimicrobial Peptides

Identifiers

PMID35305010
PMCPMC9216472
OpenAlexW4214618924

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.