A review on antimicrobial peptides databases and the computational tools

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...

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Bibliographic Details
Published in:Database : the journal of biological databases and curation Vol. 2022
Main Authors: Ramazi, Shahin, Mohammadi, Neda, Allahverdi, Abdollah, Khalili, Elham, Abdolmaleki, Parviz
Format: Journal Article
Language:English
Published: England Oxford University Press 19.03.2022
Subjects:
Animals
Antibiotic resistance
Antimicrobial Cationic Peptides - chemistry
Antimicrobial Cationic Peptides - pharmacology
Antimicrobial Peptides
Computer applications
Databases, Factual
Drug development
Drug discovery
Immune system
Immunosuppressive agents
Machine Learning
Mammals
Peptides
Review
Toxicity
ISSN:1758-0463, 1758-0463
Online Access:Get full text
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Summary: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.
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ISSN:1758-0463
1758-0463
DOI:10.1093/database/baac011