CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides

To combat infection by microorganisms host organisms possess a primary arsenal via the innate immune system. Among them are defense peptides with the ability to target a wide range of pathogenic organisms, including bacteria, viruses, parasites, and fungi. Here, we present the development of a novel...

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Bibliographic Details
Published in:Antibiotics (Basel) 2023-04, Vol.12 (4), p.725
Main Authors: Bournez, Colin, Riool, Martijn, de Boer, Leonie, Cordfunke, Robert A, de Best, Leonie, van Leeuwen, Remko, Drijfhout, Jan Wouter, Zaat, Sebastian A J, van Westen, Gerard J P
Format: Article
Language:English
Subjects:
Algorithms
Amino acids
Antibiotics
Antiinfectives and antibacterials
Antimicrobial activity
Antimicrobial agents
Antimicrobial peptides
antimicrobial resistance
artificial intelligence
Bacteria
Cancer
Datasets
Deep learning
drug discovery
Drug resistance
Fungi
Gram-negative bacteria
Gram-positive bacteria
Health aspects
Immune system
Innate immunity
Learning algorithms
Machine learning
Microorganisms
Multidrug resistance
Parasites
Pathogens
Peptides
Physicochemical properties
Prediction models
Streptococcus infections
Viruses
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Summary:To combat infection by microorganisms host organisms possess a primary arsenal via the innate immune system. Among them are defense peptides with the ability to target a wide range of pathogenic organisms, including bacteria, viruses, parasites, and fungi. Here, we present the development of a novel machine learning model capable of predicting the activity of antimicrobial peptides (AMPs), CalcAMP. AMPs, in particular short ones (
ISSN:2079-6382
2079-6382
DOI:10.3390/antibiotics12040725