Proteomic Screening for Prediction and Design of Antimicrobial Peptides with AmpGram
Michał Burdukiewicz , Katarzyna Sidorczuk , Dominik Rafacz , Filip Pietluch , Jarosław Chilimoniuk , Stefan Rödiger , Przemysław Gagat
AbstractAntimicrobial peptides (AMPs) are molecules widespread in all branches of the tree of life that participate in host defense and/or microbial competition. Due to their positive charge, hydrophobicity and amphipathicity, they preferentially disrupt negatively charged bacterial membranes. AMPs are considered an important alternative to traditional antibiotics, especially at the time when multidrug-resistant bacteria being on the rise. Therefore, to reduce the costs of experimental research, robust computational tools for AMP prediction and identification of the best AMP candidates are essential. AmpGram is our novel tool for AMP prediction; it outperforms top-ranking AMP classifiers, including AMPScanner, CAMPR3R and iAMPpred. It is the first AMP prediction tool created for longer AMPs and for high-throughput proteomic screening. AmpGram prediction reliability was confirmed on the example of lactoferrin and thrombin. The former is a well known antimicrobial protein and the latter a cryptic one. Both proteins produce (after protease treatment) functional AMPs that have been experimentally validated at molecular level. The lactoferrin and thrombin AMPs were located in the antimicrobial regions clearly detected by AmpGram. Moreover, AmpGram also provides a list of shot 10 amino acid fragments in the antimicrobial regions, along with their probability predictions; these can be used for further studies and the rational design of new AMPs. AmpGram is available as a web-server, and an easy-to-use R package for proteomic analysis at CRAN repository.
|Journal series||International Journal of Molecular Sciences, ISSN 1422-0067|
|Publication size in sheets||215.5|
|Keywords in Polish||AMP; peptydy antydrobnoustrojowe; peptydy odporności gospodarza; lekooporne bakterie; predykcja; badania proteomu; lasy losowe|
|Keywords in English||AMP; antimicrobial peptides; host defense peptides; multidrug-resistant bacteria; prediction; proteomic screening; random forest|
|ASJC Classification||; ; ; ; ; ; ;|
|Abstract in Polish||Praca prezentuje nowy model uczenia maszynowego do przewidywania AMP|
|Score||= 140.0, 09-07-2020, ArticleFromJournal|
|Publication indicators||: 2018 = 1.224; : 2018 = 4.183 (2) - 2018=4.331 (5)|
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