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


Antimicrobial 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.
Author Michał Burdukiewicz (FMIS / DCSDCAM)
Michał Burdukiewicz,,
- Department of CAD/CAM Systems Design and Computer-Aided Medicine
, Katarzyna Sidorczuk
Katarzyna Sidorczuk,,
, Dominik Rafacz
Dominik Rafacz,,
, Filip Pietluch
Filip Pietluch,,
, Jarosław Chilimoniuk
Jarosław Chilimoniuk,,
, Stefan Rödiger
Stefan Rödiger,,
, Przemysław Gagat
Przemysław Gagat,,
Journal seriesInternational Journal of Molecular Sciences, ISSN 1422-0067
Issue year2020
Publication size in sheets215.5
Keywords in PolishAMP; peptydy antydrobnoustrojowe; peptydy odporności gospodarza; lekooporne bakterie; predykcja; badania proteomu; lasy losowe
Keywords in EnglishAMP; antimicrobial peptides; host defense peptides; multidrug-resistant bacteria; prediction; proteomic screening; random forest
ASJC Classification2700 General Medicine; 1312 Molecular Biology; 1503 Catalysis; 1604 Inorganic Chemistry; 1605 Organic Chemistry; 1606 Physical and Theoretical Chemistry; 1607 Spectroscopy; 1706 Computer Science Applications
Abstract in PolishPraca prezentuje nowy model uczenia maszynowego do przewidywania AMP
Languageen angielski
Score (nominal)140
Score sourcejournalList
ScoreMinisterial score = 140.0, 09-07-2020, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2018 = 1.224; WoS Impact Factor: 2018 = 4.183 (2) - 2018=4.331 (5)
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