TY - GEN
T1 - Identification of Antimicrobial Peptides from Macroalgae with Machine Learning
AU - Caprani, Michela
AU - Slattery, Orla
AU - O’Keeffe, Joan
AU - Healy, John
N1 - Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Antimicrobial peptides (AMPs) are essential components of innate host defense showing a broad spectrum of activity against bacteria, viruses, fungi, and multi-resistant pathogens. Despite their diverse nature, with high sequence similarities in distantly related mammals, invertebrate and plant species, their presence and functional roles in marine macroalgae remain largely unexplored. In recent years, computational tools have successfully predicted and identified encoded AMPs sourced from ubiquitous dual-functioning proteins, including histones and ribosomes, in various aquatic species. In this paper, a computational design is presented that uses machine learning classifiers, artificial neural networks and random forests, to identify putative AMPs in macroalgae. 42,213 protein sequences from five macroalgae were processed by the classifiers which identified 24 putative AMPs. While initial testing with AMP databases positively identifies these sequences as AMPs, an absolute determination cannot be made without in vitro extraction and purification techniques. If confirmed, these AMPs will be the first-ever identified in macroalgae.
AB - Antimicrobial peptides (AMPs) are essential components of innate host defense showing a broad spectrum of activity against bacteria, viruses, fungi, and multi-resistant pathogens. Despite their diverse nature, with high sequence similarities in distantly related mammals, invertebrate and plant species, their presence and functional roles in marine macroalgae remain largely unexplored. In recent years, computational tools have successfully predicted and identified encoded AMPs sourced from ubiquitous dual-functioning proteins, including histones and ribosomes, in various aquatic species. In this paper, a computational design is presented that uses machine learning classifiers, artificial neural networks and random forests, to identify putative AMPs in macroalgae. 42,213 protein sequences from five macroalgae were processed by the classifiers which identified 24 putative AMPs. While initial testing with AMP databases positively identifies these sequences as AMPs, an absolute determination cannot be made without in vitro extraction and purification techniques. If confirmed, these AMPs will be the first-ever identified in macroalgae.
KW - Antimicrobial peptides
KW - Machine learning classifiers
KW - Macroalgae
KW - Pseudo Amino Acid Composition (PseAAC)
UR - http://www.scopus.com/inward/record.url?scp=85089220385&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-54568-0_1
DO - 10.1007/978-3-030-54568-0_1
M3 - Conference contribution
AN - SCOPUS:85089220385
SN - 9783030545673
T3 - Advances in Intelligent Systems and Computing
SP - 1
EP - 11
BT - Practical Applications of Computational Biology and Bioinformatics, 14th International Conference, PACBB 2020
A2 - Panuccio, Gabriella
A2 - Rocha, Miguel
A2 - Fdez-Riverola, Florentino
A2 - Mohamad, Mohd Saberi
A2 - Casado-Vara, Roberto
PB - Springer
T2 - 14th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2020
Y2 - 17 June 2020 through 19 June 2020
ER -
