Taxonomic bias in AMP prediction of invertebrate peptides
Abstract Invertebrate antimicrobial peptides (AMPs) are at the forefront in the search for agents of therapeutic utility against multi-resistant microbial pathogens, and in recent years substantial advances took place in the in silico prediction of antimicrobial function of amino acid sequences. A y...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-09-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-97415-z |
| _version_ | 1818431723495489536 |
|---|---|
| author | Zoltán Rádai Johanna Kiss Nikoletta A. Nagy |
| author_facet | Zoltán Rádai Johanna Kiss Nikoletta A. Nagy |
| author_sort | Zoltán Rádai |
| collection | DOAJ |
| description | Abstract Invertebrate antimicrobial peptides (AMPs) are at the forefront in the search for agents of therapeutic utility against multi-resistant microbial pathogens, and in recent years substantial advances took place in the in silico prediction of antimicrobial function of amino acid sequences. A yet neglected aspect is taxonomic bias in the performance of these tools. Owing to differences in the prediction algorithms and used training data sets between tools, and phylogenetic differences in sequence diversity, physicochemical properties and evolved biological functions of AMPs between taxa, notable discrepancies may exist in performance between the currently available prediction tools. Here we tested if there is taxonomic bias in the prediction power in 10 tools with a total of 20 prediction algorithms in 19 invertebrate taxa, using a data set containing 1525 AMP and 3050 non-AMP sequences. We found that most of the tools exhibited considerable variation in performance between tested invertebrate groups. Based on the per-taxa performances and on the variation in performances across taxa we provide guidance in choosing the best-performing prediction tool for all assessed taxa, by listing the highest scoring tool for each of them. |
| first_indexed | 2024-12-14T15:53:50Z |
| format | Article |
| id | doaj.art-44a2751542f54a958d4d9824600cb3d4 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-14T15:53:50Z |
| publishDate | 2021-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-44a2751542f54a958d4d9824600cb3d42022-12-21T22:55:17ZengNature PortfolioScientific Reports2045-23222021-09-0111111010.1038/s41598-021-97415-zTaxonomic bias in AMP prediction of invertebrate peptidesZoltán Rádai0Johanna Kiss1Nikoletta A. Nagy2Lendület Seed Ecology Research Group, Institute of Ecology and Botany, Centre for Ecological ResearchMTA-DE Behavioural Ecology Research Group, Department of Evolutionary Zoology and Human Biology, University of DebrecenDepartment of Metagenomics, University of DebrecenAbstract Invertebrate antimicrobial peptides (AMPs) are at the forefront in the search for agents of therapeutic utility against multi-resistant microbial pathogens, and in recent years substantial advances took place in the in silico prediction of antimicrobial function of amino acid sequences. A yet neglected aspect is taxonomic bias in the performance of these tools. Owing to differences in the prediction algorithms and used training data sets between tools, and phylogenetic differences in sequence diversity, physicochemical properties and evolved biological functions of AMPs between taxa, notable discrepancies may exist in performance between the currently available prediction tools. Here we tested if there is taxonomic bias in the prediction power in 10 tools with a total of 20 prediction algorithms in 19 invertebrate taxa, using a data set containing 1525 AMP and 3050 non-AMP sequences. We found that most of the tools exhibited considerable variation in performance between tested invertebrate groups. Based on the per-taxa performances and on the variation in performances across taxa we provide guidance in choosing the best-performing prediction tool for all assessed taxa, by listing the highest scoring tool for each of them.https://doi.org/10.1038/s41598-021-97415-z |
| spellingShingle | Zoltán Rádai Johanna Kiss Nikoletta A. Nagy Taxonomic bias in AMP prediction of invertebrate peptides Scientific Reports |
| title | Taxonomic bias in AMP prediction of invertebrate peptides |
| title_full | Taxonomic bias in AMP prediction of invertebrate peptides |
| title_fullStr | Taxonomic bias in AMP prediction of invertebrate peptides |
| title_full_unstemmed | Taxonomic bias in AMP prediction of invertebrate peptides |
| title_short | Taxonomic bias in AMP prediction of invertebrate peptides |
| title_sort | taxonomic bias in amp prediction of invertebrate peptides |
| url | https://doi.org/10.1038/s41598-021-97415-z |
| work_keys_str_mv | AT zoltanradai taxonomicbiasinamppredictionofinvertebratepeptides AT johannakiss taxonomicbiasinamppredictionofinvertebratepeptides AT nikolettaanagy taxonomicbiasinamppredictionofinvertebratepeptides |