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AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest
Bhadra, Pratiti; Yan, Jielu; Li, Jinyan; Fong, Simon; Siu, Shirley W. I.
2018-01-26
Source PublicationSCIENTIFIC REPORTS
ISSN2045-2322
Volume8
AbstractAntimicrobial peptides (AMPs) are promising candidates in the fight against multidrug-resistant pathogens owing to AMPs' broad range of activities and low toxicity. Nonetheless, identification of AMPs through wet-lab experiments is still expensive and time consuming. Here, we propose an accurate computational method for AMP prediction by the random forest algorithm. The prediction model is based on the distribution patterns of amino acid properties along the sequence. Using our collection of large and diverse sets of AMP and non-AMP data (3268 and 166791 sequences, respectively), we evaluated 19 random forest classifiers with different positive: negative data ratios by 10-fold cross-validation. Our optimal model, AmPEP with the 1:3 data ratio, showed high accuracy (96%), Matthew's correlation coefficient (MCC) of 0.9, area under the receiver operating characteristic curve (AUC-ROC) of 0.99, and the Kappa statistic of 0.9. Descriptor analysis of AMP/non-AMP distributions by means of Pearson correlation coefficients revealed that reduced feature sets (from a full-featured set of 105 to a minimal-feature set of 23) can result in comparable performance in all respects except for some reductions in precision. Furthermore, AmPEP outperformed existing methods in terms of accuracy, MCC, and AUC-ROC when tested on benchmark datasets.
DOI10.1038/s41598-018-19752-w
URLView the original
Indexed BySCI
Language英语
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000423428300025
PublisherNATURE PUBLISHING GROUP
The Source to ArticleWOS
Fulltext Access
Citation statistics
Cited Times [WOS]:15   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Recommended Citation
GB/T 7714
Bhadra, Pratiti,Yan, Jielu,Li, Jinyan,et al. AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest[J]. SCIENTIFIC REPORTS,2018,8.
APA Bhadra, Pratiti,Yan, Jielu,Li, Jinyan,Fong, Simon,&Siu, Shirley W. I..(2018).AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest.SCIENTIFIC REPORTS,8.
MLA Bhadra, Pratiti,et al."AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest".SCIENTIFIC REPORTS 8(2018).
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