Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance

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dc.contributor.authorChowdhury, Abu Sayeden
dc.contributor.authorReehl, Sarah M.en
dc.contributor.authorKehn-Hall, Kyleneen
dc.contributor.authorBishop, Barney M.en
dc.contributor.authorWebb-Robertson, Bobbie-Jo M.en
dc.contributor.departmentBiomedical Sciences and Pathobiologyen
dc.date.accessioned2021-01-26T18:08:43Zen
dc.date.available2021-01-26T18:08:43Zen
dc.date.issued2020-11-06en
dc.description.abstractThe emergence of viral epidemics throughout the world is of concern due to the scarcity of available effective antiviral therapeutics. The discovery of new antiviral therapies is imperative to address this challenge, and antiviral peptides (AVPs) represent a valuable resource for the development of novel therapies to combat viral infection. We present a new machine learning model to distinguish AVPs from non-AVPs using the most informative features derived from the physicochemical and structural properties of their amino acid sequences. To focus on those features that are most likely to contribute to antiviral performance, we filter potential features based on their importance for classification. These feature selection analyses suggest that secondary structure is the most important peptide sequence feature for predicting AVPs. Our Feature-Informed Reduced Machine Learning for Antiviral Peptide Prediction (FIRM-AVP) approach achieves a higher accuracy than either the model with all features or current state-of-the-art single classifiers. Understanding the features that are associated with AVP activity is a core need to identify and design new AVPs in novel systems. The FIRM-AVP code and standalone software package are available at https://github.com/pmartR/FIRM-AVP with an accompanying web application at https://msc-viz.emsl.pnnl.gov/AVPR.en
dc.description.notesThis work was supported by the U.S. Army Medical Research Acquisition Activity, through the Accelerating Innovation in Military Medicine program under Award No. W81XWH-18-1-0801. Opinions, interpretations, conclusions and recommendations are those of the author and are not necessarily endorsed by the Department of Defense or the U.S. Army. We are grateful to the St. Augustine Alligator Farm Zoological Park for their collaboration on this project. Computational work was completed at Pacific Northwest National Laboratory (PNNL). PNNL is operated by Battelle Memorial Institute for the Department of Energy under contract DEAC05-76RLO1830.en
dc.description.sponsorshipU.S. Army Medical Research Acquisition Activity, through the Accelerating Innovation in Military Medicine program [W81XWH-18-1-0801]; Department of EnergyUnited States Department of Energy (DOE) [DEAC05-76RLO1830]en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1038/s41598-020-76161-8en
dc.identifier.issn2045-2322en
dc.identifier.issue1en
dc.identifier.other19260en
dc.identifier.pmid33159146en
dc.identifier.urihttp://hdl.handle.net/10919/102081en
dc.identifier.volume10en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleBetter understanding and prediction of antiviral peptides through primary and secondary structure feature importanceen
dc.title.serialScientific Reportsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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Scholarly Works, Biomedical Sciences and Pathobiology
Destination Area: Global Systems Science (GSS)