Computational Antigen Discovery for Eukaryotic Pathogens Using Vacceed.

Goodswen, SJ; Kennedy, PJ; Ellis, JT

Computational Antigen Discovery for Eukaryotic Pathogens Using Vacceed.

Goodswen, SJ Kennedy, PJ Ellis, JT

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Publisher:: Springer US
Publication Type:: Journal Article
Citation:: Methods in molecular biology (Clifton, N.J.), 2021, 2183, pp. 29-42
Issue Date:: 2021-01

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Field	Value	Language
dc.contributor.author	Goodswen, SJ
dc.contributor.author	Kennedy, PJ
dc.contributor.author	Ellis, JT
dc.date.accessioned	2021-08-19T04:41:15Z
dc.date.available	2021-08-19T04:41:15Z
dc.date.issued	2021-01
dc.identifier.citation	Methods in molecular biology (Clifton, N.J.), 2021, 2183, pp. 29-42
dc.identifier.isbn	9781071607947
dc.identifier.issn	1064-3745
dc.identifier.issn	1940-6029
dc.identifier.uri	http://hdl.handle.net/10453/150170
dc.description.abstract	Bioinformatics programs have been developed that exploit informative signals encoded within protein sequences to predict protein characteristics. Unfortunately, there is no program as yet that can predict whether a protein will induce a protective immune response to a pathogen. Nonetheless, predicting those pathogen proteins most likely from those least likely to induce an immune response is feasible when collectively using predicted protein characteristics. Vacceed is a computational pipeline that manages different standalone bioinformatics programs to predict various protein characteristics, which offer supporting evidence on whether a protein is secreted or membrane -associated. A set of machine learning algorithms predicts the most likely pathogen proteins to induce an immune response given the supporting evidence. This chapter provides step by step descriptions of how to configure and operate Vacceed for a eukaryotic pathogen of the user's choice.
dc.format	Print
dc.language	eng
dc.publisher	Springer US
dc.relation.ispartof	Methods in molecular biology (Clifton, N.J.)
dc.relation.isbasedon	10.1007/978-1-0716-0795-4_4
dc.rights	The final publication is available at Springer via http://dx.doi.org/ 10.1007/978-1-0716-0795-4_4
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0399 Other Chemical Sciences, 0601 Biochemistry and Cell Biology
dc.subject.classification	Developmental Biology
dc.subject.mesh	Antigens
dc.subject.mesh	Epitope Mapping
dc.subject.mesh	Computational Biology
dc.subject.mesh	Algorithms
dc.subject.mesh	Software
dc.subject.mesh	Host-Pathogen Interactions
dc.subject.mesh	Eukaryota
dc.subject.mesh	Algorithms
dc.subject.mesh	Antigens
dc.subject.mesh	Computational Biology
dc.subject.mesh	Epitope Mapping
dc.subject.mesh	Eukaryota
dc.subject.mesh	Host-Pathogen Interactions
dc.subject.mesh	Software
dc.title	Computational Antigen Discovery for Eukaryotic Pathogens Using Vacceed.
dc.type	Journal Article
utslib.citation.volume	2183
utslib.location.activity	United States
utslib.for	0399 Other Chemical Sciences
utslib.for	0601 Biochemistry and Cell Biology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-01-31T00:00:00+1000Z
dc.date.updated	2021-08-19T04:41:14Z
pubs.publication-status	Published
pubs.volume	2183

Abstract:

Bioinformatics programs have been developed that exploit informative signals encoded within protein sequences to predict protein characteristics. Unfortunately, there is no program as yet that can predict whether a protein will induce a protective immune response to a pathogen. Nonetheless, predicting those pathogen proteins most likely from those least likely to induce an immune response is feasible when collectively using predicted protein characteristics. Vacceed is a computational pipeline that manages different standalone bioinformatics programs to predict various protein characteristics, which offer supporting evidence on whether a protein is secreted or membrane -associated. A set of machine learning algorithms predicts the most likely pathogen proteins to induce an immune response given the supporting evidence. This chapter provides step by step descriptions of how to configure and operate Vacceed for a eukaryotic pathogen of the user's choice.

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http://hdl.handle.net/10453/150170