EpiTOP—a proteochemometric tool for MHC class II binding prediction

Ivan Dimitrov; Panayot Garnev; Darren R Flower; Irini Doytchinova

doi:10.1093/bioinformatics/btq324

EpiTOP—a proteochemometric tool for MHC class II binding prediction

Ivan Dimitrov
, Panayot Garnev
, Darren R Flower
, Irini Doytchinova

Medical University of Sofia

Research output: Contribution to journal › Article › peer-review

68 Citations (SciVal)

129 Downloads (Pure)

Abstract

Motivation: T-cell epitope identification is a critical immunoinformatic problem within vaccine design. To be an epitope, a peptide must bind an MHC protein.
Results: Here, we present EpiTOP, the first server predicting MHC class II binding based on proteochemometrics, a QSAR approach for ligands binding to several related proteins. EpiTOP uses a quantitative matrix to predict binding to 12 HLA-DRB1 alleles. It identifies 89% of known epitopes within the top 20% of predicted binders, reducing laboratory labour, materials and time by 80%. EpiTOP is easy to use, gives comprehensive quantitative predictions and will be expanded and updated with new quantitative matrices over time.

Original language	English
Article number	btq324
Pages (from-to)	2066-8
Number of pages	3
Journal	Bioinformatics
Volume	26
Issue number	16
Early online date	23 Jun 2010
DOIs	https://doi.org/10.1093/bioinformatics/btq324
Publication status	Published - 15 Aug 2010

Bibliographical note

This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The definitive publisher-authenticated version Dimitrov, I, Garnev, P, Flower, DR & Doytchinova, I 2010, 'EpiTOP—a proteochemometric tool for MHC class II binding prediction', Bioinformatics (Oxford, England), vol 26, no. 16, pp. 2066-8. is available online at:http://bioinformatics.oxfordjournals.org/content/26/16/2066.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

alleles
T-lymphocyte epitopes
HLA-DR antigens
HLA-DRB1 chains
histocompatibility antigens class II
ligands
peptides
software

Access to Document

10.1093/bioinformatics/btq324

S1 ln79828911501893818 1939656818Hwf 1200343156IdV19489983697982891PDF HI0001
This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The definitive publisher-authenticated version Dimitrov, I, Garnev, P, Flower, DR & Doytchinova, I 2010, 'EpiTOP—a proteochemometric tool for MHC class II binding prediction', Bioinformatics (Oxford, England), vol 26, no. 16, pp. 2066-8. is available online at:http://bioinformatics.oxfordjournals.org/content/26/16/2066.
Accepted author manuscript, 77.7 KB

http://bioinformatics.oxfordjournals.org/content/26/16/2066

Cite this

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title = "EpiTOP—a proteochemometric tool for MHC class II binding prediction",

abstract = "Motivation: T-cell epitope identification is a critical immunoinformatic problem within vaccine design. To be an epitope, a peptide must bind an MHC protein. Results: Here, we present EpiTOP, the first server predicting MHC class II binding based on proteochemometrics, a QSAR approach for ligands binding to several related proteins. EpiTOP uses a quantitative matrix to predict binding to 12 HLA-DRB1 alleles. It identifies 89\% of known epitopes within the top 20\% of predicted binders, reducing laboratory labour, materials and time by 80\%. EpiTOP is easy to use, gives comprehensive quantitative predictions and will be expanded and updated with new quantitative matrices over time. ",

keywords = "alleles, T-lymphocyte epitopes, HLA-DR antigens, HLA-DRB1 chains, histocompatibility antigens class II, ligands, peptides, software",

author = "Ivan Dimitrov and Panayot Garnev and Flower, \{Darren R\} and Irini Doytchinova",

note = "This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The definitive publisher-authenticated version Dimitrov, I, Garnev, P, Flower, DR \& Doytchinova, I 2010, 'EpiTOP—a proteochemometric tool for MHC class II binding prediction', Bioinformatics (Oxford, England), vol 26, no. 16, pp. 2066-8. is available online at:http://bioinformatics.oxfordjournals.org/content/26/16/2066.",

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AB - Motivation: T-cell epitope identification is a critical immunoinformatic problem within vaccine design. To be an epitope, a peptide must bind an MHC protein. Results: Here, we present EpiTOP, the first server predicting MHC class II binding based on proteochemometrics, a QSAR approach for ligands binding to several related proteins. EpiTOP uses a quantitative matrix to predict binding to 12 HLA-DRB1 alleles. It identifies 89% of known epitopes within the top 20% of predicted binders, reducing laboratory labour, materials and time by 80%. EpiTOP is easy to use, gives comprehensive quantitative predictions and will be expanded and updated with new quantitative matrices over time.

KW - alleles

KW - T-lymphocyte epitopes

KW - HLA-DR antigens

KW - HLA-DRB1 chains

KW - histocompatibility antigens class II

KW - ligands

KW - peptides

KW - software

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DO - 10.1093/bioinformatics/btq324

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ER -

EpiTOP—a proteochemometric tool for MHC class II binding prediction

Abstract

Bibliographical note

UN SDGs

Keywords

Access to Document

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