Pan-specific prediction of peptide-MHC Class I complex stability, a correlate of T cell immunogenicity

Michael Rasmussen; Emilio Fenoy; Mikkel Harndahl; Anne Bregnballe Kristensen; Ida Kallehauge Nielsen; Morten Milek Nielsen; Søren Buus

doi:10.4049/jimmunol.1600582

Pan-specific prediction of peptide-MHC Class I complex stability, a correlate of T cell immunogenicity

Michael Rasmussen, Emilio Fenoy, Mikkel Harndahl, Anne Bregnballe Kristensen, Ida Kallehauge Nielsen, Morten Milek Nielsen, Søren Buus^*

^*Corresponding author for this work

59 Citations (Scopus)

Abstract

Binding of peptides to MHC class I (MHC-I) molecules is the most selective event in the processing and presentation of Ags to CTL, and insights into the mechanisms that govern peptide-MHC-I binding should facilitate our understanding of CTL biology. Peptide-MHC-I interactions have traditionally been quantified by the strength of the interaction, that is, the binding affinity, yet it has been shown that the stability of the peptide-MHC-I complex is a better correlate of immunogenicity compared with binding affinity. In this study, we have experimentally analyzed peptide-MHC-I complex stability of a large panel of human MHC-I allotypes and generated a body of data sufficient to develop a neural network-based pan-specific predictor of peptide-MHC-I complex stability. Integrating the neural network predictors of peptide-MHC-I complex stability with state-of-the-art predictors of peptide-MHC-I binding is shown to significantly improve the prediction of CTL epitopes. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCstabpan.

Original language	English
Journal	Journal of Immunology
Volume	197
Issue number	4
Pages (from-to)	1517-1524
Number of pages	8
ISSN	0022-1767
DOIs	https://doi.org/10.4049/jimmunol.1600582
Publication status	Published - 15 Aug 2016

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title = "Pan-specific prediction of peptide-MHC Class I complex stability, a correlate of T cell immunogenicity",

abstract = "Binding of peptides to MHC class I (MHC-I) molecules is the most selective event in the processing and presentation of Ags to CTL, and insights into the mechanisms that govern peptide-MHC-I binding should facilitate our understanding of CTL biology. Peptide-MHC-I interactions have traditionally been quantified by the strength of the interaction, that is, the binding affinity, yet it has been shown that the stability of the peptide-MHC-I complex is a better correlate of immunogenicity compared with binding affinity. In this study, we have experimentally analyzed peptide-MHC-I complex stability of a large panel of human MHC-I allotypes and generated a body of data sufficient to develop a neural network-based pan-specific predictor of peptide-MHC-I complex stability. Integrating the neural network predictors of peptide-MHC-I complex stability with state-of-the-art predictors of peptide-MHC-I binding is shown to significantly improve the prediction of CTL epitopes. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCstabpan.",

author = "Michael Rasmussen and Emilio Fenoy and Mikkel Harndahl and Kristensen, {Anne Bregnballe} and Nielsen, {Ida Kallehauge} and Nielsen, {Morten Milek} and S{\o}ren Buus",

year = "2016",

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doi = "10.4049/jimmunol.1600582",

language = "English",

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T1 - Pan-specific prediction of peptide-MHC Class I complex stability, a correlate of T cell immunogenicity

AU - Rasmussen, Michael

AU - Fenoy, Emilio

AU - Harndahl, Mikkel

AU - Kristensen, Anne Bregnballe

AU - Nielsen, Ida Kallehauge

AU - Nielsen, Morten Milek

AU - Buus, Søren

PY - 2016/8/15

Y1 - 2016/8/15

N2 - Binding of peptides to MHC class I (MHC-I) molecules is the most selective event in the processing and presentation of Ags to CTL, and insights into the mechanisms that govern peptide-MHC-I binding should facilitate our understanding of CTL biology. Peptide-MHC-I interactions have traditionally been quantified by the strength of the interaction, that is, the binding affinity, yet it has been shown that the stability of the peptide-MHC-I complex is a better correlate of immunogenicity compared with binding affinity. In this study, we have experimentally analyzed peptide-MHC-I complex stability of a large panel of human MHC-I allotypes and generated a body of data sufficient to develop a neural network-based pan-specific predictor of peptide-MHC-I complex stability. Integrating the neural network predictors of peptide-MHC-I complex stability with state-of-the-art predictors of peptide-MHC-I binding is shown to significantly improve the prediction of CTL epitopes. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCstabpan.

AB - Binding of peptides to MHC class I (MHC-I) molecules is the most selective event in the processing and presentation of Ags to CTL, and insights into the mechanisms that govern peptide-MHC-I binding should facilitate our understanding of CTL biology. Peptide-MHC-I interactions have traditionally been quantified by the strength of the interaction, that is, the binding affinity, yet it has been shown that the stability of the peptide-MHC-I complex is a better correlate of immunogenicity compared with binding affinity. In this study, we have experimentally analyzed peptide-MHC-I complex stability of a large panel of human MHC-I allotypes and generated a body of data sufficient to develop a neural network-based pan-specific predictor of peptide-MHC-I complex stability. Integrating the neural network predictors of peptide-MHC-I complex stability with state-of-the-art predictors of peptide-MHC-I binding is shown to significantly improve the prediction of CTL epitopes. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCstabpan.

U2 - 10.4049/jimmunol.1600582

DO - 10.4049/jimmunol.1600582

M3 - Journal article

C2 - 27402703

AN - SCOPUS:84983738683

SN - 0022-1767

VL - 197

SP - 1517

EP - 1524

JO - Journal of Immunology

JF - Journal of Immunology

IS - 4

ER -

Pan-specific prediction of peptide-MHC Class I complex stability, a correlate of T cell immunogenicity

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