Deep Learning Models for Vaccinology: Predicting T-cell Epitopes in C57BL/6 Mice

Zitian Zhen; Yuhe Wang; Derin B. Keskin; Vladimir Brusic; Lou Chitkushev; Guang Lan Zhang

doi:10.1007/978-3-031-44668-9_14

Deep Learning Models for Vaccinology: Predicting T-cell Epitopes in C57BL/6 Mice

Zitian Zhen
, Yuhe Wang
, Derin B. Keskin
, Vladimir Brusic
, Lou Chitkushev
, Guang Lan Zhang^*

^*Corresponding author for this work

School of Economics

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

The C57 Black 6 (C57BL/6) mice are one the earliest and most widely used inbred laboratory animals in biomedical research and vaccine development. We propose developing a bioinformatics system for the identification of T-cell epitopes in C57BL/6 mice by integrating multiple contributing factors critical to the antigen processing and recognition pathway. The interaction between peptides and MHC molecules is a highly specific step in the antigen processing pathway and T-cell mediated immunity. As the first step of the project, we built a computational tool for predicting MHC class I binding peptides for the C57BL/6 mice. Utilizing deep learning methods, we trained and rigorously validated the prediction models using naturally eluted MHC ligands. The prediction models are of high accuracy.

Original language	English
Title of host publication	Computer Science and Education in Computer Science - 19th EAI International Conference, CSECS 2023, Proceedings
Editors	Tanya Zlateva, Georgi Tuparov
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	182-192
Number of pages	11
ISBN (Print)	9783031446672
DOIs	https://doi.org/10.1007/978-3-031-44668-9_14
Publication status	Published - 2023
Event	19th EAI International Conference on Computer Science and Education in Computer Science, CSECS 2023 - Boston, United States Duration: 28 Jun 2023 → 29 Jun 2023

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	514 LNICST
ISSN (Print)	1867-8211
ISSN (Electronic)	1867-822X

Conference

Conference	19th EAI International Conference on Computer Science and Education in Computer Science, CSECS 2023
Country/Territory	United States
City	Boston
Period	28/06/23 → 29/06/23

UN SDGs

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

SDG 3 Good Health and Well-being

Free Keywords

Bioinformatics System
C57BL/6 Mice
Deep Learning
MHC Binding
Prediction Tool
T-cell Epitope

ASJC Scopus subject areas

Computer Networks and Communications

Access to Document

10.1007/978-3-031-44668-9_14

Cite this

Zhen, Z., Wang, Y., Keskin, D. B., Brusic, V., Chitkushev, L., & Zhang, G. L. (2023). Deep Learning Models for Vaccinology: Predicting T-cell Epitopes in C57BL/6 Mice. In T. Zlateva, & G. Tuparov (Eds.), Computer Science and Education in Computer Science - 19th EAI International Conference, CSECS 2023, Proceedings (pp. 182-192). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 514 LNICST). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-44668-9_14

Zhen, Zitian ; Wang, Yuhe ; Keskin, Derin B. et al. / Deep Learning Models for Vaccinology : Predicting T-cell Epitopes in C57BL/6 Mice. Computer Science and Education in Computer Science - 19th EAI International Conference, CSECS 2023, Proceedings. editor / Tanya Zlateva ; Georgi Tuparov. Springer Science and Business Media Deutschland GmbH, 2023. pp. 182-192 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

@inproceedings{77023cbfbdb44ed88ae14b94a1e791e4,

title = "Deep Learning Models for Vaccinology: Predicting T-cell Epitopes in C57BL/6 Mice",

abstract = "The C57 Black 6 (C57BL/6) mice are one the earliest and most widely used inbred laboratory animals in biomedical research and vaccine development. We propose developing a bioinformatics system for the identification of T-cell epitopes in C57BL/6 mice by integrating multiple contributing factors critical to the antigen processing and recognition pathway. The interaction between peptides and MHC molecules is a highly specific step in the antigen processing pathway and T-cell mediated immunity. As the first step of the project, we built a computational tool for predicting MHC class I binding peptides for the C57BL/6 mice. Utilizing deep learning methods, we trained and rigorously validated the prediction models using naturally eluted MHC ligands. The prediction models are of high accuracy.",

keywords = "Bioinformatics System, C57BL/6 Mice, Deep Learning, MHC Binding, Prediction Tool, T-cell Epitope",

author = "Zitian Zhen and Yuhe Wang and Keskin, \{Derin B.\} and Vladimir Brusic and Lou Chitkushev and Zhang, \{Guang Lan\}",

note = "Publisher Copyright: {\textcopyright} ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2023.; 19th EAI International Conference on Computer Science and Education in Computer Science, CSECS 2023 ; Conference date: 28-06-2023 Through 29-06-2023",

year = "2023",

doi = "10.1007/978-3-031-44668-9\_14",

language = "English",

isbn = "9783031446672",

series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "182--192",

editor = "Tanya Zlateva and Georgi Tuparov",

booktitle = "Computer Science and Education in Computer Science - 19th EAI International Conference, CSECS 2023, Proceedings",

address = "Germany",

}

Zhen, Z, Wang, Y, Keskin, DB, Brusic, V, Chitkushev, L & Zhang, GL 2023, Deep Learning Models for Vaccinology: Predicting T-cell Epitopes in C57BL/6 Mice. in T Zlateva & G Tuparov (eds), Computer Science and Education in Computer Science - 19th EAI International Conference, CSECS 2023, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 514 LNICST, Springer Science and Business Media Deutschland GmbH, pp. 182-192, 19th EAI International Conference on Computer Science and Education in Computer Science, CSECS 2023, Boston, United States, 28/06/23. https://doi.org/10.1007/978-3-031-44668-9_14

Deep Learning Models for Vaccinology: Predicting T-cell Epitopes in C57BL/6 Mice. / Zhen, Zitian; Wang, Yuhe; Keskin, Derin B. et al.
Computer Science and Education in Computer Science - 19th EAI International Conference, CSECS 2023, Proceedings. ed. / Tanya Zlateva; Georgi Tuparov. Springer Science and Business Media Deutschland GmbH, 2023. p. 182-192 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 514 LNICST).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Deep Learning Models for Vaccinology

T2 - 19th EAI International Conference on Computer Science and Education in Computer Science, CSECS 2023

AU - Zhen, Zitian

AU - Wang, Yuhe

AU - Keskin, Derin B.

AU - Brusic, Vladimir

AU - Chitkushev, Lou

AU - Zhang, Guang Lan

N1 - Publisher Copyright: © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2023.

PY - 2023

Y1 - 2023

N2 - The C57 Black 6 (C57BL/6) mice are one the earliest and most widely used inbred laboratory animals in biomedical research and vaccine development. We propose developing a bioinformatics system for the identification of T-cell epitopes in C57BL/6 mice by integrating multiple contributing factors critical to the antigen processing and recognition pathway. The interaction between peptides and MHC molecules is a highly specific step in the antigen processing pathway and T-cell mediated immunity. As the first step of the project, we built a computational tool for predicting MHC class I binding peptides for the C57BL/6 mice. Utilizing deep learning methods, we trained and rigorously validated the prediction models using naturally eluted MHC ligands. The prediction models are of high accuracy.

AB - The C57 Black 6 (C57BL/6) mice are one the earliest and most widely used inbred laboratory animals in biomedical research and vaccine development. We propose developing a bioinformatics system for the identification of T-cell epitopes in C57BL/6 mice by integrating multiple contributing factors critical to the antigen processing and recognition pathway. The interaction between peptides and MHC molecules is a highly specific step in the antigen processing pathway and T-cell mediated immunity. As the first step of the project, we built a computational tool for predicting MHC class I binding peptides for the C57BL/6 mice. Utilizing deep learning methods, we trained and rigorously validated the prediction models using naturally eluted MHC ligands. The prediction models are of high accuracy.

KW - Bioinformatics System

KW - C57BL/6 Mice

KW - Deep Learning

KW - MHC Binding

KW - Prediction Tool

KW - T-cell Epitope

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U2 - 10.1007/978-3-031-44668-9_14

DO - 10.1007/978-3-031-44668-9_14

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AN - SCOPUS:85175803004

SN - 9783031446672

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

SP - 182

EP - 192

BT - Computer Science and Education in Computer Science - 19th EAI International Conference, CSECS 2023, Proceedings

A2 - Zlateva, Tanya

A2 - Tuparov, Georgi

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 28 June 2023 through 29 June 2023

ER -

Zhen Z, Wang Y, Keskin DB, Brusic V, Chitkushev L, Zhang GL. Deep Learning Models for Vaccinology: Predicting T-cell Epitopes in C57BL/6 Mice. In Zlateva T, Tuparov G, editors, Computer Science and Education in Computer Science - 19th EAI International Conference, CSECS 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 182-192. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-031-44668-9_14

Deep Learning Models for Vaccinology: Predicting T-cell Epitopes in C57BL/6 Mice

Abstract

Publication series

Conference

UN SDGs

Free Keywords

ASJC Scopus subject areas

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