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The Impact of peptide flanking residues on predicting peptide-MHC-II binding interactions using convolutional Neural Networks
Thesis (MSc)--Stellenbosch University, 2022.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2022
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| _version_ | 1867614016501710848 |
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| access_status_str | Open Access |
| author | Daumas, Tshenolo Thato Eustacia |
| author2 | Bah, Bubacarr |
| author_browse | Bah, Bubacarr Daumas, Tshenolo Thato Eustacia |
| author_facet | Bah, Bubacarr Daumas, Tshenolo Thato Eustacia |
| author_sort | Daumas, Tshenolo Thato Eustacia |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MSc)--Stellenbosch University, 2022. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/124973 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:45:20.375Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/124973 The Impact of peptide flanking residues on predicting peptide-MHC-II binding interactions using convolutional Neural Networks Daumas, Tshenolo Thato Eustacia Bah, Bubacarr Degoot, Abdoelnaser M. Ndifon, Wilfred Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences (Applied Mathematics) Peptide-MHC-II UCTD Peptides Neural networks (Computer science) Major histocompatibility complex Histocompatibility antigens -- Binding Thesis (MSc)--Stellenbosch University, 2022. ENGLISH ABSTRACT: Major histocompatibility complex class II (MHC-II) is one of three classes of MHC molecules and is located on the surface of professional antigen presenting cells. MHC-II molecules present antigenic peptides derived from pathogens that cause infection, for recognition by CD4+ T lymphocytes. MHC-II molecules are critical components of the chain of intercellular interactions required for the adaptive im- mune response to be launched successfully, as this chain is thought to begin with the binding of antigenic peptides by MHC-II molecules. While considerable progress in computational efforts have been made towards un- derstanding peptide-MHC interactions for classes I and II, the case for peptide- MHC-II remains challenging due to MHC-II molecules being highly polymorphic and having open-ended binding grooves. Consequently, MHC-II molecules interact with peptides of varying lengths; therefore, the role that peptide flanking residues (PFRs) play in peptide-MHC-II binding interactions must be considered. We pro- pose an allele-specific convolutional neural network model that simulates binding interactions between peptides and MHC-II molecules that also incorporates PFR information in the input. Deep learning models for peptide-MHC-II interactions that have been published, such as the allele-specific model, NetMHCII and the transallelic model NetMHCI- Ipan have demonstrated encouraging predictive performance. When compared, our proposed CNN model outperformed the latest version of the model, NetMHCII-2.3 across all MHC-II alleles considered with mean AUC value of 0.951 as compared with 0.822 for NetMHCII-3.2. Furthermore, we analysed the impact that PFRs have on modelling peptide-MHC-II binding interactions and laid the foundations of de- veloping a transallelic model based on the CNN model. AFRIKAANSE OPSOMMING: Groot histoversoenbaarheidskompleks klas II (MHC-II) is een van drie klasse van MHC molekules en is geleë op die oppervlak van professionele antigeen-presenterende selle. MHC-II molekules bied antigeniese peptiede aan wat afkomstig is van pato- gene wat infeksie veroorsaak, vir herkenning deur CD4+ T limfosiete. MHC-II molekules is kritieke komponente van die ketting van intersellulêre interaksies wat nodig is vir die aanpasbare immuunrespons om suksesvol van stapel te stuur, aan- gesien hierdie ketting vermoedelik begin met die binding van antigeniese peptiede deur MHC-II molekules. Terwyl aansienlike vordering in berekeningspogings gemaak is om peptied-MHC interaksies vir klasse I en II te verstaan, bly die saak vir peptied-MHC-II uitdagend as gevolg van MHC-II-molekules wat hoogs polimorf is en oop-einde bindings- groewe het. Gevolglik, MHC-II molekules interaksie met peptiede van verskil- lende lengtes; daarom, moet die rol wat peptied flankerende residue (PFRs) speel in peptied-MHC-II bindende interaksies oorweeg word. Ons stel ’n alleel-spesifieke konvolusionele neurale netwerk model voor wat bindingsinteraksies tussen peptiede en MHC-II molekules simuleer wat ook PFR-inligting in die inset inkorporeer. Diep leer modelle vir peptied-MHC-II interaksies wat gepubliseer is, soos die al- leelspesifieke model, NetMHCII en die transalleliese model, NetMHCIIpan het be- moedigende voorspellende prestasie getoon. As dit vergelyk word, het ons voorge-stelde CNN-model beter gevaar as die nuutste weergawe van die model, NetMHCII- 2.3 oor alle MHC-II allele wat oorweeg word met gemiddelde AUC waarde van 0,951 in vergelyking met 0,822 vir NetMHCII-3.2. Verder het ons die impak wat PFRe het op die modellering van peptied-MHC-II bindingsinteraksies ontleed en die grondslag gelê van die ontwikkeling van ’n transalleliese model gebaseer op die CNN-model. Masters 2022-03-11T14:39:59Z 2022-04-29T09:44:19Z 2022-03-11T14:39:59Z 2022-04-29T09:44:19Z 2022-04 Thesis http://hdl.handle.net/10019.1/124973 en_ZA Stellenbosch University 80 pages application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Peptide-MHC-II UCTD Peptides Neural networks (Computer science) Major histocompatibility complex Histocompatibility antigens -- Binding Daumas, Tshenolo Thato Eustacia The Impact of peptide flanking residues on predicting peptide-MHC-II binding interactions using convolutional Neural Networks |
| title | The Impact of peptide flanking residues on predicting peptide-MHC-II binding interactions using convolutional Neural Networks |
| title_full | The Impact of peptide flanking residues on predicting peptide-MHC-II binding interactions using convolutional Neural Networks |
| title_fullStr | The Impact of peptide flanking residues on predicting peptide-MHC-II binding interactions using convolutional Neural Networks |
| title_full_unstemmed | The Impact of peptide flanking residues on predicting peptide-MHC-II binding interactions using convolutional Neural Networks |
| title_short | The Impact of peptide flanking residues on predicting peptide-MHC-II binding interactions using convolutional Neural Networks |
| title_sort | impact of peptide flanking residues on predicting peptide mhc ii binding interactions using convolutional neural networks |
| topic | Peptide-MHC-II UCTD Peptides Neural networks (Computer science) Major histocompatibility complex Histocompatibility antigens -- Binding |
| url | http://hdl.handle.net/10019.1/124973 |
| work_keys_str_mv | AT daumastshenolothatoeustacia theimpactofpeptideflankingresiduesonpredictingpeptidemhciibindinginteractionsusingconvolutionalneuralnetworks AT daumastshenolothatoeustacia impactofpeptideflankingresiduesonpredictingpeptidemhciibindinginteractionsusingconvolutionalneuralnetworks |