Deep learning for predicting antigen presentation
Abstract/Contents
- Abstract
- The presentation of antigens by Human Leukocyte Antigen (HLA) class II molecules is an essential component of adaptive immune responses. Accurate prediction of which antigen fragments are likely to be presented by class II molecules will be of great value for vaccine development and cancer immunotherapies. However, current computational methods trained on in vitro binding data have limitations for accurately predicting class II antigen presentation. Here, we describe MARIA (MHC Analysis with Recurrent Integrated Architecture), a multimodal recurrent neural network for predicting the likelihood of presentation for a given candidate peptide. In addition to in vitro binding measurements, MARIA is trained on peptide HLA ligand sequences identified by mass spectrometry, antigen gene expression levels, and protease cleavage signatures. By integrating these diverse features and leveraging both improved training data and a novel machine learning framework, MARIA (AUC=0.89-0.92) outperformed existing methods in validation data sets and allowed identification of immunogenic epitopes in lymphomas and celiac disease. Across several independent early phase cancer vaccine studies, peptides with high MARIA scores were more likely to elicit strong CD4 T-cell responses. In summary, MARIA improves identification of human peptide immunogens for diverse applications and can be accessed at https://maria.stanford.edu
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Chen, Binbin |
|---|---|
| Degree supervisor | Altman, Russ |
| Thesis advisor | Altman, Russ |
| Thesis advisor | Alizadeh, Ash |
| Thesis advisor | Fire, Andrew Zachary |
| Thesis advisor | Fordyce, Polly |
| Degree committee member | Alizadeh, Ash |
| Degree committee member | Fire, Andrew Zachary |
| Degree committee member | Fordyce, Polly |
| Associated with | Stanford University, Department of Genetics. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Binbin Chen |
|---|---|
| Note | Submitted to the Department of Genetics |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Binbin Chen
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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