Academic Journal
A modular protein language modelling approach to immunogenicity prediction.
| Title: | A modular protein language modelling approach to immunogenicity prediction. |
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| Authors: | Hugh O'Brien, Max Salm, Laura T Morton, Maciej Szukszto, Felix O'Farrell, Charlotte Boulton, Laurence King, Supreet Kaur Bola, Pablo D Becker, Andrew Craig, Morten Nielsen, Yardena Samuels, Charles Swanton, Marc R Mansour, Sine Reker Hadrup, Sergio A Quezada |
| Source: | PLoS Computational Biology, Vol 20, Iss 11, p e1012511 (2024) |
| Publisher Information: | Public Library of Science (PLoS), 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Biology (General) |
| Subject Terms: | Biology (General), QH301-705.5 |
| More Details: | Neoantigen immunogenicity prediction is a highly challenging problem in the development of personalised medicines. Low reactivity rates in called neoantigens result in a difficult prediction scenario with limited training datasets. Here we describe ImmugenX, a modular protein language modelling approach to immunogenicity prediction for CD8+ reactive epitopes. ImmugenX comprises of a pMHC encoding module trained on three pMHC prediction tasks, an optional TCR encoding module and a set of context specific immunogenicity prediction head modules. Compared with state-of-the-art models for each task, ImmugenX's encoding module performs comparably or better on pMHC binding affinity, eluted ligand prediction and stability tasks. ImmugenX outperforms all compared models on pMHC immunogenicity prediction (Area under the receiver operating characteristic curve = 0.619, average precision: 0.514), with a 7% increase in average precision compared to the next best model. ImmugenX shows further improved performance on immunogenicity prediction with the integration of TCR context information. ImmugenX performance is further analysed for interpretability, which locates areas of weakness found across existing immunogenicity models and highlight possible biases in public datasets. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1553-734X 1553-7358 |
| Relation: | https://doaj.org/toc/1553-734X; https://doaj.org/toc/1553-7358 |
| DOI: | 10.1371/journal.pcbi.1012511 |
| Access URL: | https://doaj.org/article/d0648697accf4d3db851dc1ee5b36eb3 |
| Accession Number: | edsdoj.0648697accf4d3db851dc1ee5b36eb3 |
| Database: | Directory of Open Access Journals |
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| ISSN: | 1553734X 15537358 |
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| DOI: | 10.1371/journal.pcbi.1012511 |
| Published in: | PLoS Computational Biology |
| Language: | English |