2323-11-24 デンマーク工科大学(DTU)
◆この詳細なマッピングにより、新たな免疫療法や病気への理解が進むことが期待されています。20年以上にわたる研究と大規模なデータセット、機械学習のフレームワークを組み合わせ、HLA class IIの機能を正確に予測できるようになりました。
<関連情報>
- https://www.dtu.dk/english/news/all-news/scientists-map-the-antigenic-landscape
- https://www.science.org/doi/10.1126/sciadv.adj6367
HLAクラスII抗原提示を全遺伝子座にわたって正確に予測することに成功。 Accurate prediction of HLA class II antigen presentation across all loci using tailored data acquisition and refined machine learning
Jonas B. Nilsson,Saghar Kaabinejadian,Hooman Yari,Michel G. D. Kester,Peter van Balen,William H. Hildebrand,and Morten Nielsen
Science Advances Published:24 Nov 2023
DOI:https://doi.org/10.1126/sciadv.adj6367
Abstract
Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules is crucial for rational development of immunotherapies and vaccines targeting CD4+ T cell activation. So far, most prediction methods for HLA class II antigen presentation have focused on HLA-DR because of limited availability of immunopeptidomics data for HLA-DQ and HLA-DP while not taking into account alternative peptide binding modes. We present an update to the NetMHCIIpan prediction method, which closes the performance gap between all three HLA class II loci. We accomplish this by first integrating large immunopeptidomics datasets describing the HLA class II specificity space across all loci using a refined machine learning framework that accommodates inverted peptide binders. Next, we apply targeted immunopeptidomics assays to generate data that covers additional HLA-DP specificities. The final method, NetMHCIIpan-4.3, achieves high accuracy and molecular coverage across all HLA class II allotypes.
