科學家優化預測HLA呈遞抗原的算法



本期文章:《自然—生物技術》:Online/在線發表

美國斯坦福大學Ash A. Alizadeh研究組通過整合深度學習方法,預測 II類HLA對抗原的呈遞。 該項研究成果在線發表于2019年10月14日的《自然—生物技術》。

研究人員描述了MARIA(具有遞歸集成架構的主要組織相容性復合體分析),一種多模態遞歸神經網絡,用于在許多感興趣的基因中預測特定情況下II類白細胞抗原(HLA)呈遞抗原的可能性等位基因。除了進行體外結合測試外,還利用質譜對MARIA鑒定的HLA結合肽段進行了序列分析,以及抗原基因的表達水平和蛋白酶切割位點的標記。因為它利用了這些多樣化的訓練數據和改進的機器學習框架,所以MARIA(曲線下面積= 0.89–0.92)優于現有的驗證數據集中的方法。

在獨立的癌癥新抗原研究中,具有較高MARIA評分的肽更有可能引起強烈的CD4 + T細胞反應。因此可以利用MARIA鑒定多種癌癥和自身免疫性疾病中的免疫原性表位。

據悉,準確預測人II類白細胞抗原(HLA)對抗原呈遞對于疫苗研發和癌癥免疫治療具有重要價值。目前,體外結合訓練數據的計算方法受到訓練數據不足和算法約束的限制。

附:英文原文

Title: Predicting HLA class II antigen presentation through integrated deep learning

Author: Binbin Chen, Michael S. Khodadoust, Niclas Olsson, Lisa E. Wagar, Ethan Fast, Chih Long Liu, Yagmur Muftuoglu, Brian J. Sworder, Maximilian Diehn, Ronald Levy, Mark M. Davis, Joshua E. Elias, Russ B. Altman, Ash A. Alizadeh

Issue&Volume: 2019-10-14

Abstract: 

Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules would be valuable for vaccine development and cancer immunotherapies. Current computational methods trained on in vitro binding data are limited by insufficient training data and algorithmic constraints. Here we describe MARIA (major histocompatibility complex analysis with recurrent integrated architecture; https://maria.stanford.edu/), a multimodal recurrent neural network for predicting the likelihood of antigen presentation from a gene of interest in the context of specific HLA class II alleles. In addition to in vitro binding measurements, MARIA is trained on peptide HLA ligand sequences identified by mass spectrometry, expression levels of antigen genes and protease cleavage signatures. Because it leverages these diverse training data and our improved machine learning framework, MARIA (area under the curve = 0.89–0.92) outperformed existing methods in validation datasets. Across independent cancer neoantigen studies, peptides with high MARIA scores are more likely to elicit strong CD4+ T cell responses. MARIA allows identification of immunogenic epitopes in diverse cancers and autoimmune disease.

DOI: 10.1038/s41587-019-0280-2

Source: https://www.nature.com/articles/s41587-019-0280-2

期刊信息

Nature Biotechnology:《自然—生物技術》,創刊于1996年。隸屬于施普林格·自然出版集團,最新IF:31.864
官方網址:https://www.nature.com/nbt/
投稿鏈接:https://mts-nbt.nature.com/cgi-bin/main.plex




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