模拟电影
表位
免疫原性
计算生物学
班级(哲学)
人类白细胞抗原
计算机科学
抗原处理
抗原
鉴定(生物学)
人工智能
生物
抗原呈递
生物信息学
表位定位
免疫学
UniProt公司
线性表位
主要组织相容性复合体
生成语法
单克隆抗体
抗体
作者
Jiani Ma,Yumeng Zhang,L Zhang,Hui Liu,Jiangning Song
标识
DOI:10.1021/acs.jcim.6c00337
摘要
Accurate identification of human leukocyte antigen (HLA) class I-presented epitopes is essential for developing personalized vaccines and immunotherapies. Here, we present HLABrew, a unified deep learning framework for HLA class I epitope prediction and allele-specific mimotope design. By integrating the Transformer, variational autoencoder, and dual cross-attention, HLABrew achieves state-of-the-art performance with hierarchical and allele-aware representations. HLABrew accurately deconvolves multiallelic immunopeptidomics data to recover allele-specific binding motifs and expand epitope coverage. Leveraging its generative capacity, HLABrew further designs mimotopes guided by learned peptide-HLA binding preferences, with the potential to enhance antigen presentation and provide candidates for downstream immunogenicity evaluation.
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