干旱区生态环境知识资源中心(Arid): Graph-pMHC: graph neural network approach to MHC class II peptide presentation and antibody immunogenicity

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DOI	10.1093/bib/bbae123
	Graph-pMHC: graph neural network approach to MHC class II peptide presentation and antibody immunogenicity
	Thrift, William John; Perera, Jason; Cohen, Sivan; Lounsbury, Nicolas W.; Gurung, Hem R.; Rose, Christopher M.; Chen, Jieming; Jhunjhunwala, Suchit; Liu, Kai
通讯作者	Liu, K
来源期刊	BRIEFINGS IN BIOINFORMATICS
ISSN	1467-5463
EISSN	1477-4054
出版年	2024
卷号	25 期号:3
英文摘要	Antigen presentation on MHC class II (pMHCII presentation) plays an essential role in the adaptive immune response to extracellular pathogens and cancerous cells. But it can also reduce the efficacy of large-molecule drugs by triggering an anti-drug response. Significant progress has been made in pMHCII presentation modeling due to the collection of large-scale pMHC mass spectrometry datasets (ligandomes) and advances in machine learning. Here, we develop graph-pMHC, a graph neural network approach to predict pMHCII presentation. We derive adjacency matrices for pMHCII using Alphafold2-multimer and address the peptide-MHC binding groove alignment problem with a simple graph enumeration strategy. We demonstrate that graph-pMHC dramatically outperforms methods with suboptimal inductive biases, such as the multilayer-perceptron-based NetMHCIIpan-4.0 (+20.17% absolute average precision). Finally, we create an antibody drug immunogenicity dataset from clinical trial data and develop a method for measuring anti-antibody immunogenicity risk using pMHCII presentation models. Our model increases receiver operating characteristic curve (ROC)-area under the ROC curve (AUC) by 2.57% compared to just filtering peptides by hits in OASis alone for predicting antibody drug immunogenicity.
英文关键词	graph neural networks pMHC-II anti-drug antibody immunogenicity prediction deep learning
类型	Article
语种	英语
开放获取类型	Green Published, Green Submitted, hybrid
收录类别	SCI-E
WOS记录号	WOS:001193845100007
WOS关键词	ANTIGEN PRESENTATION ; HIGH-THROUGHPUT ; HLA-DP ; PREDICTION ; BINDING
WOS类目	Biochemical Research Methods ; Mathematical & Computational Biology
WOS研究方向	Biochemistry & Molecular Biology ; Mathematical & Computational Biology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403080
推荐引用方式 GB/T 7714	Thrift, William John,Perera, Jason,Cohen, Sivan,et al. Graph-pMHC: graph neural network approach to MHC class II peptide presentation and antibody immunogenicity[J],2024,25(3).
APA	Thrift, William John.,Perera, Jason.,Cohen, Sivan.,Lounsbury, Nicolas W..,Gurung, Hem R..,...&Liu, Kai.(2024).Graph-pMHC: graph neural network approach to MHC class II peptide presentation and antibody immunogenicity.BRIEFINGS IN BIOINFORMATICS,25(3).
MLA	Thrift, William John,et al."Graph-pMHC: graph neural network approach to MHC class II peptide presentation and antibody immunogenicity".BRIEFINGS IN BIOINFORMATICS 25.3(2024).