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超类型聚类在 II 类 MHC-肽结合预测中的应用。

The Utility of Supertype Clustering in Prediction for Class II MHC-Peptide Binding.

机构信息

Department of Bioinformatics, Shantou University Medical College, Shantou 515000, China.

Department of Computer Science, Guangzhou University, Guangzhou 510000, China.

出版信息

Molecules. 2018 Nov 20;23(11):3034. doi: 10.3390/molecules23113034.

DOI:10.3390/molecules23113034
PMID:30463372
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6278554/
Abstract

MOTIVATION

Extensive efforts have been devoted to understanding the antigenic peptides binding to MHC class I and II molecules since they play a fundamental role in controlling immune responses and due their involvement in vaccination, transplantation, and autoimmunity. The genes coding for the MHC molecules are highly polymorphic, and it is difficult to build computational models for MHC molecules with few know binders. On the other hand, previous studies demonstrated that some MHC molecules share overlapping peptide binding repertoires and attempted to group them into supertypes. Herein, we present a framework of the utility of supertype clustering to gain more information about the data to improve the prediction accuracy of class II MHC-peptide binding.

RESULTS

We developed a new method, called superMHC, for class II MHC-peptide binding prediction, including three MHC isotypes of HLA-DR, HLA-DP, and HLA-DQ, by using supertype clustering in conjunction with RLS regression. The supertypes were identified by using a novel repertoire dissimilarity index to quantify the difference in MHC binding specificities. The superMHC method achieves the state-of-the-art performance and is demonstrated to predict binding affinities to a series of MHC molecules with few binders accurately. These results have implications for understanding receptor-ligand interactions involved in MHC-peptide binding.

摘要

动机

由于 MHC 类 I 和 II 分子结合的抗原肽在控制免疫反应中起着至关重要的作用,并且与疫苗接种、移植和自身免疫有关,因此人们投入了大量精力来理解这些抗原肽。编码 MHC 分子的基因高度多态性,并且很难为具有少数已知结合物的 MHC 分子构建计算模型。另一方面,先前的研究表明,一些 MHC 分子共享重叠的肽结合谱,并尝试将它们分组为超型。在此,我们提出了一种利用超型聚类来获取更多关于数据的信息以提高 II 类 MHC-肽结合预测准确性的框架。

结果

我们开发了一种新的方法,称为 superMHC,用于通过使用超型聚类结合 RLS 回归来预测 II 类 MHC-肽结合,包括 HLA-DR、HLA-DP 和 HLA-DQ 三种 MHC 同型物。通过使用新的库特异性差异指数来量化 MHC 结合特异性的差异来识别超型。superMHC 方法实现了最先进的性能,并被证明可以准确地预测一系列具有少数结合物的 MHC 分子的结合亲和力。这些结果对于理解涉及 MHC-肽结合的受体-配体相互作用具有重要意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce5b/6278554/5b71768a6ca9/molecules-23-03034-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce5b/6278554/e167d64590b0/molecules-23-03034-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce5b/6278554/b27efc2c7458/molecules-23-03034-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce5b/6278554/0564710e0684/molecules-23-03034-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce5b/6278554/c605df3e874e/molecules-23-03034-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce5b/6278554/5b71768a6ca9/molecules-23-03034-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce5b/6278554/e167d64590b0/molecules-23-03034-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce5b/6278554/b27efc2c7458/molecules-23-03034-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce5b/6278554/0564710e0684/molecules-23-03034-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce5b/6278554/c605df3e874e/molecules-23-03034-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce5b/6278554/5b71768a6ca9/molecules-23-03034-g005.jpg

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