DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction.

IF 4.4 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Wei Qu, Ronghui You, Hiroshi Mamitsuka, Shanfeng Zhu
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引用次数: 0

Abstract

Motivation: Computationally predicting major histocompatibility complex class I (MHC-I) peptide binding affinity is an important problem in immunological bioinformatics, which is also crucial for the identification of neoantigens for personalized therapeutic cancer vaccines. Recent cutting-edge deep learning-based methods for this problem cannot achieve satisfactory performance, especially for non-9-mer peptides. This is because such methods generate the input by simply concatenating the two given sequences: a peptide and (the pseudo sequence of) an MHC class I molecule, which cannot precisely capture the anchor positions of the MHC binding motif for the peptides with variable lengths. We thus developed an anchor position-aware and high-performance deep model, DeepMHCI, with a position-wise gated layer and a residual binding interaction convolution layer. This allows the model to control the information flow in peptides to be aware of anchor positions and model the interactions between peptides and the MHC pseudo (binding) sequence directly with multiple convolutional kernels.

Results: The performance of DeepMHCI has been thoroughly validated by extensive experiments on four benchmark datasets under various settings, such as 5-fold cross-validation, validation with the independent testing set, external HPV vaccine identification, and external CD8+ epitope identification. Experimental results with visualization of binding motifs demonstrate that DeepMHCI outperformed all competing methods, especially on non-9-mer peptides binding prediction.

Availability and implementation: DeepMHCI is publicly available at https://github.com/ZhuLab-Fudan/DeepMHCI.

Abstract Image

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DeepMHCI:一个锚定位置感知的深度相互作用模型,用于准确预测MHC-I肽结合亲和力。
动机:计算预测主要组织相容性复合物I类(MHC-I)肽结合亲和力是免疫学生物信息学中的一个重要问题,这对于鉴定用于个性化治疗性癌症疫苗的新抗原也至关重要。针对这一问题,最近基于深度学习的前沿方法无法获得令人满意的性能,尤其是对于非9-聚体肽。这是因为这种方法通过简单地连接两个给定的序列来产生输入:肽和MHC I类分子的(伪序列),这不能精确地捕捉可变长度肽的MHC结合基序的锚定位置。因此,我们开发了一个锚位置感知和高性能的深度模型DeepMHCI,该模型具有位置门控层和残余结合相互作用卷积层。这允许该模型控制肽中的信息流以了解锚定位置,并直接用多个卷积核对肽和MHC伪(结合)序列之间的相互作用进行建模。结果:DeepMHCI的性能已通过在四个基准数据集上进行的广泛实验在各种设置下得到了彻底验证,如5倍交叉验证、独立测试集验证、外部HPV疫苗鉴定和外部CD8+表位鉴定。结合基序可视化的实验结果表明,DeepMHCI优于所有竞争方法,尤其是在非9-聚体肽结合预测方面。可用性和实施:DeepMHCI可在https://github.com/ZhuLab-Fudan/DeepMHCI.
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来源期刊
Bioinformatics
Bioinformatics 生物-生化研究方法
CiteScore
11.20
自引率
5.20%
发文量
753
审稿时长
2.1 months
期刊介绍: The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.
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