Improved prediction of MHC-peptide binding using protein language models.
Improved prediction of MHC-peptide binding using protein language models.
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DOI：
10.3389/fbinf.2023.1207380
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Kozakov, Dima
中科院分区：
文献类型：
--
作者：
Hashemi, Nasser;Hao, Boran;Ignatov, Mikhail;Paschalidis, Ioannis Ch;Vakili, Pirooz;Vajda, Sandor;Kozakov, Dima
主要组织相容性复合物 I 类 (MHC-I) 分子与细胞内抗原衍生的肽结合，并将其呈现在细胞表面，使免疫系统（T 细胞）能够检测到它们。阐明这种表达的过程对于细胞免疫系统的调节和潜在操纵至关重要。预测给定的肽是否与 MHC 分子结合是上述过程中的重要一步，并促使引入许多计算方法来解决这个问题。 NetMHCPan 是一种用于预测肽与任何 MHC 分子结合的泛特异性模型，是最广泛使用的方法之一，专注于使用浅层神经网络解决此二元分类问题。深度学习（DL）方法，特别是自然语言处理（基于 NLP）预训练模型在各种应用（包括蛋白质结构测定）中最近取得的成功成果，促使我们探索它们在这个问题中的应用。具体来说，我们考虑应用在大型蛋白质序列数据集上预训练的深度学习模型来预测 MHC I 类肽结合。使用该领域的标准性能指标以及相同的训练和测试集，我们表明我们的模型优于目前被认为是最先进的 NetMHCpan4.1。
Major histocompatibility complex Class I (MHC-I) molecules bind to peptides derived from intracellular antigens and present them on the surface of cells, allowing the immune system (T cells) to detect them. Elucidating the process of this presentation is essential for regulation and potential manipulation of the cellular immune system. Predicting whether a given peptide binds to an MHC molecule is an important step in the above process and has motivated the introduction of many computational approaches to address this problem. NetMHCPan, a pan-specific model for predicting binding of peptides to any MHC molecule, is one of the most widely used methods which focuses on solving this binary classification problem using shallow neural networks. The recent successful results of Deep Learning (DL) methods, especially Natural Language Processing (NLP-based) pretrained models in various applications, including protein structure determination, motivated us to explore their use in this problem. Specifically, we consider the application of deep learning models pretrained on large datasets of protein sequences to predict MHC Class I-peptide binding. Using the standard performance metrics in this area, and the same training and test sets, we show that our models outperform NetMHCpan4.1, currently considered as the-state-of-the-art.
影响因子：
2.9
作者：
Egbert M;Ghani U;Ashizawa R;Kotelnikov S;Nguyen T;Desta I;Hashemi N;Padhorny D;Kozakov D;Vajda S
通讯作者：
Vajda S