利用起始网络揭示 MHC 肽结合的静电景观。

Cell systems Pub Date : 2024-04-17 Epub Date: 2024-03-29 DOI:10.1016/j.cels.2024.03.001
Eric Wilson, John Kevin Cava, Diego Chowell, Remya Raja, Kiran K Mangalaparthi, Akhilesh Pandey, Marion Curtis, Karen S Anderson, Abhishek Singharoy
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引用次数: 0

摘要

大分子识别和蛋白质互补的预测模型是生物物理科学的基石之一。然而,分子界面相互作用的组合复杂性往往阻碍了此类模型的建立。这一问题的典型例子是高度多态的主要组织相容性复合体 I 类(MHC-I)分子呈现肽,这是免疫识别的主要组成部分。我们开发了人类白细胞抗原(HLA)-Inception,这是一种深度生物物理卷积神经网络,它整合了分子静电学,捕捉非键式相互作用,用于预测 5,821 个 MHC-I 等位基因的肽结合主题。这些预测生成的主题与实验肽结合和呈现数据密切相关。除了分子相互作用外,该研究还展示了预测基团在分析 MHC-I 等位基因与 HIV 疾病进展和患者对免疫检查点抑制剂的反应之间的关联方面的应用。补充信息中包含了这篇论文透明的同行评审过程记录。
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The electrostatic landscape of MHC-peptide binding revealed using inception networks.

Predictive modeling of macromolecular recognition and protein-protein complementarity represents one of the cornerstones of biophysical sciences. However, such models are often hindered by the combinatorial complexity of interactions at the molecular interfaces. Exemplary of this problem is peptide presentation by the highly polymorphic major histocompatibility complex class I (MHC-I) molecule, a principal component of immune recognition. We developed human leukocyte antigen (HLA)-Inception, a deep biophysical convolutional neural network, which integrates molecular electrostatics to capture non-bonded interactions for predicting peptide binding motifs across 5,821 MHC-I alleles. These predictions of generated motifs correlate strongly with experimental peptide binding and presentation data. Beyond molecular interactions, the study demonstrates the application of predicted motifs in analyzing MHC-I allele associations with HIV disease progression and patient response to immune checkpoint inhibitors. A record of this paper's transparent peer review process is included in the supplemental information.

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