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Updated: Jan 10, 2026

Subnanometer-resolution Structural Determination of Hemagglutinin from Cryo-electron Tomography of Influenza Viruses

Subnanometer-resolution Structural Determination of Hemagglutinin from Cryo-electron Tomography of Influenza Viruses

Published on: November 7, 2025

使用梯度增强和人工神经网络 (ANN) 模型识别流感A (H1N1) 病毒基因组上的蛋白质-蛋白质相互作用 (PPI) 位点

Vidhi Sajnani¹, Omer Ali¹, Satarupa Das¹

¹Department of Biological Sciences, Birla Institute of Science and Technology, Pilani, Hyderabad Campus, Hyderabad, Telangana 500078, India.

|November 24, 2025

概括

这项研究引入了一种新的计算方法,用于预测流感A病毒 (IAV) 基因组上的蛋白质-蛋白质相互作用 (PPI) 位点. 渐变增强模型在识别病毒PPI位点方面表现出卓越的准确性,为抗新兴病毒的药物设计提供了潜力.

科学领域:

计算生物学和生物信息学
病毒学和分子建模.
机器学习在药物发现中的应用.

背景情况:

蛋白与蛋白相互作用 (PPI) 对于分子识别至关重要,但实验性识别是资源密集的.
现有的计算方法往往忽视了像流感A病毒 (IAV) 这样的快速突变病毒所带来的独特挑战.
IAV基因组的高突变率需要专门的方法来预测其蛋白质相互作用部位.

研究的目的:

开发和验证用于预测特定在IAV基因组上的PPI位点的计算模型.
评估机器学习模型的性能,包括梯度增强和Prot-BERT-ANN,用于IAV PPI站点预测.
研究氨基酸替代对病毒PPI部位的影响及其对药物设计的影响.

主要方法:

基准测试各种机器学习模型,优化类不平衡和积极的未标记 (PU) 学习.
针对不同蛋白家族 (Train-1) 和IAV特有的保护区域 (Train-2) 的培训模型.
对IAV蛋白与宿主因子相互作用的外部验证 (测试-1) 和SARS-CoV-2尖端蛋白 (测试-2).

主要成果:

渐变增强模型,增强了过量采样和PU学习,优于其他模型,在外部IAV和SARS-CoV-2数据集上实现了更高的回忆.
梯度增强模型实现了IAV蛋白质的平均回忆率为0.53 ± 0.04,明显高于D-SCRIPT模型 (0.18 ± 0.19).
该模型在独立的SARS-CoV-2尖端蛋白数据集上显示了55%的准确性,表明了可概括性和可解释性.

结论:

梯度增强模型为预测IAV基因组上的PPI位点提供了强大的和可泛化的方法.
这种方法可以准确地预测病毒蛋白上的PPI位点,包括来自新兴菌株的病毒蛋白,如SARS-CoV-2.
这种方法对识别新型药物标,改善药物结合,以及对病毒感染的现有药物的重新用途充满希望.

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