从蛋白质结构中解码糖化潜力,通过具有多视图反复神经网络的人类糖蛋白进行解码
Benjamin P Kellman1,2,3,4,5, Julien Mariethoz6, Yujie Zhang1
1Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA.
bioRxiv : the preprint server for biology
|May 27, 2024
概括
蛋白质结构会影响N-甘氨酸位点的位置. 一个新的神经网络预测N-糖化事件和甘氨酸结构,进步我们对这个复杂的生物过程的理解.
科学领域:
- 生物化学 生化学
- 计算生物学 计算生物学
- 葡萄糖生物学 葡萄糖生物学
背景情况:
- 作为一个关键的翻译后修改,N-糖基化传统上被视为非模板化.
- 观察到的特定位点的N-甘氨酸模式挑战了无模板生物合成模型.
- 假设蛋白质结构与葡萄糖体的接近性可以解释N-葡萄糖的微异质性.
研究的目的:
- 开发一个N-糖基化事件和糖结构的预测模型.
- 为了研究蛋白质结构在限制糖基化中的作用.
- 创建一个在的工具,用于预测新型序列中的糖化.
主要方法:
- 训练了一种混合神经网络,将循环和图形神经网络结合起来.
- 使用了特定位点的甘油化数据和甘油酸侧翼序列.
- 根据GlyConnect数据库和特定蛋白质示例进行验证的预测.
主要成果:
- 该算法准确地预测了人类N-糖化事件和糖结构.
- 在增强的芳香序列,SARS-CoV-2尖峰和IgG3变体中成功重复了糖化模式.
- 证明能够预测甘氨酸结构和丰富性的能力.
结论:
- 蛋白质结构显然限制了N-糖化.
- 开发出来的神经网络能够准确地预测N-糖化.
- 这种工具对未表征的蛋白质和遗传变异有潜在的应用.
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