SAPP:结构意识到PTM预测
Yujin Choo1, Seungjin Na2, Eunok Paek3
1Department of Artificial Intelligence, Hanyang University, Seoul, 04763, Republic of Korea.
Computers in biology and medicine
|October 9, 2025
概括
我们开发了SAPP,这是一个新的结构意识模型,用于预测翻译后修改站点. 通过将蛋白质结构与序列数据集成,SAPP提高了细胞机制的预测准确性和生物相关性.
科学领域:
- 生物化学 生化学
- 计算生物学 计算生物学
- 结构生物学 结构生物学
背景情况:
- 翻译后修饰 (PTMs) 对于调节各种细胞过程至关重要,包括信号传导,细胞生长和分化.
- 准确识别PTM部位对于理解细胞机制和开发向疗法的必要.
- 现有的计算模型经常忽视结构上下文,如内在无序区域和溶剂可访问性,限制预测准确性.
研究的目的:
- 引入SAPP (结构意识的PTM预测),这是一个用于PTM站点预测的新计算框架.
- 将蛋白质结构信息与序列数据集成,以改善预测.
- 证明基于结构的模型在PTM预测中的实用性,包括对有限数据的PTM类型的概括.
主要方法:
- 开发了SAPP,这是一个基于变压器的模型,利用自我注意和交叉注意机制.
- 综合结构特征来自AlphaFold2预测与序列信息.
- 使用酸化预训练模型对其他PTM类型进行微调.
主要成果:
- SAPP有效地捕捉了蛋白质序列及其结构状态之间的复杂相互作用.
- 与仅依据序列的模型相比,结构意识的方法显著提高了PTM预测准确性和生物相关性.
- 对于具有有限培训数据的PTM类型,SAPP表现出强大的泛化性能.
结论:
- 结构信息对于准确的PTM站点预测至关重要.
- SAPP代表了基于结构的PTM预测的开创性框架,推动了该领域的发展.
- 这项工作加深了对PTMs生物意义的理解,并为治疗开发开辟了道路.
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