原子:学习分子间相互作用的普遍表征.
bioRxiv : the preprint server for biology
|April 28, 2025
概括
ATOMICA是一种新的几何深度学习模型,可以学习各种生物分子之间的分子相互作用. 它系统地注释着暗色蛋白质组,并通过模拟原子尺度接口来识别疾病途径.
科学领域:
- 计算生物学 计算生物学
- 生物物理学的生物物理.
- 机器学习 机器学习
背景情况:
- 分子相互作用是生物过程的基础.
- 当前的机器学习模型往往孤立分子或专注于单一的相互作用类型,限制了概括.
- 需要一种系统的方法来建模各种生物分子相互作用.
研究的目的:
- 介绍ATOMICA,一个几何深度学习模型,用于学习分子间接口的原子尺度表示.
- 能够在各种生物分子模式 (小分子,离子,氨基酸,核酸) 中进行概括.
- 系统地注释分子相互作用和黑暗蛋白质组.
主要方法:
- 开发了ATOMICA,这是一款使用自主监督的清除和掩盖目标的几何深度学习模型.
- 在超过200万个相互作用复合体上受过训练,以产生层次嵌入 (原子,化学块,接口).
- 应用了ATOMICA来构建五个模式特定接口网络 (ATOMICA网络).
主要成果:
- 原子在分子类之间进行概括,在没有监督的情况下恢复共享的物理化学特征.
- 该模型的潜伏空间捕捉了跨交互类型的相似性,并随着数据的增加而得到改善.
- 构建的ATOMICA网络基于相互作用相似性连接蛋白质,识别疾病途径并预测与疾病相关的蛋白质.
- 在暗色蛋白质组中,有2,646个未表征的联结位点,包括潜在的指图案.
结论:
- 原子提供了一个统一的框架,用于学习和代表跨多种生物分子的分子际接口.
- 该模型促进了分子相互作用的系统注释,有助于疾病途径的识别和暗蛋白质体的表征.
- ATOMICA促进了对分子相互作用及其在生物过程和疾病中的作用的理解.
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