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可解释的 PROTAC 降解预测与结构信息的深度第三级注意力框架

Zhenglu Chen¹, Chunbin Gu², Shuoyan Tan³

¹School of Pharmacy, Lanzhou University, Lanzhou, 730000, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|September 30, 2025

概括

这项研究介绍了PROTAC-STAN,这是一个深度学习框架,用于预测蛋白质溶解向奇美拉 (PROTAC) 降解. 它通过整合分子结构和注意力机制来提高准确性和可解释性,加速药物发现.

科学领域:

生物化学生物化学
计算化学计算化学
药物发现药物发现药物发现

背景情况:

化向化体 (PROTACs) 提供了一种新的方法来降解致病蛋白质,包括那些以前被认为是"不可抗药"的蛋白质.
目前的PROTAC开发主要依赖于广泛的湿实验室实验,这些实验是昂贵和耗时的.
现有的用于PROTAC降解预测的深度学习模型经常忽视关键的层次分子表示和蛋白质结构数据,限制了它们的预测能力和可解释性.

研究的目的:

开发一个可解释的深度学习框架,PROTAC-STAN,用于准确预测PROTAC诱导的蛋白质降解.
通过结合层次分子特征和蛋白质结构信息来解决现有方法的局限性.
通过基于注意力的机制,提供有关PROTAC疗效的分子相互作用的见解.

主要方法:

开发了PROTAC-STAN,一个结构信息深层三元注意网络 (STAN).
使用原子,分子和属性等级结构来表示PROTAC分子.
综合蛋白质结构数据的蛋白质的兴趣 (POI) 和E3联结酶使用蛋白质语言模型.
利用一种新的三元注意网络,在子结构层面上模拟 PROTAC 组件和目标蛋白之间的相互作用.

主要成果:

与最先进的基线方法相比,PROTAC-STAN在多个绩效指标上取得了超过10%的改善.
该框架提供了显著的可解释性,可视化了原子和残留层面的相互作用.
案例研究和探索性评估证实了PROTAC-STAN的实用性和稳定性.

结论:

PROTAC-STAN提供了一种强大且可解释的深度学习方法,用于预测 PROTAC 退化.
该模型能够整合结构信息并提供机械洞察力的能力加速了PROTAC的研究和开发.
预计PROTAC-STAN将成为推进向蛋白质降解领域的基础工具.

关键词:

深度学习是一种深度学习.可以解释的解释性.分子动力学模拟模拟蛋白质溶解的目标是木乃伊.

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