通过对比性学习增强激酶抑制剂活性和选择性预测
Yanan Tian1,2, Ruiqiang Lu1, Xiaoqing Gong1
1Faculty of Applied Sciences, Macao Polytechnic University, Macao, China.
Nature communications
|December 3, 2025
概括
MMCLKin使用新型深度学习框架准确预测激酶抑制剂活性和选择性. 该工具有助于发现强效和选择性激酶抑制剂,克服药物开发中的挑战.
科学领域:
- 生物化学 生化学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 由于保留的蛋白质结构和昂贵的查,开发选择性激酶抑制剂是很困难的.
- 准确预测酶抑制剂的亲和力和特异性对于有效的药物开发至关重要.
研究的目的:
- 介绍MMCLKin,用于预测激酶抑制剂活性和选择性的深度学习框架.
- 与现有方法相比,证明MMCLKin的优越性能和通用性.
主要方法:
- 开发了MMCLKin,这是一个以注意力一致性为指导的对比学习框架.
- 集成的几何图形和序列网络,具有多头注意力和多模式,多尺度的对比学习.
- 在多个3D激酶药物,蛋白质药物和突变感知数据集上验证了MMCLKin.
主要成果:
- 在各种数据集中,MMCLKin的表现优于现有的方法.
- 该框架在已知和未知的激酶结构上表现出强大的概括性.
- 注意力分析确定了酶抑制剂结合的关键残留物和功能组.
- 实验验证证了MMCLKin识别强效抑制剂的能力,包括对抗LRRK2 G2019S突变.
结论:
- MMCLKin提供了对激酶抑制剂相互作用的准确和可解释的预测.
- 该框架有效地选强效和选择性激酶抑制剂.
- MMCLKin是促进酶抑制剂药物发现的宝贵工具.
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