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Proteins undergo chemical modifications that trigger changes in the charge, structure, and conformation of the proteins. Phosphorylation, acetylation, glycosylation, nitrosylation, ubiquitination, lipidation, methylation, and proteolysis are various protein modifications that regulate protein activity. Such modifications are usually enzyme-driven.
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Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays
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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.

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|December 3, 2025
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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.

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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.