使用人工智能预测的结构,全面探索蛋白质激酶的可用药物的构造空间
Noah B Herrington1, Yan Chak Li2, David Stein1,2
1Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.
PLoS computational biology
|July 24, 2024
概括
像AlphaFold2这样的AI模型可以探索多样化的激酶结构,揭示药物发现的新构造. 然而,完善这些模型对于可靠的治疗开发至关重要.
科学领域:
- 结构生物学是结构生物学.
- 计算化学是一种计算化学.
- 药物发现 药物发现
背景情况:
- 蛋白激酶活性受到DFG和aC-Helix等动机的构造变化的调节.
- 针对激酶的小分子药物表现出选择性和依赖激酶构造 (活性/非活性) 的停留时间.
- 不活性激酶状态的有限实验结构阻碍了药物发现工作.
研究的目的:
- 评估基于人工智能的蛋白质结构预测方法 (AlphaFold2和ESMFold) 来探索酶结构空间.
- 调查AlphaFold2在不同多重序列对齐 (MSA) 深度预测多种激酶构造的能力.
- 通过配体丰富分析评估人工智能生成的激酶模型对基于结构的药物发现的实用性.
主要方法:
- 使用蛋白质数据库 (PDB) 结构和AlphaFold2 (AF2) 和ESMFold模型探索酶构造空间.
- 在不同MSA深度下分析AF2的形状预测偏差.
- 使用对接模型对23个激酶进行带丰富分析,以评估预测的准确性.
主要成果:
- AF2显示了对DFG-in形状的偏差,反映了PDB数据.
- 在较低的MSA深度使用AF2预测激酶结构,为398个激酶揭示了替代性构造.
- 接模型显示中度的配体丰富 (avgAUC 64.58),一些激酶表现良好 (例如,PTK2,JAK2).
- 一些模型中的结合部位封闭解释了连接物丰富性能的差异.
结论:
- AF2成功地探索了新酶结构空间,为合理的药物发现提供了潜力.
- 人工智能生成的模型可以识别出有前途的形状,但对于可靠的药物发现应用需要严格的细化.
- 人工智能模型的进一步开发和验证是必要的,以克服诸如绑定部位封闭等局限性.
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