从蛋白质到体:解码深度学习方法用于绑定亲和力预测
Rohan Gorantla1,2, Alžbeta Kubincová3, Andrea Y Weiße1,4
1School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, U.K.
Journal of chemical information and modeling
|November 20, 2023
概括
预测蛋白质-连接体结合亲和力的深度学习模型严重依赖连接体数据,而不是蛋白质编码. 提高通用性需要专注于模型如何解释药物发现的配体信息.
科学领域:
- 计算化学是一种计算化学.
- 结构生物学是结构生物学.
- 药物发现 药物发现
背景情况:
- 准确的in silico预测蛋白质 - 配体结合亲和力对于早期药物发现至关重要.
- 当前的深度学习方法与传统技术 (如千兆对接) 相比,缺乏通用性.
- 从蛋白质和配体数据中理解模型学习是提高概括性的关键.
研究的目的:
- 系统地研究基于序列的深度学习框架,用于预测绑定亲和关系.
- 评估各种蛋白质和配体编码对预测准确性的影响.
- 评估不同蛋白质接触生成方法和连接体扰动的影响.
主要方法:
- 使用基于卷积神经网络 (CNN) 和图形神经网络 (GNN) 的编码来编码蛋白质和连接体.
- 使用由AlphaFold2,Pconsc4和ESM-1b生成的蛋白质接触地图,以及随机对照.
- 通过随机化节点和边缘属性来测试连接体编码,以评估数据依赖性.
主要成果:
- 蛋白质编码对KIBA数据集的多个指标 (一致性指数,皮尔森的R,斯皮尔曼的排名,RMSE) 的结合亲和力预测没有显著影响.
- 观察到对联体编码的显著差异,特别是在使用随机联体或随机联体节点属性时.
- 通过不同方式组合蛋白质和配体编码并没有产生显著的性能改善.
结论:
- 用于结合亲和力预测的深度学习模型显示,对连接体数据特征的依赖性比对蛋白质编码的依赖性更强.
- 选择蛋白质接触生成方法对预测性能的影响有限.
- 进一步的研究应侧重于优化连接体表征和特征工程,以提高药物发现中的模型通用性.
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