预测OATP1B1的抑制剂通过异质的OATP-连接体相互作用图神经网络 (HOLIgraph)
Mehrsa Mardikoraem1,2, Joelle N Eaves1,2, Theodore Belecciu1,2
1Department of Chemical Engineering and Materials Science, Michigan State University, 428 S Shaw Ln, East Lansing, MI, 48824, USA.
通过有机离子载体聚 (OATPs) 介导的药物相互作用的预测现在更准确地使用HOLIgraph,一种新的计算模型. 这种先进的方法提高了OATP抑制的预测能力,提高了药物安全性.
科学领域:
- 药理学 药理学是指药理学的学科.
- 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:
- 有机阴离子载体多 (OATPs) 是重要的膜载体,影响药物的吸收和分布.
- 由于药物摄入受损,OATP介导的药物相互作用 (DDI) 存在重大风险,包括潜在的致命副作用.
- 目前对OATP介导的DDI的预测受到抑制机制和可变实验数据的不完全理解的阻碍.
研究的目的:
- 开发一种新的计算模型,HOLIgraph,用于更好地预测药物诱导的OATP抑制.
- 整合分子建模和图形神经网络,以提高DDI预测准确度.
- 为了确定参与OATP抑制剂相互作用的关键蛋白质残留物.
主要方法:
- 开发了HOLIgraph,一种异质OATP-利干互动图神经网络模型.
- 集成的连接物分子特征与来自对接模拟的蛋白质-连接物相互作用数据.
- 训练并验证了用于预测OATP1B1.1.的抑制剂的模型.
主要成果:
- 在预测OATP抑制方面,HOLIgraph在与传统的纯联体模型相比表现优越.
- 在预测OATP1B1抑制剂方面达到超过90%的中位平衡精度.
- 确定了特定的OATP1B1残留物,如I46和K49,它们优先与抑制剂相互作用.
结论:
- HOLIgraph显著提高了药物诱导的OATP抑制的预测,提供了一个更可靠的DDI评估工具.
- 该模型对蛋白质-连接体相互作用的洞察力可以指导未来对OATP的机制和结构研究.
- 改善OATP介导的DDI的预测有助于更安全的药物开发和临床实践.
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