从与人类蛋白质组结构的药物结合 afinities 预测治疗和副作用
Ryusuke Sawada1,2, Yuko Sakajiri1,3, Tomokazu Shibata1
1Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Japan.
iScience
|June 13, 2024
概括
这项研究引入了一种计算方法,利用蛋白质结合亲缘关系来预测药物治疗指示和副作用. 该方法利用大规模模拟和机器学习来开发药物发现应用.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学是指药理学的学科.
- 药物发现 药物发现
背景情况:
- 评估药物与蛋白质的结合亲缘关系对于理解药物作用至关重要,但传统上需要已知的3D蛋白质结构.
- 现有的方法面临局限性,特别是对于缺乏实验结构数据的蛋白质.
研究的目的:
- 开发和验证用于预测药物治疗指示和副作用的新计算方法.
- 为了能够在整个蛋白质组中预测药物向相互作用及其后果.
主要方法:
- 进行了大规模的对接模拟,将7582种药物与来自AlphaFold的19135种人类蛋白质结构对比.
- 基于全蛋白质组结合亲和度评分 (PBAS) 资料构建的机器学习模型.
- 应用模型来预测治疗指示和潜在的副作用.
主要成果:
- 成功预测了559种疾病的治疗适用性和285种毒性的副作用.
- 证明能够预测药物向蛋白质的指示,而无需实验确定结构.
- 确定了负责药物诱导副作用的特定蛋白质.
结论:
- 拟议的计算方法为预测全蛋白质组范围内的药物指示和副作用提供了一个强大的工具.
- 这种方法可以通过在过程的早期确定潜在的治疗用途和安全问题来加速药物发现.
- 该方法对于探索涉及具有有限结构信息的蛋白质的药物向相互作用特别有价值.
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