在CASP16中使用几何深度学习组合和流量匹配预测蛋白质-连接体结构和亲和力
Alex Morehead1, Jian Liu1, Pawan Neupane1
1Department of Electrical Engineering & Computer Science, NextGen Precision Health, University of Missouri, Columbia, Missouri, USA.
Proteins
|April 8, 2025
概括
我们开发了MULTICOM_ligand,这是一个深度学习工具,用于预测蛋白质-连接体结构和结合亲和关系. 它在排名姿势和估计绑定方面表现出色,在药物发现评估中证明有效.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物信息学 结构生物信息学
- 药物发现 药物发现
背景情况:
- 准确预测蛋白质 - 配体相互作用对于药物发现至关重要.
- 目前的深度学习方法面临的挑战是对位列排名和绑定亲和度估计.
- 需要强大的基准测试来验证预测模型.
研究的目的:
- 引入MULTICOM_ligand,这是一个集体深度学习系统,用于蛋白质-连接体结构和结合亲和力预测.
- 通过结构性共识改善无监督姿势的排名.
- 为共同预测开发一种新的深度生成流匹配模型.
主要方法:
- 开发了一种基于深度学习的整体方法 (MULTICOM_ligand).
- 实施了对姿势选择的结构共识排名.
- 引入了一个深度生成流匹配模型,用于集成预测.
主要成果:
- 在CASP16.16中,MULTICOM_ligand在蛋白质-连接体结构预测方面取得了前五名的表现.
- 该系统在CASP16.16的结合亲和力预测方面也排在前五名.
- 在现实世界药物发现场景中证明有效性.
结论:
- MULTICOM_ligand为蛋白质-连接体结构和结合亲和力预测提供了一个强大的解决方案.
- 该系统在CASP16中的性能验证了它在药物发现中的实用性.
- 自由可用的源代码有助于进一步的研究和应用.
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