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对基因酶药物发现中的结构信息化机器学习的交叉对接策略进行基准测试

David Schaller^1,2, Clara D Christ³, John D Chodera²

¹In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany.

bioRxiv : the preprint server for biology

|September 25, 2023

概括

准确预测蛋白质:连接体复杂结构是机器学习在药物发现中的关键. 结合对接方法,特别是Posit,显著改善了对激酶抑制剂的结合姿势的预测.

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.
机器学习在药物发现中的作用

背景情况:

机器学习 (ML) 正在彻底改变药物发现,特别是小分子设计.
精确预测蛋白质:配体复杂结构对于基于ML的生物活性预测至关重要.
目前的方法在可靠和自动预测这些复杂结构方面存在局限性.

研究的目的:

开发用于ML评分生成精确的酶:抑制剂复杂几何的实用方法.
创建和评估一个以酶为中心的对接基准,用于评估对接和姿势选择策略.
为了确定实验观察到的结合模式可以在现实的交叉对接场景中再现得多好.

主要方法:

组建了一个基准数据集,包括589个蛋白激酶结构和423个ATP竞争性联结体.
评估了各种对接和姿势选择策略,包括基于物理的对接和带偏差的方法.
利用了形状重叠,最大共同的基结构匹配,以及Posit方法 (结合形状和静电学).
使用OpenEye工具包,并在KinoML框架内实施策略.

主要成果:

基于联体的对接方法 (形状重叠,MCS) 超过了基于物理的标准对接.
在多个蛋白质结构中对接增加了产生低RMSD姿势的可能性.
通过结合多种策略,Posit方法在复制约束姿势方面取得了最高的成功率 (66.9%).

结论:

联体偏差和多结构对接策略提高了蛋白质:联体复杂结构预测的准确性.
定位方法提供了一种有效的方法,用于生成可靠的结合定位.
这些发现虽然集中在激酶上,但对于ML应用来说,这些发现有可能转移到其他蛋白质家族.

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