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Transducer Mechanism: Enzyme-Linked Receptors

Transducer Mechanism: Enzyme-Linked Receptors

Enzyme-linked receptors are cell-surface receptors acting as an enzyme or associating with an enzyme intracellularly. They make excellent drug targets. Drugs can bind to the extracellular ligand-binding domain or directly affect their enzymatic domain and alter their activity.
Major types that are helpful drug targets include:

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Updated: Jun 17, 2026

Identification of Kinase-substrate Pairs Using High Throughput Screening

Identification of Kinase-substrate Pairs Using High Throughput Screening

Published on: August 29, 2015

在Kinase药物发现中的基准测试交叉对接策略.

David A 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.

Journal of chemical information and modeling

|November 19, 2024

概括

此摘要是机器生成的。

精确预测蛋白质-配体复杂结构对于药物发现中的机器学习至关重要. 结合对接方法和使用多种蛋白质结构,改善了对激酶抑制剂的姿势预测精度.

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Last Updated: Jun 17, 2026

Identification of Kinase-substrate Pairs Using High Throughput Screening

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Published on: August 29, 2015

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

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Published on: October 23, 2019

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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.
预测生物活性需要精确的蛋白质-连接体复杂结构,这是目前的局限性.
结构信息可以提高ML得分,但依赖于可靠的复杂结构预测.

研究的目的:

开发用于ML评分的实用方法来生成有用的激酶抑制剂复杂几何体.
创建一个以酶为中心的对接基准来评估对接和姿势选择策略.
在一个现实的交叉对接场景中评估实验观察到的结合模式的回顾.

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,形状重叠和最大常见底结构 (MCS) 匹配.
利用KinoML框架和OpenEye工具包进行自动化复杂生成.

主要成果:

由共结晶联体体 (形状与/或没有MCS重叠) 偏向的对接方法优于标准基于物理的对接.
在多个蛋白质结构中对接显著增加了生成准确 (低RMSD) 姿势的可能性.
使用MCS选择相似的配体和结构的综合方法 (Posit) 在复制结合姿势方面取得了70.4%的成功率.

结论:

干偏差对接策略和多结构对接提高了蛋白质-干复合体预测的准确性.
Posit方法提供了一种有效的方法,用于ML应用程序生成可靠的姿势.
这些发现虽然集中在激酶上,但有可能转移到其他蛋白质家族,以改善药物发现管道.