Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics.
Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics.
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DOI：
10.1021/acs.jctc.1c00924
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发表时间：
2022-04-12
影响因子：
5.5
通讯作者：
Rosta, Edina
中科院分区：
文献类型：
--
作者：
Badaoui, Magd;Buigues, Pedro J.;Berta, Denes;Mandana, Gaurav M.;Gu, Hankang;Foldes, Tamas;Dickson, Callum J.;Hornak, Viktor;Kato, Mitsunori;Molteni, Carla;Parsons, Simon;Rosta, Edina
药物停留时间的确定是药物发现过程的一个基本部分，它定义了抑制剂与靶点复合的时间。然而，动力学速率常数的合成和实验测量既昂贵又耗时。在这项工作中，我们的目标是计算获得药物停留时间。此外，我们提出了一种新的算法来识别基于配体解结合动力学的分子设计目标。我们设计了一种增强的采样技术，以准确预测配体解结合过程的自由能分布，重点关注解结合的自由能势垒。我们的方法首先确定解结合路径，确定一组相应的内部坐标（ic），这些内部坐标在蛋白质和配体之间形成接触；然后在一系列有偏分子动力学（MD）模拟中迭代更新这些相互作用，以揭示在整个解结合过程中重要的ic。随后，我们进行了有限温度串模拟，以获得将一组ic作为复杂反应坐标的解结自由能势垒。重要的是，我们还旨在进一步设计专注于改善停留时间的药物。为此，我们开发了一种有监督的机器学习（ML）方法，其输入来自于在字符串解绑定路径的过渡状态（TS）集合附近启动的无偏“下坡”轨迹。我们证明我们的ML方法可以识别驱动系统通过TS的关键配体-蛋白质相互作用。一些最重要的癌症治疗药物是激酶抑制剂。其中一个激酶靶点是周期蛋白依赖性激酶2 (CDK2)，这是抗癌药物开发的一个吸引人的靶点。在这里，我们使用两种不同的CDK2抑制剂测试了我们的方法，以进一步开发这些化合物。我们将计算得到的自由能势垒与现有实验数据中观察到的自由能势垒进行了比较。我们强调了重要的相互作用在配体的远端，可以改善停留时间的目标。我们的方法提供了一种新的工具来确定解结合率，并确定抑制剂的关键结构特征，这些特征可以用作药物发现新设计策略的起点。
The determination of drug residence times, which define the time an inhibitor is in complex with its target, is a fundamental part of the drug discovery process. Synthesis and experimental measurements of kinetic rate constants are, however, expensive and time consuming. In this work, we aimed to obtain drug residence times computationally. Furthermore, we propose a novel algorithm to identify molecular design objectives based on ligand unbinding kinetics. We designed an enhanced sampling technique to accurately predict the free-energy profiles of the ligand unbinding process, focusing on the free-energy barrier for unbinding. Our method first identifies unbinding paths determining a corresponding set of internal coordinates (ICs) that form contacts between the protein and the ligand; it then iteratively updates these interactions during a series of biased molecular dynamics (MD) simulations to reveal the ICs that are important for the whole of the unbinding process. Subsequently, we performed finite-temperature string simulations to obtain the free-energy barrier for unbinding using the set of ICs as a complex reaction coordinate. Importantly, we also aimed to enable the further design of drugs focusing on improved residence times. To this end, we developed a supervised machine learning (ML) approach with inputs from unbiased “downhill” trajectories initiated near the transition state (TS) ensemble of the string unbinding path. We demonstrate that our ML method can identify key ligand–protein interactions driving the system through the TS. Some of the most important drugs for cancer treatment are kinase inhibitors. One of these kinase targets is cyclin-dependent kinase 2 (CDK2), an appealing target for anticancer drug development. Here, we tested our method using two different CDK2 inhibitors for the potential further development of these compounds. We compared the free-energy barriers obtained from our calculations with those observed in available experimental data. We highlighted important interactions at the distal ends of the ligands that can be targeted for improved residence times. Our method provides a new tool to determine unbinding rates and to identify key structural features of the inhibitors that can be used as starting points for novel design strategies in drug discovery.
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影响因子：
1.8
作者：
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通讯作者：
Brault, PA
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Gervasio, Francesco Luigi