Development of Enhanced Sampling Methods for Biomolecular Simulations applying Machine Learning methods.

应用机器学习方法开发生物分子模拟的增强采样方法。

• 批准号: 2125311
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2125311
• 关键词: Development; Enhanced; Sampling; Methods; Biomolecular

Biomolecular simulations of rare events are accessible for small proteins and simple enzymes with few microseconds of Molecular Dynamics (MD) simulations. Nevertheless, It is way harder to sample the interesting events in the free energy landscape of a protein when dealing with bigger and way more complex biomolecules. The Transition State (TS) and activation barrier of some of those events may be too high to climb by means of free MD, thus many different methods to overcome the barriers and sample the TS regions between them had been developed in the recent years. So far, the goal is to develop new methods that could improve on the sampling aspect with the aid of Machine Learning (ML) by treating the resulting data and updating the method or either applying it directly in the method. For instance, a Replica Exchange (Rex) method is being adapted for Mg charges to get an approximate of 30% probability of exchange, we will further study its behaviour and the amount of time required for a configuration to run from the first window to the end and get back, so we can improve on calculation time. This is done with for Crispr-Cas9 protein system with a DNA strand attached to it. The MG charge ranges from a meaningful charge (2.0) to a low one (0.4) by an exponential decay in 77 windows. With this we hope we will be able to use this method to find the correct metal coordination for this protein in the active site, and develop a pipeline to apply it easily to any other metal enzymes. Besides the Rex, we are also trying to come up with the optimal reaction coordinates to describe the FEL of a binding/unbinding event for the CDK2 system with a good inhibitor. We are testing a ML approach based on supervised learning by testing some Neural Network (NN) algorithms such as convolutional NN or Multi-layer Perceptron NN. The optimal reaction coordinates will be used then to adapt the sampling method to find the best path to the TS of the event.

对于小的蛋白质和简单的酶，只需几微秒的分子动力学(MD)模拟，就可以对罕见事件进行生物分子模拟。然而，在处理更大、更复杂的生物分子时，要在蛋白质的自由能图景中采样有趣的事件要困难得多。其中一些事件的过渡态(TS)和活化势垒可能太高，无法通过自由MD攀登，因此近年来人们发展了许多不同的方法来克服这些障碍并采样它们之间的TS区。到目前为止，我们的目标是开发新的方法，通过处理得到的数据并更新方法，或者直接将其应用于方法，从而在机器学习(ML)的帮助下改进采样方面。例如，正在采用副本交换(REX)方法来处理MG费用，以获得大约30%的交换概率，我们将进一步研究其行为以及配置从第一个窗口运行到结束并返回所需的时间量，以便我们可以改进计算时间。这是用带有DNA链的Crispr-Cas9蛋白系统完成的。在77个窗口中，MG电荷的范围从有意义的电荷(2.0)到低电荷(0.4)呈指数衰减。有了这一点，我们希望我们能够使用这种方法来找到该蛋白在活性部位的正确金属配位，并开发一条管道，以便将其应用于任何其他金属酶。除了REX，我们还试图提出一个最优的反应坐标来描述具有良好抑制剂的CDK2体系的结合/非结合事件的自由电子发光。我们正在通过测试卷积神经网络或多层感知器神经网络等神经网络算法来测试一种基于监督学习的最大似然方法。然后将使用最优反应坐标来调整采样方法，以找到到达事件TS的最佳路径。