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.

对于小蛋白质和简单的酶，少数微秒的分子动力学（MD）模拟可访问罕见事件的生物分子模拟。然而，在处理更大且更复杂的生物分子时，在蛋白质的自由能景观中采样有趣的事件是更难的。其中一些事件的过渡状态（TS）和激活屏障可能太高而无法通过自由MD攀爬，因此近年来已经开发了许多不同的方法来克服障碍和采样它们之间的TS区域。到目前为止，目的是开发新方法，通过处理所得数据并更新方法或直接将其直接应用于方法，可以借助机器学习（ML）来改善采样方面。例如，正在对MG电荷进行调整复制品交换（REX）方法，以获得约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到末端运行并重新恢复所需的配置所需的时间，因此我们可以改善计算时间。这是针对CRISPR-CAS9蛋白质系统的，并附有DNA链。 MG电荷范围从有意义的电荷（2.0）到低一个（0.4），通过77个窗口中的指数衰减。因此，我们希望我们能够使用这种方法在活性位点找到该蛋白质的正确金属配位，并开发一条管道以轻松地将其应用于任何其他金属酶。除了REX外，我们还试图提出最佳反应坐标，以描述具有良好抑制剂的CDK2系统的结合/解开事件的FEL。我们正在通过测试某些神经网络（NN）算法（例如卷积NN或多层感知者NN）测试基于监督学习的ML方法。然后将使用最佳反应坐标来调整采样方法，以找到事件TS的最佳途径。