Predicting Absolute Binding Free Energies for Lipid Exposed Binding Sites

我们体内的细胞被膜包围着。嵌入在这些膜中的蛋白质具有广泛的功能，包括维持细胞的完整性，充当信号的受体，催化反应的酶，以及将营养物质带进和带出细胞的转运蛋白。事实上，这些膜蛋白约占人类所有已知药物靶点的50%。然而，由于它们的功能需要它们改变形状(构象)，所以新药的设计仍然非常困难。该项目旨在开发工具，以加速发现经典的不可药物的、固有的灵活的膜蛋白靶标。尽管膜蛋白是最难获得和表征的蛋白质之一，但已经使用了几种技术来推动这一领域的发展，包括电子晶体和冷冻电子显微镜。自2005年以来，结构生物学领域的最新进展导致可用结构的数量每年都有潜在的增长。截至2019年8月20日，共有938个独特的膜蛋白结构存放在公共领域。这首次提供了一个机会，不仅可以研究这些药物的作用机制，而且还可以使未来的药物设计工作建立在更坚实、更具结构性的基础上，特别是针对作用于膜内部位的药物。分子动力学(MD)模拟是一种关键的计算技术，它可以扩展从结构研究中获得的信息，并提供关于潜在动力学的迫切需要的信息。有效地，MD模拟使人们能够在原子水平上可视化蛋白质在真实的膜环境中的运动。该项目建立在Vertex和Biggin教授的实验室的综合专业知识的基础上。这个计算项目将开发和应用先进的分子动力学和自由能计算，以及机器学习(AI)技术。这两个方面将是理解各种膜蛋白如何以合理的、结构的方式作为靶点的关键。此外，Vertex在为此类目标开发小分子方面的丰富经验，以及分析和合成专业知识，将使我们能够以前瞻性的方式验证在该项目中执行的计算预测。该项目的总体目标是：1.在Biggin小组以前工作的基础上，开发和验证膜蛋白-药物相互作用的新方法，特别是利用深度学习进行自由能计算和快速姿势预测。2.在模拟体内组成的双层体系中，评估目标蛋白质的动态行为及其与小分子的相互作用。这一项目可以被称为“发现科学”，因此是MRCS的战略重点之一。更重要的是，学员将接受高级模拟和机器学习技术方面的培训。他们将会涌现出优秀而多样的量化技能，这目前被视为MRC技能的优先事项。

The cells in our bodies are surrounded by membranes. Embedded within these membranes are proteins that perform a wide variety of functions including maintaining the cell integrity, acting as receptors for signals, enzymes to catalyse reactions and transporters whose job is to get nutrients into and out of the cell. These membrane proteins in fact make up about 50% of all known drug targets in humans. However, because their function requires them to change shape (conformation) the design of new drugs is still very difficult. This project aims to develop tools, which would accelerate the discovery for classically undruggable, inherently flexible membrane protein targets.Although membrane proteins are among the most difficult proteins to obtain and characterise, several techniques have been used to progress this field including electron crystallograpny and cryo-electron microscopy. Recent advancement in structure biology field led to an expotential grow of the number of available structures every year since 2005. As of 20th August 2019 there are 938 unique membrane protein structures deposited in the public domain. This provides for the first time an opportunity to not only investigate the mechanism by which these medicines work but also to put future drug design work on a more solid, structural footing, specifically for drugs that act at sites within the membrane. A key computational technique that can extend the information obtained from structural studies and provide much needed information on the underlying dynamics is Molecular Dynamics (MD) Simulations. Effectively, MD simulations allow one to visualize at the atomic level how the protein moves in a realistic membrane environment.The project builds on the combined expertise of Vertex and Professor Biggin's laboratory. This computational project will develop and apply advanced molecular dynamics and free energy calculations, as well as machine learning (AI) techniques. Both aspects will be key to understanding how various membrane proteins can be targeted in a rational, structural fashion. In addition, the substantial experience of Vertex in terms of developing small molecules for such targets, alongside assay and synthesis expertise, will allow us to validate the computational predictions performed in this project in a prospective manner. The overall aims of the project are: 1. To develop and validate new methodologies for membrane protein-drug interactions, building on the previous work in the Biggin group, with particular focus on free energy calculations and rapid pose prediction using deep-learning. 2. To assess the dynamic behaviour of target proteins and their interaction with small molecules in bilayer systems that mimic the in vivo composition.This project is what might call "Discovery science" and is thus one of the MRCs strategic priorities. More importantly, the studentship will involve training the student in advanced simulation and machine learning techniques. They will emerge will excellent and varied quantitative skills, which is currently seen as an MRC Skills Priority.