Predicting Absolute Binding Free Energies for Lipid Exposed Binding Sites

我们体内的细胞被膜包围着。嵌入这些膜中的蛋白质执行各种各样的功能，包括维持细胞完整性，充当信号受体，催化反应的酶和转运蛋白，其工作是将营养物质送入和送出细胞。事实上，这些膜蛋白占人类所有已知药物靶标的50%左右。然而，由于它们的功能要求它们改变形状（构象），因此新药的设计仍然非常困难。该项目旨在开发工具，这将加速发现经典的不可药用的，固有的灵活的膜蛋白靶点。虽然膜蛋白是最难获得和纯化的蛋白质之一，但几种技术已经被用于推进这一领域，包括电子晶体学和冷冻电子显微镜。结构生物学领域的最新进展导致自2005年以来每年可用结构的数量都在增长。截至2019年8月20日，共有938个独特的膜蛋白结构被存放在公共领域。这第一次提供了一个机会，不仅可以研究这些药物的作用机制，还可以将未来的药物设计工作置于更坚实的结构基础上，特别是针对作用于膜内位点的药物。一个关键的计算技术，可以扩展从结构研究中获得的信息，并提供急需的信息的基础动力学是分子动力学（MD）模拟。有效地，MD模拟允许人们在原子水平上可视化蛋白质如何在现实的膜环境中移动。该项目建立在Vertex和Biggin教授实验室的综合专业知识基础上。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如何以合理的结构方式靶向各种膜蛋白的关键。此外，Vertex在为这些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该项目中进行的计算预测。该项目的总体目标是：1。开发和验证膜蛋白-药物相互作用的新方法，以Biggin小组以前的工作为基础，特别关注自由能计算和使用深度学习的快速姿势预测。2.评估靶蛋白的动态行为及其与模拟体内组成的双层系统中小分子的相互作用。该项目可称为“发现科学”，因此是MRC的战略优先事项之一。更重要的是，该奖学金将涉及培训学生先进的模拟和机器学习技术。他们将出现优秀和多样化的定量技能，这是目前被视为MRC技能优先。

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.