Research and cloud deployment of enhanced sampling methods in MovableType

摘要 蛋白质/配体结合的研究是计算生物学研究的核心问题之一。 对了解分子间相互作用的重要性，因为它在药物发现中的实际回报 努力。准确的靶/配体结构在下一代设计中可以产生革命性的影响 药物怎么夸大都不为过。如果我们能够常规地、准确地使用这些方法设计分子 它将通过筛选出没有活性的化合物来彻底改变药物发现，同时集中更多的努力 以及对高活性化合物的审查。 在这个提案中，我们描述了一种称为MovableType(MT)的新方法，它将首次与 尖端增强分子动力学(MD)方法(例如，模拟回火，加速MD， 元动力学和复制品交换MD)在AIMS I.1和II.1a中，线性标度量子力学(用于 改进的静电学)，以及称为连续直方图的新的蒙特卡罗采样机制 目标II.1b中的蒙特卡洛(CHMC)以提高速度。我们预计这一发展将显著扩大 特别是MT的领域适用性(以及一般的自由能方法)，以包括以下情况 与单独对接相比，需要更大的构象采样。 MT结合使用基本统计力学解决了蛋白质配体结合和评分问题 用一种新的方法来产生蛋白质结合口袋中的配体集合。通过快速组装 必要的配分函数，用MT直接得到绝对结合自由能和低自由能 姿势(与商业/工业实验室中的大多数传统自由能方法相比，通常获得相对 结合自由能)。从概念上讲，MT方法类似于版式和排版打印，这允许 以有效地评估描述感兴趣的区域或系统的配分函数。总体而言，MT方法是 一般的二体势函数，可以推广到更高阶的二体势函数 如果需要的话，可以进行交互。最近关于MT方法的工作导致推出了三个核心产品模块： MTScore(基于末端状态和基于系综的结合亲和力预测)、MTDock(配体放置)和MTCS (配基构象搜索)。在这个项目中，我们将通过优化MT的方法来扩展我们的MT产品线 与先进的采样技术一起使用，并将此方法提供给计算化学家，以便在其 基于产业结构的药物设计活动。这项工作将涉及开发一种新的集成工具 用于自动化结构/模型准备、集成和优化多个分子动力学 发动机，增加了最新的静电发动机(建立在我们成熟的线性定标，半经验量子基础上 机械基础设施)，开发一种新的蒙特卡罗方法以提高速度，以及基于云 在GridMarkets平台上部署(AIM二.2)。最后，在目标II.3中，我们将在商业上部署 技术，构建用于MOE的图形用户界面，并验证其在基于结构的现实生活中的使用 与我们的药物合作者的药物发现问题(请参阅支持信)。

Abstract The study of protein/ligand binding is one of the central problems in computational biology because of its importance in understanding intermolecular interactions, and because of its practical payoff in drug discovery efforts. The transformative impact accurate target/ligand structure can have in the design of next generation medicines cannot be overstated. If we could routinely and accurately design molecules using these approaches it would revolutionize drug discovery by winnowing out compounds with no activity while focusing more effort and scrutiny on highly active compounds. In this proposal we describe a novel method we call MovableType (MT) that for the first time will be coupled with cutting edge enhanced molecular dynamics (MD) methods (e.g., Simulated Tempering, Accelerated MD, Metadynamics, and Replica exchange MD) in Aims I.1 and II.1a, linear scaling quantum mechanics (for improved electrostatics) in Aim I.2, and a new Monte Carlo sampling regime called Consecutive Histograms Monte Carlo (CHMC) in Aim II.1b for increased speed. We expect this development to significantly expand the domain applicability of MT in particular (and free energy methods in general) to include those situations which require greater conformational sampling than can be provided by docking alone. MT addresses the protein ligand binding and scoring problem using fundamental statistical mechanics combined with a new way to generate the ensemble of a ligand in a protein binding pocket. Via a rapid assembly of the necessary partition functions, with MT we directly obtain absolute binding free energies and the low free energy poses (versus most conventional free energy methods in commercial/industrial labs which usually obtain relative binding free energies). Conceptually, the MT method is analogous to block and type set printing, which allows us to efficiently evaluate partition functions describing regions or systems of interest. Overall, the MT method is a general one and can use a broad range of two-body potential functions and can be extended to higher-order interactions if so desired. Recent work with the MT method has led to the launch of three core product modules: MTScore (both end state and ensemble-based binding affinity prediction), MTDock (ligand placement), and MTCS (ligand conformational search). In this project, we will extend our MT product line by optimizing the method for use with advanced sampling techniques and deliver this methodology to computational chemists for use in their industrial structure-based drug design campaigns. This work will involve development of a new, integrated tool for automated structure/model preparation, integration with and optimization for several molecular dynamics engines, addition an updated electrostatics engine (built on our mature, linear scaling, semi-empirical quantum mechanics infrastructure), development of a new Monte Carlo method for increased speed, and cloud-based deployment on the GridMarkets platform (Aim II.2). Finally, in Aim II.3, we will commercially deploy the technology, construct graphical user interfaces for use in MOE, and validate its use in real life structure-based drug discovery problems with our pharmaceutical collaborators (see Letters of Support).