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的核心问题之一，因为其 重要的是了解分子间的相互作用，因为它在药物发现的实际回报， 努力精确的靶标/配体结构在下一代设计中可能产生的变革性影响 药物不能被夸大。如果我们能用这些方法常规而精确地设计分子 它将彻底改变药物发现，通过筛选出没有活性的化合物，同时集中更多的精力， 以及对高活性化合物的详细检查 在这个提议中，我们描述了一种新的方法，我们称之为MovableType（MT），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， 目标I.1和II.1a中的线性标度量子力学（用于 改进的静电学），以及一种称为连续直方图的新的蒙特卡罗采样机制 目标二.1b中的蒙特卡罗法（CHMC），以提高速度。我们预计，这一发展将大大扩大 特别是MT的域适用性（以及一般的自由能方法），包括那些 需要比单独对接提供的更多的构象取样。 MT解决了蛋白质配体结合和评分问题，使用基本的统计力学结合 用一种新的方法在蛋白质结合口袋中产生配体的整体。通过快速组装 必要的配分函数，与MT，我们直接获得绝对结合自由能和低自由能 姿势（相对于商业/工业实验室中的大多数常规自由能方法， 结合自由能）。从概念上讲，MT方法类似于块和类型集打印，它允许 我们可以有效地评估描述感兴趣的区域或系统的分区函数。总的来说，MT方法是 一个通用的，可以使用广泛的两体势函数，并可以扩展到高阶 如果需要的话。最近MT方法的工作已经导致了三个核心产品模块的推出： MTScore（基于终态和整体的结合亲和力预测）、MTDock（配体放置）和MTCS （配体构象搜索）。在这个项目中，我们将通过优化方法来扩展我们的MT产品线， 使用先进的采样技术，并将此方法交付给计算化学家， 基于工业结构的药物设计活动。这项工作将涉及开发一个新的综合工具 用于自动化结构/模型制备，集成和优化多种分子动力学 引擎，此外，更新的静电引擎（建立在我们成熟的，线性缩放，半经验量子 机械基础设施），开发新的Monte Carlo方法以提高速度，以及基于云的 在GridMarkets平台上进行部署（目标II.2）。最后，在目标II.3中，我们将在商业上部署 技术，构建用于莫伊图形用户界面，并验证其在基于真实的生活结构中的使用 药物发现问题与我们的制药合作者（见支持信）。

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).