Research and cloud deployment of enhanced sampling methods in MovableType

MovableType中增强采样方法的研究和云部署

• 批准号: 10699159
• 负责人: Lance M Westerhoff
• 金额: $ 20.77万
• 依托单位: QUANTUMBIO, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10699159
• 关键词: Acceleration; Active Sites; Address; Affinity; Binding; Binding Proteins; Binding Sites; Biological; Biological Sciences; Charge; Chemicals; Client; Cloud Computing; Computational Biology; Coupled; Data; Development; Docking; Drug Design; Electrostatics; Event; Free Energy; Grant; Health; Human; Industrialization; Infrastructure; KRAS2 gene; Laboratories; Letters; Libraries; Licensing; Life; Ligand Binding; Ligands; Link; Markov chain Monte Carlo methodology; Medicine; Methodology; Methods; Michigan; Modeling; Molecular; Molecular Conformation; Monte Carlo Method; Movement; Perception; Pharmaceutical Preparations; Pharmaceutical Services; Pharmacologic Substance; Phase; Preparation; Printing; Proteins; Protocols documentation; Publications; Quantum Mechanics; Reporting; Research; Sampling; Series; Small Business Innovation Research Grant; Speed; Statistical Mechanics; Structure; System; Techniques; Technology; Time; Universities; Update; Validation; Variant; Work; case finding; cloud based; cloud platform; computational platform; cost; design; drug discovery; flexibility; graph theory; graphical user interface; human disease; improved; innovation; insight; interest; intermolecular interaction; lead optimization; molecular dynamics; next generation; novel; novel therapeutics; protein structure; quantum; rational design; receptor; receptor binding; small molecule; tool; validation studies; virtual machine

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).

抽象的 蛋白质/配体结合的研究是计算生物学的核心问题之一 在理解分子间相互作用的重要性，并且由于其在药物发现中的实际收益 努力。在下一代设计中，具有变革性影响准确的目标/配体结构可以具有 药物不能被夸大。如果我们可以常规，准确地使用这些方法设计分子 它将通过赢得无活动的胜利而彻底改变药物发现，同时集中精力 并对高活性化合物进行审查。 在此提案中，我们描述了一种我们称为MovableType（MT）的新方法，这将首次与 尖端增强的分子动力学（MD）方法（例如，模拟回火，加速MD， AIMI.1和II.1A中的元动力学和副本交换MD），线性缩放量子力学（用于 AIM I.2中的静电提高），以及一种称为连续直方图的新蒙特卡洛采样制度 AIMII.1B中的Monte Carlo（CHMC）以提高速度。我们希望这一发展将大大扩展 MT尤其适用于域（通常是自由能方法），包括那些情况 需要比单独停靠的构象采样更大。 MT使用基本统计力学结合了蛋白质配体结合和评分问题 采用一种新的方式来产生蛋白质结合口袋中配体的合奏。通过快速组装 必要的分区功能，使用MT，我们直接获得绝对结合的自由能和低自由能 姿势（在商业/工业实验室中的大多数常规自由能法相对于通常获得相对的商业/工业实验室 结合自由能）。从概念上讲，MT方法类似于阻止和类型设置打印，这允许 我们有效地评估描述区域或感兴趣系统的分区功能。总体而言，MT方法是 一般的，可以使用广泛的两体电位功能，可以扩展到高阶 如果需要的话，相互作用。 MT方法最近的工作导致了三个核心产品模块的推出： MTSCORE（端状态和集合的结合亲和力预测），mtdock（配体位置）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MT产品线 与先进的采样技术一起使用，并将这种方法传递给计算化学家 基于工业结构的药物设计活动。这项工作将涉及开发新的集成工具 为了自动结构/模型制备，与几种分子动力学进行集成并优化 引擎，增加更新的静电引擎（建立在我们成熟的线性缩放，半经验量子上 力学基础设施），开发一种新的蒙特卡洛方法，以提高速度，基于云 在Gridmarkets平台上部署（AIM II.2）。最后，在AIM II.3中，我们将商业部署 技术，构建用于MOE的图形用户界面，并验证其在现实生活结构中的使用 药物发现问题与我们的药物合作者有关（请参阅支持信）。