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中心问题之一，因为它 对理解分子间相互作用的重要性，以及它在药物发现中的实际回报 努力。准确的靶标/配体结构对下一代设计可能产生的变革性影响 药物的作用怎么强调都不为过。如果我们能够使用这些方法常规且准确地设计分子 它将通过筛选出没有活性的化合物同时集中更多精力来彻底改变药物发现 以及对高活性化合物的审查。 在本提案中，我们描述了一种称为 MovableType (MT) 的新颖方法，该方法首次与 最先进的增强分子动力学 (MD) 方法（例如模拟回火、加速 MD、 目标 I.1 和 II.1a 中的元动力学和复制交换 MD），线性标度量子力学（对于 Aim I.2 中改进的静电学），以及称为连续直方图的新蒙特卡洛采样制度 Monte Carlo (CHMC) 在 Aim II.1b 中提高速度。我们预计这一发展将显着扩大 特别是机器翻译（以及一般的自由能方法）的领域适用性，包括以下情况 需要比单独对接提供的更多的构象采样。 MT 使用基本统计力学结合解决蛋白质配体结合和评分问题 用一种新方法在蛋白质结合袋中生成配体整体。通过快速组装 必要的配分函数，通过MT我们直接得到绝对结合自由能和低自由能 构成（与商业/工业实验室中最传统的自由能方法相比，通常获得相对 结合自由能）。从概念上讲，MT 方法类似于块版印刷和排版印刷，它允许 我们可以有效地评估描述感兴趣区域或系统的配分函数。总体而言，MT方法是 一种通用的，可以使用广泛的二体势函数，并且可以扩展到更高阶 如果需要的话可以进行互动。最近对 MT 方法的研究导致了三个核心产品模块的推出： MTScore（最终状态和基于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 与先进的采样技术一起使用，并将这种方法提供给计算化学家，以供他们在他们的研究中使用 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、与多种分子动力学的集成和优化 引擎，添加了更新的静电引擎（基于我们成熟的线性缩放、半经验量子 机械基础设施），开发新的蒙特卡罗方法以提高速度，以及基于云的 在 GridMarkets 平台上部署（目标 II.2）。最后，在目标 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中基于结构的使用 我们的制药合作者的药物发现问题（参见支持信）。