Research and deployment of binding-domain flexible MovableType (MTFlex) for free energy-based affinity prediction and crystallographic structure determination

研究和部署结合域柔性 MovableType (MTFlex)，用于基于自由能的亲和力预测和晶体结构测定

• 批准号: 10093097
• 负责人: Lance M Westerhoff
• 金额: $ 51.32万
• 依托单位: QUANTUMBIO, INC.
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10093097
• 关键词: Abbreviations; Active Sites; Address; Affinity; Binding; Binding Proteins; Biological; Collaborations; Computational Biology; Computer software; Data; Databases; Development; Disease; Docking; Drug Design; Drug Interactions; Environment; Free Energy; Generations; Grant; Hand; Health; Human; Industrialization; Laboratories; Lead; Letters; Libraries; Licensing; Life; Ligands; Literature; Manuals; Manuscripts; Maps; Medicine; Methodology; Methods; Michigan; Modeling; Modernization; Molecular; Molecular Conformation; Online Systems; Pharmacologic Substance; Phase; Positioning Attribute; Preparation; Printing; Probability; Proteins; Protocols documentation; Publishing; Reader; Research; Roentgen Rays; Sampling; Small Business Innovation Research Grant; Statistical Mechanics; Structural Models; Structure; Surface; System; Technology; Therapeutic; Universities; Validation; Work; base; cloud platform; cost; database structure; density; design; drug candidate; drug discovery; electron density; flexibility; graphical user interface; human disease; improved; innovation; insight; interest; intermolecular interaction; knowledge base; molecular dynamics; next generation; novel; novel therapeutics; protein data bank; protein structure; receptor; small molecule; success; tool

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. Determining the structure of a small molecule (drug candidate or lead compound) bound to a biological receptor (protein implicated in disease) is a necessary step in this approach to drug discovery. In this proposal we describe a novel method we call MovableType (MT), which addresses the protein ligand binding and scoring problem using fundamental statistical mechanics combined with a novel way to generate the ensemble of a ligand in a protein binding pocket. Via a rapid assembly of the necessary partition functions we directly obtain binding free energies and the low free energy poses. 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. In this approach we construct two databases that 1) describe the probability of certain pairwise interactions as a function of r obtained from a knowledge base (Protein Databank (PDB) or the Cambridge Structural Database (CSD)) and 2) the energetics of the pairwise interactions as a function of r obtained from empirical potentials, which can be either derived from the probabilities or can utilize extant pairwise potentials like AMBER. 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 endstate and ensemble binding affinity prediction), MTDock (ligand placement), and MTCS (ligand conformational search). In this project, we will extend our MT product line and deliver this methodology to X-ray crystallographers and computational chemists for use in automated sidechain rotamer and target loop sampling within and around the active site, accurate binding affinity prediction and minima selection, and crystallographic density matching and placement. This work will involve development of a new, integrated tool for automated structure/model preparation, rotamer/loop selection, rotamer/loop generation (“MTFlex proper”), loop/totamer minimization, and analysis. We will commercially deploy the technology, which we will call MTFlex, construct graphical user interfaces for use in MOE, Phenix, and our web-based cloud platform. Finally, this software will be used in real life structure-based drug discovery problems with our pharmaceutical collaborators (see Letters of Support).

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 准确的目标努力/配体结构可以对下一代设计产生变革性影响 如果我们能够使用这些方法常规且准确地设计分子，那么药物的作用就不会被夸大。 它将通过在付出更多努力的情况下胜出无需活性聚焦的化合物来彻底改变药物发现 以及对高活性化合物的审查确定小分子（候选药物或先导药物）的结构。 化合物）与生物受体（与疾病有关的蛋白质）结合是该方法的必要步骤 药物发现。 在这个提案中，我们描述了一种称为 MovableType (MT) 的新方法，它解决了蛋白质配体 使用基本统计力学结合新颖的生成方式来解决绑定和评分问题 通过快速组装必要的配分函数，将配体集成到蛋白质结合袋中。 我们直接获得结合自由能和低自由能位姿 从概念上讲，MT方法是。 类似于块和排版打印，这使我们能够有效地评估描述的分区函数 在这种方法中，我们构建了两个数据库，1）描述了感兴趣的区域或系统。 某些成对相互作用作为从知识库（蛋白质数据库（PDB）或 剑桥结构数据库 (CSD)) 和 2) 成对相互作用的能量学作为 r 的函数 从经验势中获得，经验势可以从概率中导出，也可以利用现有的成对 总体而言，MT 方法是一种通用方法，可以使用广泛的二体势。 如果需要的话，最近的 MT 方法的工作可以扩展到更高阶的相互作用。 导致推出了三个核心产品模块：MTScore（终态和整体结合亲和力预测）、 MTDock（配体布局）和 MTCS（配体构象搜索）在这个项目中，我们将扩展我们的 MT。 产品线并将该方法提供给 X 射线晶体学家和计算化学家，用于 活性位点内部和周围的自动化侧链旋转异构体和目标环采样，准确的结合亲和力 这项工作将涉及预测和最小值选择以及晶体密度匹配和放置。 开发一种新的集成工具，用于自动化结构/模型准备、旋转异构体/环选择、 旋转异构体/环生成（“MTFlex 适当”）、环/环异构体最小化和分析我们将进行商业部署。 该技术，我们将称为 MTFlex，构建用于 MOE、Phenix 和我们的图形用户界面 最后，该软件将用于现实生活中基于结构的药物发现问题。 与我们的制药合作者（参见支持信）。