Development of the Movable Type free energy method for ligand placement in X-ray crystallography

X 射线晶体学中配体放置的可移动式自由能方法的开发

• 批准号: 9347830
• 负责人: Lance M Westerhoff
• 金额: $ 16.59万
• 依托单位: QUANTUMBIO, INC.
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2017-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9347830
• 关键词: Active Sites; Address; Agreement; Algorithms; Binding; Binding Proteins; Binding Sites; Biochemistry; Biological; Chemistry; Complex; Computational Biology; Computer software; Crystallization; Databases; Development; Dimensions; Disease; Docking; Drug Design; Drug Targeting; Free Energy; Hand; Imagery; Intervention; Iridium; Lead; Letters; Libraries; Life; Ligand Binding; Ligands; Location; Manuals; Manuscripts; Maps; Measures; Mechanics; Medicine; Methodology; Methods; Modeling; Modernization; Molecular Conformation; Online Systems; Pharmacologic Substance; Phase; Play; Population; Positioning Attribute; Preparation; Printing; Probability; Procedures; Process; Productivity; Proteins; Protomer; Publishing; Quantum Mechanics; R-factor; Radial; Reporting; Research; Resolution; Roentgen Rays; Role; Sampling; Scientist; Solvents; Statistical Mechanics; Structure; Surface; System; Techniques; Technology; Time; Validation; Work; X-Ray Crystallography; base; cloud platform; computer program; density; design; drug candidate; drug discovery; electron density; experimental study; graphical user interface; innovation; insight; interest; intermolecular interaction; knowledge base; model building; next generation; novel; protein complex; protein structure; quantum; receptor; resistance factors; restraint; small molecule; success; tautomer; three dimensional structure; tool

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. 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. X-ray techniques provide astounding insights into the structure of protein- ligand complexes, but can be hampered by the resolution to which a crystal diffracts and the refinement process can be hampered by the lack of good potentials for novel small molecule compounds. We have extended our linear-scaling semiempirical quantum mechanical (QM) X-ray refinement approach and applied it to this field with great success. This approach has proven itself to be robust enough for routine QM-based X- ray refinement and it is currently being successfully marketed. However, since refinement methods are ultimately built on optimization algorithms and do not include sampling, they all suffer from what is termed a “limited radius of convergence.” Therefore, crystallographic workflows - automatic and manual - include ligand placement as part of the model building process. Conventional automatic procedures for ligand placement are resolution dependent and are unable to take into account the chemistry of the active site. Further, the ligand conformation is often so highly strained that X-ray refinement alone, is unable to deduce the proper structure. When this happens, significant intervention on the part of the crystallographer is required, which increases expense and decreases productivity. In this proposal we describe a novel method we call Movable Type (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. In this project we will extend the MT method and deliver this methodology to X-ray crystallographers and computational chemists for use in automated ligand placement within the experimental density during X-ray refinement. This work will involve development of a new, automated tool to find the active site ligand density and place the ligand within that density using the MT method. We will commercially deploy the technology, 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).

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 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药物来常规且准确地设计分子的话，这一点都不为过。 方法将通过赢得没有活性集中的化合物而彻底改变药物发现 对高活性化合物（药物）的结构进行更多的努力和审查。 候选或先导化合物）与生物受体（与疾病有关的蛋白质）结合是必要的步骤 在这种药物发现方法中。 配体络合物，但可能会受到晶体衍射和细化的分辨率的阻碍 由于缺乏新型小分子化合物的良好潜力，这一过程可能会受到阻碍。 扩展了我们的线性缩放半经验量子力学 (QM) X 射线细化方法并应用它 该方法已证明其对于基于 QM 的常规 X- 来说足够强大。 射线细化目前已成功推向市场，但由于细化方法尚未成熟。 最终建立在优化算法之上并且不包括采样，它们都遭受所谓的 “收敛半径有限。”因此，晶体学工作流程（自动和手动）包括配体 配体放置的传统自动程序是模型构建过程的一部分。 分辨率依赖并且无法考虑活性位点的化学性质。 构象往往非常紧张，仅靠 X 射线细化无法推断出正确的结构。 当这种情况发生时，需要晶体学家进行大量干预，这会增加 费用并降低生产力。 在本提案中，我们描述了一种称为 Movable Type (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 从经验潜力获得，经验潜力可以从概率导出，也可以利用现有的 总体而言，MT 方法是一种通用方法，可以使用广泛的二元势。 身体势能函数，如果需要的话，可以扩展到更高阶的相互作用。 扩展 MT 方法并将该方法提供给 X 射线晶体学家和计算化学家 这项工作将用于 X 射线细化过程中实验密度内的自动配体放置。 涉及开发一种新的自动化工具来查找活性位点配体密度并将配体放置在 我们将使用 MT 方法来商业部署该技术，构建图形用户。 MOE、Phenix 和我们基于 Web 的云平台的接口最终将在实际中使用。 我们的制药合作者面临基于生命结构的药物发现问题（参见支持信）。