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

摘要 蛋白质/配体结合的研究是计算生物学研究的核心问题之一。 对了解分子间相互作用的重要性，因为它在药物发现中的实际回报 努力。准确的靶/配体结构在下一代设计中可以产生革命性的影响 药物怎么夸大都不为过。如果我们能用这些常规而准确的设计分子 它将通过在集中精力的同时筛选出没有活性的化合物来革命性地发现药物 对高活性化合物进行更多的努力和审查。测定小分子(药物)的结构 候选化合物或先导化合物)与生物受体(与疾病有关的蛋白质)结合是必要的步骤 以这种方式进行药物发现。X射线技术为蛋白质的结构提供了惊人的洞察力- 配体络合物，但会受到晶体衍射率和精确度的限制 缺乏新的小分子化合物的良好潜力可能会阻碍这一过程。我们有 扩展了我们的线性标度半经验量子力学(QM)X射线精化方法并应用于该方法 在这个领域取得了巨大的成功。这种方法已经证明自己足够健壮，可以用于常规的基于QM的X- Ray Refinition，目前正在成功上市。然而，由于精化方法是 归根结底，它们都建立在优化算法之上，不包括抽样，它们都遭受着所谓的 “有限的收敛半径。”因此，晶体工作流程--自动和手动--包括配体 放置作为模型构建过程的一部分。用于配基放置的传统自动程序是 依赖于分辨率，无法考虑活性中心的化学成分。此外，配体 构象通常是如此高度应变，以至于仅靠X射线精化不能推断出正确的结构。 当这种情况发生时，需要对结晶师的部分进行重大干预，这增加了 成本高，工作效率低。 在这项建议中，我们描述了一种新的方法，我们称之为可移动类型(MT)，它解决了蛋白质配体 用基本统计力学结合一种新的方法生成装订和评分问题 蛋白质结合口袋中的配位体。通过快速组合必要的分区函数 我们直接得到了束缚自由能和低自由能姿态。从概念上讲，MT方法是 类似于版式和排版打印，它允许我们有效地计算描述 感兴趣的地区或系统。在这种方法中，我们构建了两个数据库，它们1)描述了 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 从经验势获得的，经验势可以从概率中推导出来，也可以利用现有的 像琥珀这样的成对势能。总体而言，MT方法是一种通用方法，可以使用范围很广的两个- 体势函数，如果需要，可以扩展到更高阶的相互作用。在这个项目中，我们将 扩展MT方法，并将此方法提供给X射线晶体学家和计算化学家 用于X射线细化过程中实验密度范围内的自动配基放置。这项工作将 参与开发一种新的自动化工具，以找到活性部位配体密度并将配体放置在 使用MT方法得到的密度。我们将商业部署该技术，构建图形用户 用于MoE、Phenix和我们的基于Web的云平台的接口。最后，该软件将在实际中使用 基于生命结构的药物发现与我们的药物合作者之间的问题(参见支持信)。

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. 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).