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的核心问题之一，因为其 重要的是了解分子间的相互作用，因为它在药物发现的实际回报， 努力精确的靶标/配体结构在下一代设计中可能产生的变革性影响 药物不能被夸大。如果我们能用这些常规而精确地设计分子 这种方法将彻底改变药物发现，通过筛选出没有活性的化合物， 对高活性化合物进行更多的努力和审查。确定小分子（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 在这种药物发现方法中。X射线技术为蛋白质结构提供了惊人的见解- 配体络合物，但可能会受到晶体衍射的分辨率和细化的阻碍。 由于缺乏新的小分子化合物的良好潜力，该方法可能受到阻碍。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，并将其应用于 在这个领域取得了巨大成功。这种方法已被证明是足够强大的常规基于QM的X- 射线细化，目前已成功上市。然而，由于细化方法是 最终建立在优化算法上，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 作为模型构建过程的一部分。配体放置的常规自动程序是 分辨率依赖性，并且不能考虑活性位点的化学性质。此外，配体 构象往往是如此高度紧张，X射线精修单独，是无法推断出正确的结构。 当这种情况发生时，需要晶体学家进行重大干预， 成本高，降低生产率。 在这个建议中，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 绑定和评分问题，使用基本的统计力学结合一种新的方式来生成 蛋白质结合口袋中配体的集合。通过快速组装必要的分区函数 我们直接得到结合自由能和低自由能位姿。从概念上讲，MT方法是 类似于块和类型集打印，这使我们能够有效地评估分区函数， 感兴趣的区域或系统。在这种方法中，我们构建了两个数据库，1）描述了 作为r的函数的某些成对相互作用从知识库（蛋白质数据库（PDB）或 剑桥结构数据库（CSD））和2）作为r的函数的成对相互作用的能量学 从经验势中获得，经验势可以从概率中获得，也可以利用现有的 像琥珀这样的成对电位。总的来说，MT方法是一种通用方法，可以使用宽范围的两个- 体势起作用，并且如果需要的话，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，并将此方法交付给X射线晶体学家和计算化学家， 在X射线细化期间用于实验密度内的自动配体放置。这项工作将 涉及开发一种新的自动化工具，以找到活性位点配体密度并将配体放置在 用MT方法计算密度。我们将商业化部署这项技术，构建图形用户界面， 用于莫伊、Phenix和我们基于Web的云平台的接口。最后，将该软件应用于真实的 与我们的制药合作者一起解决基于生命结构的药物发现问题（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).