Data-Driven Approaches for Molecular Docking

描述（由申请人提供）：用于药物发现的分子对接方法用于预测配体结合模式，配体结合亲和力，以及用于计算筛选以确定与药物相关的生物靶标的新特异性配体。目前的方法的特点是高度的靶标间可变性和对结构上不同于已知配体的新配体进行准确预测的能力有很大的局限性。直接模拟蛋白质柔韧性或模拟复杂分子系统细节的方法已经取得了一些成功。然而，它们通常只适用于低通量和高质量实验确定的蛋白质靶结构可用的情况。这在一定程度上解释了为什么“模仿型”药物主导着制药市场和研发渠道。与结构新颖的疗法相比，这类药物通常给患者治疗带来的药理学新颖性要少得多。我们提出了一套完整的分子对接方法，这些方法认真对待蛋白质的灵活性，计算效率足够高，可以广泛使用，并且在很少存在感兴趣的生物靶点的实验结构的情况下，它提供了有效使用对接的机会。我们最近的工作已经建立了一种处理对接中蛋白质灵活性的方法，该方法通过考虑多种实验结构和从许多假定的配体对接开始的蛋白质/配体复合物弛豫来解决大蛋白质运动和小配体依赖运动。我们还建立了一种从头开始的蛋白质口袋诱导方法，该方法仅基于配体结合数据构建结合位点，能够准确预测结构新颖配体的结合几何形状和结合亲和力。我们建议的工作将结合这些方法。在蛋白质结构信息可用的情况下，实验确定的结构将进行额外的采样，然后根据配体结合数据对结合袋进行改进，以改进基于我们为从头构建口袋开发的现有方法从对接中获得的预测。除了数据驱动的口袋改进之外，建议的工作还需要改进我们对接的评分功能，考虑到更多的数据和蛋白质灵活性的显式建模以及蛋白质和配体的非结合状态。我们还将在算法改进方面投入大量精力，这将导致在普通单处理器硬件上每个配体的典型运行时间为几分钟，从而产生结合几何形状和亲和力的预测。我们相信，一个广泛适用的、真正具有预测性的、计算上易于处理的对接建模方法将在实践中大大改善药物发现。这些方法将有助于从定向先导优化和计算筛选练习中鉴定新的先导化合物。

DESCRIPTION (provided by applicant): Molecular docking approaches for drug discovery are used for predicting ligand binding modes, ligand binding affinities, and for computational screening to identify new specific ligands for biological targets of pharmaceutical relevance. Current approaches are characterized by high inter-target variability and sharp limitations in ability to make accurate predictions for new ligands that are structurally different from known ones. Approaches that model protein flexibility directly or approach simulation-level detail in complex molecular systems have shown some success. However, they are generally applicable only in low throughput and in cases where high-quality experimentally determined protein target structures are available. This is in part why "me-too" drugs dominate the pharmaceutical marketplace and development pipeline. Such drugs generally bring much less pharmacological novelty to patient treatment than structurally novel therapeutics. We propose an integrated set of methods for molecular docking that treats protein flexibility in a serious manner, is computationally efficient enough for wide use, and which offers the opportunity to effectively use docking in cases where few experimental structures exist for a biological target of interest. Our recent work has established an approach to treating protein flexibility in docking that addresses large protein movements by considering multiple experimental structures and small ligand-dependent movements by protein/ligand complex relaxation beginning from many putative ligand dockings. We have also established an approach for de novo protein pocket induction that constructs a binding site based solely on ligand binding data that is capable of making accurate predictions of binding geometry and binding affinity for structurally novel ligands. Our proposed work will combine these approaches. In cases where protein structural information is available, experimentally determined structures will undergo additional sampling, followed by refinement of the binding pockets based on ligand binding data in order to improve the predictions obtained from docking based upon our existing methods developed for de novo pocket construction. In addition to data-driven pocket refinement, the proposed effort requires improvement in our scoring functions for docking, taking into account vastly more data and explicit modeling of protein flexibility and of the unbound states of proteins and ligands. We will also put significant effort into algorithmic improvements that will result in typical run-times on common single-processor hardware of several minutes per ligand to yield predictions of binding geometry and affinity. We believe that a widely applicable, genuinely predictive, and computationally tractable modeling approach to docking will substantially improve drug discovery in practice. These methods will facilitate identification of novel lead compounds from directed lead optimization and computational screening exercises.