Data-Driven Approaches for Molecular Docking

描述(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，并用于计算机筛选以识别与药物相关的生物靶标的新的特定配体。目前的方法的特点是目标间变异性高，对结构上与已知配体不同的新配体做出准确预测的能力存在严重限制。在复杂的分子系统中，直接模拟蛋白质灵活性或接近模拟水平细节的方法已经显示出一些成功。然而，它们通常只适用于低吞吐量和高质量的实验确定的蛋白质靶结构可用的情况。这就是为什么“我也”的药物主导了药品市场和开发渠道的部分原因。与结构上的新疗法相比，这类药物给患者治疗带来的药理学新颖性要小得多。我们提出了一套集成的分子对接方法，以严肃的方式处理蛋白质的灵活性，计算效率足以广泛使用，并提供了在感兴趣的生物靶标几乎不存在实验结构的情况下有效使用对接的机会。我们最近的工作建立了一种处理蛋白质对接灵活性的方法，通过考虑多个实验结构和从许多假定的配体对接开始的蛋白质/配体复合体松弛来处理大的蛋白质运动和小的配体依赖的运动。我们还建立了一种从头蛋白口袋诱导的方法，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。我们提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，实验确定的结构将经历额外的采样，随后根据配体结合数据对结合口袋进行细化，以改进基于我们为从头构建口袋而开发的现有方法的对接获得的预测。除了数据驱动的袖珍精细化外，拟议的工作还需要改进我们的对接评分功能，考虑到更多的数据和对蛋白质灵活性以及蛋白质和配体的非结合状态的显式建模。我们还将在算法改进方面投入大量精力，这将导致每个配体在普通单处理器硬件上的典型运行时间为几分钟，以产生结合几何形状和亲和力的预测。我们相信，一种广泛适用、真正可预测和易于计算的对接建模方法将大大改善实践中的药物发现。这些方法将有助于从直接的先导优化和计算筛选练习中识别新的先导化合物。

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.