Combining structure-based network biology and heterogeneous computing for rational drug repositioning and polypharmacology

1.项目总结 药物通常是为了调节特定蛋白质的功能而开发的，这些蛋白质直接与 特定的疾病状态。尽管如此，最近的研究表明，蛋白质与药物的相互作用是混杂的。 而且大多数药物对多种通常是无关的蛋白质表现出活性。缺乏 选择性往往会导致不良的药物副作用；然而，这些多药理属性可用于 开发作用于独特疾病途径的多个靶点的药物，以及确定新的靶点 现有药物，称为药物重新定位。尽管预测交互越来越多 在药物发现中非常重要，大量相互作用的分子和高度复杂的相互作用 模式带来了巨大的挑战。显然，迫切需要新的计算方法来 严格探索药物交叉反应。因此，拟议研究的总体目标是将一个 计算系统生物学的广泛范围和前景，与医学相关的原子级建模 生物分子及其相互作用，以及使用大规模并行的异质计算 研究药物导向相互作用的加速器。这一创新项目由几个组成部分组成。第一 是设计一个全自动化的基于结构的配体虚拟筛选平台，以信息为特征 以理论为基础的化合物选择。通过使用最大熵方法，我们将能够增强 配基排序计分函数的特异性。第二，我们计划改进跨蛋白质组 结合局部结合位点比对鉴定化学相似药物结合袋 分子对接。这一新战略的优势是能够探索更大的空间 推测蛋白质和小有机化合物之间的交叉作用。第三，我们将使用新的 上述用于重建和研究蛋白质-药物相互作用网络的建模技术 人类蛋白质组。通过开发新的多靶点抗生素，我们将证明所提出的网络 分析极大地拓展了当前多元药理学和合理的药物重新定位的机会空间。 第四，手头任务的规模以及细节的程度对 计算资源。因此，迫切需要利用现代计算机 目前可用的体系结构以及预计将投入生产的亿级超级计算机 在不久的将来。为此，我们计划开发高性能代码，以充分利用异构性 配备大规模并行硬件加速器、NVIDIA GPU和Intel Xeon Phi的机器。关 与实验和计算机科学小组的合作将是拟议的研究的一部分，以进行 在这个高度专业化的领域取得的进展。这一创新提案的预期总体影响是，它将1) 从根本上提高我们对蛋白质-药物相互作用网络的理解，2)利用这一知识 以及尖端的计算机技术来支持新疗法的开发。

1. Project Summary Drugs are typically developed to modulate the function of specific proteins, which are directly associated with particular disease states. Nonetheless, recent studies suggest that protein-drug interactions are promiscuous and the majority of pharmaceuticals exhibit activity against multiple, often unrelated proteins. The lack of selectivity often leads to undesired drug side effects; yet, these polypharmacological attributes can be used to develop drugs that act on multiple targets of a unique disease pathway, as well as to identify new targets for existing drugs, known as drug repositioning. Although predicting interactomes is becoming increasingly important in drug discovery, a large number of interacting molecules and highly complicated interaction patterns present significant challenges. Clearly, novel computational approaches are desperately needed to rigorously explore drug cross-reactivity. The overall goal of the proposed research is, therefore, to combine a broad scope and promises of computational systems biology, atomic-level modeling of medically relevant biomolecules and interactions among them, and heterogeneous computing using massively parallel accelerators to study drug-oriented interactomes. This innovative project comprises several components. First is to design a fully automated platform for structure-based ligand virtual screening featuring an information theory-based compound selection. By using the Maximum Entropy Method, we will be able to enhance the specificity of scoring functions for ligand ranking. Second, we plan to improve the across-proteome identification of chemically similar drug binding pockets by combining local binding site alignment with molecular docking. The advantage of this new strategy is the capability to explore a much larger space of putative cross-interactions between proteins and small organic compounds. Third, we are going to use new modeling techniques described above to reconstruct and investigate protein-drug interaction networks in the human proteome. By developing novel multi-target antibiotics, we will demonstrate that the proposed network analysis greatly expands the current opportunity space for polypharmacology and rational drug repositioning. Fourth, the scale of the task at hand as well as the level of details put an unprecedented demand for computing resources. Consequently, there is an urgent need to take advantage of modern computer architectures currently available as well as exascale supercomputers that are expected to come into production in the near future. On that account, we plan to develop high-performance codes to fully utilize heterogeneous machines equipped with massively parallel hardware accelerators, NVIDIA GPU and Intel Xeon Phi. Close collaborations with experimental and computer science groups will be part of the proposed research to make advances in this highly specialized field. The expected overall impact of this innovative proposal is that it will 1) fundamentally advance our understanding of protein-drug interaction networks and 2) use this knowledge along with cutting-edge computing technology to support the development of novel therapies.