Omics data integration and analysis for structure-based multi-target drug design

摘要 全基因组关联研究、全基因组测序和高通量技术 产生了大量不同的组学和表型数据。然而，这些数据集还没有被 充分探索提高药物发现的有效性和效率，沿着一个方向继续-- 药物--单基因范式。因此，将一种药物推向市场的成本是惊人的，失败率也是惊人的 令人望而生畏。我们的长期目标是通过确定可靠的方法来重振落后的制药管道 为了实现精准医学。我们将通过开发一种新的结构系统来实现这一目标 药物发现的药理学方法，将基于结构的药物设计与异质药物设计相结合 在整个人类和病原体基因组和相互作用组的背景下进行组学数据集成和分析。 来自我们小组和其他人的越来越多的证据表明，大多数药物通常相互作用 具有多个受体(靶点)。强和弱的多个药物-靶点相互作用可以共同 通过生物分子的构象动力学调节药物的疗效、毒性和耐药性。按顺序 要合理设计有效、安全和精确的药物，我们面临着尚未解决的重大挑战之一 基于结构的药物设计：所有可能的蛋白质及其构象状态是什么 有机体中的药物？该提案试图通过开发、传播和 通过实验测试新的计算工具。在成功的初步结果的基础上，我们将发展 用于识别三维(3D)蛋白质-化学相互作用模型的集成计算管道 在细胞环境和结构蛋白质组的尺度上。具体地说，我们将开发一个第四级结构- 通过集成来自基因组学、蛋白质组学和 表型组学。我们将开发一种新的协作式单类协同过滤算法来推断缺失 多层网络中的关系。我们将结合结构生物信息学、生物物理学、 以及机器学习，以获得对药物作用的生物学见解。为了方便易用性和 算法的可重复性，我们将开发基于社区的网络资源，这些资源由我们的 过去开发蛋白质数据库(PDB)的经验。更重要的是，我们将与 实验实验室测试建议的计算工具，使用靶向激酶多元药理学作为 一个真实世界的例子，并迭代地提高算法、软件和Web的性能和可用性 服务。该项目的成功完成将为科学界提供：(1)新的方法 提高基于结构的多靶点药物设计的高通量筛选的范围和能力； (2)用户友好的网络服务，以支持基于社区的药物发现；及。(3)潜在的抗癌新药。 靶向治疗。总之，这些工具将通过提供一种 结构系统药理学工具包。

Abstract Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput techniques have generated vast amounts of diverse omics and phenotypic data. However, these sets of data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along the one- drug-one-gene paradigm. Consequently, the cost of bringing a drug to market is staggering, and the failure rate is daunting. Our long-term goal is to revive the lagging pharmaceutical pipeline by identifying robust methods for achieving precision medicine. We will achieve this goal by developing a novel structural systems pharmacology approach to drug discovery, which integrates structure-based drug design with heterogeneous omics data integration and analysis in the context of the whole human and pathogen genome and interactome. An increasing body of evidence from both our group and others suggests that most drugs commonly interact with multiple receptors (targets). Both strong and weak multiple drug-target interactions can collectively mediate drug efficacy, toxicity, and resistance through the conformational dynamics of biomolecules. In order to rationally design potent, safe, and precision medicine, we face one of the major unsolved challenges in structure-based drug design: what are all the possible proteins and their conformational states interacting with a drug in an organism? This proposal attempts to address this challenge by developing, disseminating, and experimentally testing novel computational tools. Based on our successful preliminary results, we will develop an integrated computational pipeline to identify three-dimensional (3D) protein-chemical interaction models in the cellular context and on a structural proteome scale. Specifically, we will develop a quaternary structure- centric multi-layered network model by integrating heterogeneous data from genomics, proteomics, and phenomics. We will develop a novel collaborative one-class collaborative filtering algorithm to infer missing relations in the multi-layered network. We will combine tools derived from structural bioinformatics, biophysics, and machine learning to gain biological insights into the drug action. To facilitate the usability and reproducibility of the proposed algorithms, we will develop community-based web resources established by our previous experiences in developing the Protein Data Bank (PDB). More importantly, we will work closely with experimental laboratories to test the proposed computational tools using targeted kinase polypharmacology as a real-world example, and iteratively improve the performance and usability of algorithm, software, and web services. The successful completion of this project will provide the scientific community with: (1) new methods to enhance the scope and capability of high-throughput screening for structure-based multi-target drug design; (2) a user-friendly web service to support community-based drug discovery; and (3) potential novel anti-cancer targeted therapeutics. Together, these tools will advance drug discovery and precision medicine by providing a structural systems pharmacology toolkit.