Docking the Proteome for New Ligands and Functional Associations

对接蛋白质组以获得新配体和功能关联

• 批准号: 8911649
• 负责人: Trent E Balius
• 金额: $ 2.97万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8911649
• 关键词: ADRB2 gene; Aden; Biochemistry; CHES1 gene; Characteristics; Chemicals; Chemistry; Collaborations; Communities; Computing Methodologies; Databases; Development; Docking; Drug Targeting; Drug toxicity; Epidermal Growth Factor Receptor; G Protein-Coupled Receptor Genes; Geometry; Goals; Hand; Histamine; Ligands; Link; MAP Kinase Gene; MAPK14 gene; Mechanics; Methods; Metric; Modeling; Molecular; Nature; One-Step dentin bonding system; Pharmaceutical Preparations; Pharmacology; Proteins; Proteome; Retrospective Studies; Roentgen Rays; Structure; Techniques; Testing; Time; acrosin; base; drug mechanism; interest; public health relevance; small molecule; structural biology; tool

DESCRIPTION (provided by applicant): Two overarching goals in chemical and structural biology are finding ligands for every protein (Schreiber, S. L., Nat Chem Biol 2005), and identifying the functions and associations among proteins based on their structure. For the last decade, these goals have been pursued empirically (Gerlt, J. A.; et al., Biochemistry 2011). I will argue that there is a need for computation in both ligand discovery and functional association of proteins. I will also argue that such goals, correctly framed, are now becoming obtainable for computational methods, and that at scale they are essentially only pragmatic computationally. In the last five years, structure-based docking screens have seen two important advances. First, the technique has predicted new chemotypes for over 60 targets. Whereas docking retains key liabilities, and cannot rank order ligands, it can now, with some reliability, prioritize likely molecules as candidates. Second, the technique has become fast. I will show that the method can screen the structurally accessible and interesting proteome-about 4000 targets-in four months. A second development is the advent of chemoinformatics techniques to compare targets by ligand similarity, rather than the structural or sequence similarity. It turns out that ligand similarity reveals associations that are curiously opaque to sequence and structure, and this has been used to make surprising and high-profile predictions of drug off-targets (Keiser, M. J.; et al., Relating protein pharmacology by ligand chemistry, Nat Biotech 2007,25(2), 197-206), drug mechanism of action (Gregori-Puigjane, E.; et al., Proc Natl Acad Sci U S A 2012; Keiser, M. J.; et al., Nature 2009), and drug toxicities (Lounkine, E.; et al., Nature 2012). Very recently the technique has been expanded to reorganize the GPCR dendogram (Lin, H.; et al., Nature Methods 2013, accepted), and I will take that tack to a whole proteome level by relating docking hit lists to associate proteins. The docking hit lists and the associations among them will enable three questions: "What compounds are available that might modulate my target", "what other targets might my target be associated with?" and, when appropriate, "what is the function of my target?" All results will be made accessible online.

描述(申请人提供)：化学和结构生物学的两个主要目标是为每种蛋白质寻找配基(Schreiber，S.L.，NAT Chem Biol 2005)，并根据它们的结构确定蛋白质之间的功能和联系。在过去的十年里，这些目标一直是经验性的(Gerlt，J.A.；等人，生物化学，2011年)。这就做 认为在蛋白质的配基发现和功能结合方面都需要计算。我还会争辩说，这样的目标，如果框架正确，现在已经可以通过计算方法实现，而且从规模上讲，它们基本上只是实用的计算方法。在过去的五年中，基于结构的对接屏幕有两个重要的进展。首先，这项技术已经为60多个目标预测了新的化学类型。虽然对接保留了关键的责任，并且不能对有序配体进行排序，但它现在可以在一定程度上可靠地将可能的分子列为候选分子。其次，这项技术已经变得很快。我将展示这种方法可以在四个月内筛选出结构上可接近的和有趣的蛋白质组-大约4000个靶点。第二个发展是化学信息学技术的出现，它通过配体相似性而不是结构或序列相似性来比较靶标。事实证明，配体相似性揭示了对于序列和结构奇怪地不透明的关联，并且这已被用于对药物非靶标(Keiser，M.J.；等人，通过配体化学来关联蛋白质药理学，NAT Biotech，2007，25(2)，197-206)、药物作用机制(Gregori-Puigjane，E.；等人，Proc Natl Acad Sci U S A 2012；Keiser，M.J.；等人，《自然》，2009)以及药物毒性(Lounkine，E.；等人，《自然》，2012年)做出令人惊讶和高调的预测。最近，该技术已经扩展到重新组织GPCR树状图(Lin，H.；等人，Natural Methods 2013，Accept)，我将通过将对接命中列表与相关蛋白质联系起来，将这一方针提升到一个完整的蛋白质组水平。对接的命中列表和它们之间的关联将使三个问题成为可能：“有哪些化合物可以调节我的靶子”，“我的靶子可能与其他什么靶子相关？”在适当的时候，“我的目标有什么作用？”所有结果都将在网上公布。