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: 8792154
• 负责人: Trent E Balius
• 金额: $ 5.42万
• 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/8792154
• 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; 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（由申请人提供）：化学和结构生物学的两个首要目标是找到每种蛋白质的配体（Schreiber，S. L.，Nat Chem Biol 2005），并基于蛋白质的结构鉴定蛋白质之间的功能和关联。在过去的十年里，这些目标一直在经验上追求（Gerlt，J.A.;例如，Biochemistry 2011）。我会 认为有必要在两个配体的发现和蛋白质的功能协会计算。我还将论证，这些目标，正确的框架，现在变得可获得的计算方法，并在规模上，他们基本上只是务实的计算。在过去的五年里，基于结构的对接屏幕已经取得了两项重要进展。首先，该技术已经预测了60多个目标的新化学型。尽管对接保留了关键的责任，并且不能对配体进行排序，但它现在可以以一定的可靠性优先考虑可能的分子作为候选分子。第二，技术变得快了。我将证明该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个目标。第二个发展是化学信息学技术的出现，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序列和结构来说奇怪的不透明的关联，并且这已被用于对药物脱靶进行令人惊讶和高调的预测（Keiser，M. J.道：例如，Relating protein pharmacology by ligand chemistry，Nat Biotech 2007，25（2），197-206），药物作用机制（Gregori-Puigjane，E.;例如，Proc Natl Acad Sci U S A 2012; Keiser，M. J.道：例如，Nature 2009）和药物毒性（Lounkine，E.;例如，Nature 2012）。最近，该技术已经扩展到重组GPCR树状图（Lin，H.;例如，Nature Methods 2013，已接受），我将通过将对接命中列表与相关蛋白质联系起来，将该策略应用于整个蛋白质组水平。对接命中列表和它们之间的关联将使三个问题成为可能：“有什么化合物可以调节我的目标”，“我的目标可能与什么其他目标相关联？在适当的时候，“我的目标的功能是什么？”“所有结果都将在网上公布。