A metabolic code for cell signaling and polypharmacology.

细胞信号传导和多药理学的代谢密码。

• 批准号: 9234409
• 负责人: Matthew J O'Meara
• 金额: $ 5.92万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-01 至 2018-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9234409
• 关键词: Acetylcholine; Adverse effects; Antipsychotic Agents; Binding; Binding Sites; Bioinformatics; Biological; Biological Process; Biology; Cells; Chemistry; Code; Collaborations; Databases; Disease; Estrogens; Evolution; Family; Future; G-Protein-Coupled Receptors; Genomics; Guilt; Hour; In Vitro; Ion Channel; Joints; Letters; Ligands; Link; Maps; Measures; Mediating; Metabolic; Metabolite Interaction; Methods; Modeling; Molecular; Molecular Evolution; Network-based; Nuclear Hormone Receptors; Pharmaceutical Preparations; Phenotype; Proteins; Proteome; Proteomics; Reagent; Recruitment Activity; Regulation; Serotonin; Side; Signal Transduction; Signaling Molecule; Signaling Protein; Terfenadine; Testing; Time; Treatment Efficacy; Vocabulary; Work; base; cost; design; functional genomics; insight; metabolome; millisecond; novel; novel therapeutics; protein metabolite; protein protein interaction; public health relevance; receptor; single molecule; small molecule; success

 DESCRIPTION (provided by applicant): Drugs typically interact with multiple targets (polypharmacology), explaining not only their side effects but also their efficacy. The aim of this proposal is to explain the mechanism for polypharmacology in terms of molecular evolution and exploit this insight to infer new signaling networks and design new drug leads. A motivating idea is that biological signaling networks have evolved to use a small vocabulary of essentially fixed, endogenous signaling molecules (serotonin, acetylcholine, estrogen, etc.). This causes proteins to have a degenerate repertoire of small molecule binding sites, which drugs 'discover' through polypharmacology. To read this metabolic code, we have developed a robust method (SEA) to measure when two proteins share similar ligands. Using it, we have shown that synthetic ligands of close to 500 non-GPCRs resemble those of 150 GPCRs and we have shown that we can accurately predict novel side-effect of existing drugs. I argue that 1) protein-metabolic interactions explain the success of ligand similarity networks, 2 ligand similarity associations largely cannot be explained by other bioinformatics networks, and 3) ligand similarity can be used to predict sets of targets that can be activated by a single synthetic ligand. To test, I will 1) use ligand similarity networks to predict and experimentally test that novel set of sequence un-related targets interact with common endogenous signaling metabolites. I will 2) quantify the functional complementarity and intersection between ligand- similarity networks and sequence-similarity, co-expression, and protein-protein interaction networks. I will 3) experimentally test that synthetic compounds and endogenous metabolites jointly activate ligand similar targets prioritizing by co-expression, and co-annotated for biological function, phenotype, and disease.

 描述（由申请人提供）：药物通常与多个靶点相互作用（多药理学），不仅解释了它们的副作用，还解释了它们的疗效。的目的 建议是从分子进化的角度来解释多药药理学的机制，并利用这一见解来推断新的信号网络和设计新的药物先导。 一个令人鼓舞的想法是，生物信号网络已经进化到使用基本上固定的内源性信号分子（血清素，乙酰胆碱，雌激素等）的小词汇。这导致蛋白质具有小分子结合位点的简并库，药物通过多药理学“发现”。 为了读取这种代谢密码，我们开发了一种强大的方法（SEA）来测量两种蛋白质何时共享相似的配体。使用它，我们已经表明，近500个非GPCR的合成配体类似于150个GPCR的合成配体，并且我们已经表明，我们可以准确地预测现有药物的新副作用。 我认为，1）蛋白质-代谢相互作用解释了配体相似性网络的成功，2配体相似性关联在很大程度上不能被其他生物信息学网络解释，3）配体相似性可以用来预测可以被单个合成配体激活的靶点集。 为了测试，我将1）使用配体相似性网络来预测和实验测试一组新的序列不相关的靶标与常见的内源性信号代谢物相互作用。我将2）量化配体相似性网络与序列相似性、共表达和蛋白质-蛋白质相互作用网络之间的功能互补性和交叉。我将3）实验性地测试合成化合物和内源性代谢物联合激活通过共表达优先化的配体相似靶标，并且共同注释生物学功能、表型和疾病。