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Photoprobes for identifying potential anti-depressant and anti-anxiety medication

用于识别潜在抗抑郁和抗焦虑药物的光探针

基本信息

批准号：
8653024
负责人：
MICHAEL CASCIO
金额：
$ 19.8万
依托单位：
DUQUESNE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-04-17 至 2016-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8653024
关键词：
3-Dimensional Adverse effects Affinity Alkynes Antidepressive Agents Anxiety Autistic Disorder Azides Behavioral Benzophenones Binding Binding Sites Biochemical Cell Line Chemicals Chemistry Citalopram Clinical Computer Assisted Computer Simulation Coupled Couples Crystallization Data Development Disease Drug Design Drug Targeting Evaluation Fingerprint Fluoxetine Future Generations Goals Homologous Gene Knowledge Label Lead Leucine Libraries Ligand Binding Ligands Literature Major Depressive Disorder Maps Mass Spectrum Analysis Mental Depression Mental disorders Mission Modeling Molecular Molecular Models Mood Disorders Neurons Obsessive-Compulsive Disorder Outcome Peptides Pharmaceutical Preparations Photoaffinity Labels Protein Family Proteins Proteomics Public Health Research Research Personnel Sampling Selective Serotonin Reuptake Inhibitor Serotonin Site Site-Directed Mutagenesis Structure Synapses Techniques Testing Therapeutic Therapeutic Effect Tricyclic Antidepressive Agents Work base clinically relevant clinically significant crosslink design drug candidate drug discovery improved inhibitor/antagonist innovation member molecular modeling molecular transporter monoamine novel novel strategies novel therapeutics overexpression post-traumatic stress public health relevance receptor reuptake screening serotonin transporter success virtual

项目摘要

DESCRIPTION (provided by applicant): There is a fundamental gap in understanding how anti-depressants interact with their drug targets at the molecular level. Continued existence of this gap represents an important problem in terms of rationally designing or developing new therapeutics for mental illnesses. The long-term goal of our research is to understand how clinically relevant anti-depressant compounds interact with their biochemical targets at the molecular level. The objective of this application is to develop and utilize photoaffinity probes based on citalopram and fluoxetine, clinically significant compounds for treating depression and other mental illnesses, to map their corresponding binding sites and poses within the serotonin transporter (SERT). The central hypothesis is that these selective serotonin reuptake inhibitors (SSRIs) can be derivatized with a photoreactive group and a bioorthogonal tag for application of click chemistry proteomic techniques. After specific crosslinking, tagged SERT will be proteolyzed and mass fingerprinted, and crosslinked peptides will be analyzed in LC-MS/MS studies to identify the specific sites of covalent attachment of each probe. Our novel approach couples photoaffinity labeling with sensitive mass spectrometry (MS) to directly identify sites of interaction of photoprobes with the SERT. Identification of these sites will allow for refinement o our molecular modeling studies that map ligand-binding poses and sites within SERT. The rationale that underlies the proposed research is that, once it is known how SSRIs interact with the SERT, lead compounds can then be rationally manipulated as potential drug candidates for a host of mental illnesses. The central hypothesis will be tested by pursuing two specific aims: 1) Development of a library of SSRI-based photoprobes for labeling SERT; and 2) Identification of the irreversible attachment sites for the photoprobes within SERT. Under the first aim, citalopram and fluoxetine will be structurally modified to contain a photoreactive group (e.g., ary azide, benzophenone) and clickable tag (e.g., terminal alkyne, aliphatic azide), followed by SERT pharmacological evaluation to identify suitable photoprobes for proteomic characterization. Under the second aim, SERT photoaffinity labeling coupled with MS will identify specific drug-protein contacts for photoprobes developed in Aim 1. All results will be coupled with SERT molecular modeling in order to refine our computational models and accurately map the ligand-binding poses and sites for citalopram and fluoxetine within the transporter. The research is innovative because it uses a tandem photoaffinity labeling-bioorthogonal conjugation chemical proteomics approach to directly map the sites of SSRI interactions in SERT. The proposed research is significant, because it is expected to vertically advance and expand our understanding of how SSRIs, as clinically relevant anti-depressant compounds, interact with their major drug target at the molecular level. Ultimately, such knowledge has the potential to guide future ligand optimization of drug candidates for numerous SERT-implicated disorders (e.g., depression, anxiety, autism, obsessive- compulsive disorder) and refine SERT molecular models for computer-aided drug discovery efforts (i.e., virtual / in silico screening, structure-based drug design).

描述（由申请人提供）：对于抗抑郁药如何在分子水平上与其药物靶点相互作用的理解存在根本性的差距。这种差距的持续存在代表了合理设计或开发精神疾病新疗法的一个重要问题。我们研究的长期目标是了解临床相关的抗抑郁化合物如何在分子水平上与其生化靶标相互作用。本申请的目的是开发和利用基于西酞普兰和氟西汀（用于治疗抑郁症和其他精神疾病的临床重要化合物）的光亲和探针，以绘制它们在血清素转运蛋白（SERT）内的相应结合位点和姿势。中心假设是这些选择性血清素再摄取抑制剂（SSRI）可以用光反应基团和生物正交标签衍生化，用于点击化学蛋白质组学技术的应用。特定交联后，标记的 SERT 将被蛋白水解并进行质量指纹分析，交联肽将在 LC-MS/MS 研究中进行分析，以确定每个探针共价连接的特定位点。我们的新颖方法将光亲和标记与灵敏的质谱 (MS) 结合起来，以直接识别光探针与 SERT 的相互作用位点。这些位点的识别将有助于完善我们的分子模型研究，以绘制 SERT 内的配体结合位点和位点。这项研究的基本原理是，一旦了解 SSRIs 如何与 SERT 相互作用，就可以合理地操纵先导化合物作为治疗许多精神疾病的潜在候选药物。中心假设将通过追求两个具体目标进行检验：1）开发基于 SSRI 的光探针库，用于标记 SERT； 2) 识别 SERT 内光探针的不可逆附着位点。第一个目标是对西酞普兰和氟西汀进行结构修饰，使其包含光反应基团（例如芳基叠氮化物、二苯甲酮）和可点击标签（例如末端炔烃、脂肪族叠氮化物），然后进行 SERT 药理学评估，以确定适合蛋白质组表征的光探针。在第二个目标下，SERT光亲和标记与MS相结合将识别目标1中开发的光探针的特定药物-蛋白质接触。所有结果将与SERT分子模型相结合，以完善我们的计算模型并准确绘制转运蛋白内西酞普兰和氟西汀的配体结合位点和位点。该研究具有创新性，因为它使用串联光亲和标记-生物正交缀合化学蛋白质组学方法来直接绘制 SERT 中 SSRI 相互作用的位点。拟议的研究意义重大，因为它有望垂直推进和扩大我们对 SSRIs 作为临床相关抗抑郁化合物如何在分子水平上与其主要药物靶点相互作用的理解。最终，这些知识有可能指导未来针对众多 SERT 相关疾病（例如抑郁症、焦虑症、自闭症、强迫症）的候选药物的配体优化，并完善计算机辅助药物发现工作的 SERT 分子模型（即虚拟/计算机筛选、基于结构的药物设计）。