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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）：在理解抗抑郁药如何在分子水平上与其药物靶点相互作用方面存在根本性的差距。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方面的一个重要问题。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。本应用程序的目的是开发和利用基于西酞普兰和氟西汀的光亲和探针，在血清素转运体（SERT）中绘制相应的结合位点和姿势。西酞普兰和氟西汀是治疗抑郁症和其他精神疾病的临床重要化合物。中心假设是，这些选择性5 -羟色胺再摄取抑制剂（SSRIs）可以衍生与光反应基团和生物正交标签的点击化学蛋白质组学技术的应用。特异性交联后，标记的SERT将被蛋白水解和大量指纹，并在LC-MS/MS研究中分析交联肽，以确定每个探针的共价附着的特定位点。我们的新方法将光亲和标记与灵敏质谱（MS）相结合，直接识别光探针与SERT相互作用的位点。这些位点的鉴定将允许我们在SERT中绘制配体结合姿势和位点的分子建模研究的改进。这项研究的基本原理是，一旦知道了SSRIs是如何与SERT相互作用的，那么先导化合物就可以被合理地操纵，成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来验证：1)开发用于标记SERT的基于ssri的光探针库；2)确定SERT中光电探针的不可逆附着位点。在第一个目标下，西酞普和氟西汀将被结构修饰，以包含光反应基团（例如，任何叠氮化物，二苯甲酮）和可点击标签（例如，末端炔，脂肪族叠氮化物），然后进行SERT药理学评估，以确定适合的光探针进行蛋白质组学表征。在第二个目标下，SERT光亲和标记结合MS将为aim 1中开发的光探针识别特定的药物-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，并准确地绘制西酞普兰和氟西汀在转运体中的配体结合姿势和位点。这项研究具有创新性，因为它使用了串联光亲和标记-生物正交偶联化学蛋白质组学方法来直接绘制SERT中SSRI相互作用的位点。这项研究具有重要意义，因为它有望垂直推进和扩展我们对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许多SERT相关疾病（如抑郁症、焦虑症、自闭症、强迫症）的候选药物配体优化，并为计算机辅助药物发现工作（即虚拟/计算机筛选、基于结构的药物设计）完善SERT分子模型。