Characterizing the Mechanism of Cancer-Causing and Resistance Mutation of EGFR

EGFR 致癌及耐药突变机制的表征

• 批准号: 7488156
• 负责人: Trent E Balius
• 金额: $ 2.86万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-05-24 至 2013-05-23
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7488156
• 关键词: Affect; Affinity; Algorithms; Amber; Amino Acids; Antineoplastic Agents; Behavior; Binding; Binding Sites; Cancer Etiology; Carcinoma; Computational Technique; Computer Simulation; Computing Methodologies; Development; Docking; Drug Design; Drug resistance; EGFR Protein Overexpression; Epidermal Growth Factor Receptor; Erlotinib; Gefitinib; Goals; Individual; Lead; Libraries; Ligand Binding; Ligands; Methods; Modification; Molecular; Molecular Conformation; Molecular Target; Mutation; Non-Small-Cell Lung Carcinoma; Outcome; Patients; Pharmaceutical Preparations; Phosphotransferases; Play; Point Mutation; Positioning Attribute; Public Health; Research; Resistance; Role; Score; Screening procedure; Simulate; Software Tools; Solid Neoplasm; Specificity; Structural Models; Structure; Structure-Activity Relationship; Techniques; Therapeutic; Tyrosine Kinase Domain; base; chemotherapeutic agent; computerized tools; design; doxorubicin/mercaptopurine/prednisone/vincristine protocol; drug development; functional group; high throughput screening; improved; inhibitor/antagonist; insight; molecular dynamics; mutant; novel; scaffold; simulation; small molecule; therapeutic target; virtual

DESCRIPTION (provided by applicant): Broad, long-term objectives: The overexpression of epidermal growth factor receptor (EGFR) is observed in several types of solid tumors including non-small cell lung cancer and many carcinomas. We are using computational techniques to simulate binding of ligands known to interact with the ATP binding site on the kinase domain of EGFR in an effort to develop improved small molecules. Specifically, we are evaluating cancer causing EGFR mutations that include either a point mutation or a deletion on the activation loop that is positioned near the ATP binding site and the effect of these mutations on ligand binding. Other mutations, which arise as resistant mutations during treatment with compounds such as erlotinib and gefitinib will also be characterized. In order to develop small molecule inhibitors with specificity and high affinity for mutants, we will complete the following specific aims: (1) Characterize how mutations affect EGFR ligand binding using molecular dynamics followed by binding energy analysis. We will determine the behavior of mutations on ligand binding affinities, which amino acids in the binding pocket play an important role in the interaction between EGFR and each ligand, and the effect of each mutation on the active and inactive conformations of EGFR. (2) Design improved small molecule inhibitors based on computational analyses performed under aim #1 by ligand modification to improve affinity towards a specific mutation. We will use the scaffolds of the known inhibitors as well as that of the natural substrate to optimize binding affinity by performing functional r-group library searches. (3) Identify new lead structures using computational tools including virtual high throughput screening (docking) of ligand libraries. Again, we will optimize top scoring and populated structures using r-group library searches. The proposed aims will use the following computational methods: MD simulations, rigid and flexible docking using the anchor and grow algorithm; structural activity relationships and r-group libraries searches (rotamers). Methods for achieving the stated goals: The proposed aims use the following tools and software: AMBER, VMD, NAMD, MOE, Dock, BOSS, and BOMB. Relevance to Public health: Structure-based drug design is an important component of targeted drug development. The research proposed will characterize EGFR kinase domain mutations and their binding with known ligands using computer simulations. The use of the information generated will provide insight into the design of new ligands based on existing scaffolds and novel leads effective against EGFR mutants. Targeted design of new chemotherapeutic agents with high specificity and affinity will enable improved outcomes for patients with solid tumors that express mutant EGFR.

描述（由申请人提供）：广泛的长期目标：表皮生长因子受体（EGFR）的过表达在几种类型的实体瘤中观察到，包括非小细胞肺癌和许多癌。我们正在使用计算技术来模拟已知与EGFR激酶结构域相互作用的配体的结合，以开发改善的小分子。具体而言，我们正在评估引起EGFR突变的癌症，该突变包括点突变或在ATP结合位点附近的激活环上的缺失以及这些突变对配体结合的影响。其他化合物（例如厄洛替尼和吉非替尼）在治疗过程中以抗性突变形成的其他突变也将被表征。为了开发具有特异性和高亲和力的小分子抑制剂， 我们将完成以下特定目标：（1）表征突变如何影响EGFR配体结合 使用分子动力学，然后使用结合能分析。我们将确定突变对配体结合亲和力的行为，在结合口袋中的氨基酸在EGFR和每种配体之间的相互作用中起着重要作用，并且每个突变对EGFR的活动性和非活性构象的影响。 （2）根据配体修饰在AIM＃1下进行的计算分析，改善了小分子抑制剂，以提高对特定突变的亲和力。我们将使用已知抑制剂的支架以及天然底物的支架来通过执行功能性R组库搜索来优化结合亲和力。 （3）使用计算工具，包括配体库的虚拟高吞吐量筛选（对接），确定新的铅结构。同样，我们将使用R组库搜索来优化最高的评分和填充结构。提出的目标将使用以下计算方法：使用锚和生长算法的MD模拟，刚性和柔性对接；结构活动关系和R组库搜索（Rotamers）。实现既定目标的方法：拟议的目的使用以下工具和软件：琥珀，VMD，NAMD，MOE，DOCK，BOSS和BOMB。 与公共卫生的相关性：基于结构的药物设计是靶向药物开发的重要组成部分。提出的研究将表征EGFR激酶结构域突变及其使用计算机模拟与已知配体的结合。生成的信息的使用将根据现有的脚手架和针对EGFR突变体有效的新铅提供对新配体设计的见解。具有高特异性和亲和力的新化学治疗剂的有针对性设计将使表达突变EGFR的实体瘤患者的预后改善。