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

EGFR 致癌及耐药突变机制的表征

基本信息

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
项目类别：
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

项目摘要

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、长期的目标：在多种类型的实体瘤中观察到表皮生长因子受体（EGFR）的过度表达，包括非小细胞肺癌和许多癌症。我们正在使用计算技术来模拟已知与 EGFR 激酶结构域上的 ATP 结合位点相互作用的配体的结合，以努力开发改进的小分子。具体来说，我们正在评估导致癌症的 EGFR 突变，包括位于 ATP 结合位点附近的激活环上的点突变或缺失，以及这些突变对配体结合的影响。在用厄洛替尼和吉非替尼等化合物治疗期间作为耐药突变出现的其他突变也将被表征。为了开发对突变体具有特异性和高亲和力的小分子抑制剂，我们将完成以下具体目标：（1）表征突变如何影响EGFR配体结合使用分子动力学，然后进行结合能分析。我们将确定突变对配体结合亲和力的行为，结合口袋中的哪些氨基酸在 EGFR 和每个配体之间的相互作用中发挥重要作用，以及每个突变对 EGFR 活性和非活性构象的影响。 (2) 根据目标#1 下进行的计算分析，通过配体修饰来设计改进的小分子抑制剂，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，通过执行功能性 r 基团库搜索来优化结合亲和力。 (3) 使用计算工具识别新的先导结构，包括配体库的虚拟高通量筛选（对接）。再次，我们将使用 r 组库搜索来优化最高得分和填充结构。所提出的目标将使用以下计算方法：MD模拟，使用锚定和生长算法的刚性和柔性对接；结构活性关系和 r 组库搜索（旋转异构体）。实现既定目标的方法：建议的目标使用以下工具和软件：AMBER、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。拟议的研究将使用计算机模拟来表征 EGFR 激酶结构域突变及其与已知配体的结合。使用生成的信息将有助于深入了解基于现有支架和有效对抗 EGFR 突变体的新先导的新配体的设计。具有高特异性和亲和力的新型化疗药物的靶向设计将能够改善表达突变 EGFR 的实体瘤患者的治疗结果。