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

描述(申请人提供)：广泛的，长期的目的：表皮生长因子受体(EGFR)的过度表达在几种类型的实体肿瘤中观察到，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激活域上的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。具体地说，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。其他突变，在使用厄洛替尼和吉非替尼等化合物治疗期间出现的耐药突变也将被描述。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 用分子动力学方法进行了结合能分析。我们将确定突变在配体结合亲和力上的行为，结合口袋中的哪些氨基酸在EGFR与每个配体的相互作用中起重要作用，以及每个突变对EGFR活性构象和非活性构象的影响。(2)基于在AIM#1下执行的计算分析，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r-群库搜索来优化结合亲和力。(3)使用包括虚拟高通量筛选(对接)配体文库的计算工具来确定新的铅结构。同样，我们将使用r-group库搜索来优化得分最高的结构和填充的结构。拟议的AIMS将使用以下计算方法：MD模拟，使用锚定和增长算法的刚性和灵活性对接，结构活性关系和r基库搜索(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：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。这项研究将利用计算机模拟来表征EGFR激活域的突变及其与已知配体的结合。利用产生的信息将提供对基于现有支架的新配体和有效对抗EGFR突变体的新线索的设计的洞察。具有高特异性和亲和力的新化疗药物的靶向设计将使表达突变EGFR的实体肿瘤患者的预后得到改善。

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.