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

描述（由申请人提供）：广泛的，长期的目标：在几种类型的实体肿瘤中观察到表皮生长因子受体（EGFR）的过表达，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。在厄洛替尼和吉非替尼等化合物治疗期间出现的其他突变也将被描述为耐药突变。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，

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.