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Computer-aided Design of Anti-cancer Drugs Targeting Protein Kinases

计算机辅助设计靶向蛋白激酶的抗癌药物

基本信息

批准号：
7117086
负责人：
Chung F. Wong
金额：
$ 21.77万
依托单位：
UNIVERSITY OF MISSOURI-ST. LOUIS
依托单位国家：
美国
项目类别：
财政年份：
2006
资助国家：
美国
起止时间：
2006-07-01 至 2010-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7117086
关键词：
computers kinase inhibitor model neoplasm /cancer protein kinase

项目摘要

DESCRIPTION (provided by applicant): Protein kinases have become important targets for the design of anti-cancer drugs. Several protein kinase drugs such as Herceptin, Gleevec, and IRESSA have already been approved for treating several forms of cancer. These drugs act on three different protein kinases. Because there are more than 500 protein kinases in human and their various mutant forms are relevant for treating different diseases, there should be more protein kinase-related targets suitable for drug development. The long-term goal of this research is to develop an efficient computational model to help speed up the discovery and development of therapeutic drugs targeting these proteins. The immediate goals of this application aim at further developing a unique five-tier hierarchical computational screening model that balances theoretical rigor and speed at different tiers to shorten the time and expense needed to select the most promising drug candidates from large chemical libraries and to further optimize them for potency, selectivity and drug-like properties. Specific Aim 1 develops a sophisticated continuum solvent model for predicting cLogP and uses it with Lipinski's Rules of Five to evaluate compounds for drug-like properties. Specific Aim 2 improves virtual screening by incorporating protein flexibility in molecular docking and develops an enhanced replica exchange method for refining docking structure for more reliable binding affinity calculation. Specific Aim 3 extends our previous informatics/structural modeling approach to help guide the development of selective protein kinase inhibitors that have fewer side effects. It involves building structural models of the whole family of human protein kinases and on using enhanced conformational sampling techniques (developed in Specific Aim 2) to refine these models well enough for molecular docking and binding affinity calculation. We will also use this whole set of structural models to perform comprehensive comparative analysis to understand better how selectivity can be achieved by protein kinase inhibitors that target the ATP-binding pocket. Specific Aim 4 evaluates the validity of a popular pharmacophore model by performing energy component analysis on all the protein kinase-inhibitor complexes in the Protein Data Bank in order to justify its use in drug design. Specific Aim 5 applies this refined five-tier hierarchical model to identify new anti-cancer drug candidates for the protein kinase targets EGFR, HER2, CDK2, and BCR-ABL.

描述(申请人提供)：蛋白激酶已成为抗癌药物设计的重要靶点。几种蛋白激酶药物，如赫赛汀、格列卫和易瑞沙，已经被批准用于治疗几种形式的癌症。这些药物作用于三种不同的蛋白激酶。由于人类有500多种蛋白激酶，它们的各种突变形式与治疗不同的疾病有关，因此应该有更多的蛋白激酶相关靶点适合于药物开发。这项研究的长期目标是开发一种有效的计算模型，以帮助加快针对这些蛋白质的治疗药物的发现和开发。这项应用的近期目标是进一步开发一种独特的五级分级计算筛选模型，该模型在不同级别上平衡理论的严谨性和速度，以缩短从大型化学库中选择最有希望的候选药物所需的时间和费用，并进一步优化它们的效力、选择性和类药物特性。特定目标1开发了用于预测cLogP的复杂的连续介质溶剂模型，并将其与Lipinski的五规则一起用于评估化合物的类药物特性。特定目的2通过在分子对接中加入蛋白质的灵活性来改进虚拟筛选，并开发了一种增强的副本交换方法来精炼对接结构，以便更可靠地计算结合亲和力。特定目标3扩展了我们以前的信息学/结构建模方法，以帮助指导副作用较少的选择性蛋白激酶抑制剂的开发。它包括建立整个人类蛋白激酶家族的结构模型，并使用增强的构象采样技术(在特定目标2中开发)来完善这些模型，以足够好地进行分子对接和结合亲和力计算。我们还将使用这一整套结构模型进行全面的比较分析，以更好地了解以ATP结合口袋为靶点的蛋白激酶抑制剂如何实现选择性。特定目的4通过对蛋白质数据库中所有蛋白激酶抑制物复合体进行能量成分分析来评估一个流行的药效团模型的有效性，以证明其在药物设计中的使用合理性。特定目标5应用这种精细化的五层分层模型来确定针对蛋白激酶靶标EGFR、HER2、CDK2和BCR-ABL的新的抗癌药物候选药物。