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Computer-Aided Drug Design Targeting Protein Phosphorylation

针对蛋白质磷酸化的计算机辅助药物设计

基本信息

批准号：
10436417
负责人：
Chung F. Wong
金额：
$ 46.95万
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：
2019
资助国家：
美国
起止时间：
2019-02-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10436417
关键词：
Antineoplastic Agents Automobile Driving Award Binding Biological Testing Cancer Patient Computer Assisted Computing Methodologies Data Defect Development Directories Disease Dissociation Docking Drug Design Drug Targeting Equilibrium Exposure to Future Genetic Goals Human KRP protein Kinetics Knowledge Learning Machine Learning Malignant Neoplasms Malignant neoplasm of lung Methodology Methods Modeling Molecular Mutation NIH Program Announcements Patients Performance Pharmaceutical Preparations Phosphorylation Phosphotransferases Probability Protein Family Protein Kinase Protein Kinase C Proteins Research Role Sampling Science Scientist Senior Scientist Speed Structure Students System Techniques Testing Therapeutic Time Training Translations Validation Work design drug candidate drug discovery drug response prediction improved innovation insight machine learning model member molecular dynamics mutant novel therapeutics precision medicine programs protein kinase inhibitor rapid technique rational design residence screening simulation small molecule inhibitor small molecule libraries tool undergraduate student web server

项目摘要

As part of the long-term goal to develop and apply computational methods to aid the design of drugs targeting protein kinases and related proteins, this research focuses on the development and application of the ensemble docking method, and on the study of drug-binding kinetics. Protein kinases continue to be the main targets for drug discovery in this research. The approval of about 60 inhibitors of protein kinases as drugs, mainly for treating cancer, has demonstrated protein kinases as important drug targets. As over 500 protein kinases are present in human and many mutants are driving diseases, many more drugs can be developed by targeting protein kinases. Specific Aim 1 continues to develop and apply the ensemble docking method to drug discovery. Aim 1a tests the hypothesis that scores, or their derivatives, from ensemble docking could predict whether lung cancer patients carrying disease-driving mutants of protein kinases are responsive to approved drugs. Aim 1b continues to validate the use of machine learning to improving ensemble docking. The validation will include all the proteins in the Directory of Useful Decoys-Enhanced developed for evaluating the performance of docking methods. Ensemble docking/machine learning models for these proteins will be made available to other scientists through the web server EDock-ML. Scientists can submit a compound to EDock-ML and receive the probability that the compound to be active. Aim 1c identifies new drug leads for the protein kinase c-MET with the aid of EDock-ML. Specific Aim 2 continues to test a combination of simulation methods for rapidly identifying compounds with therapeutically useful drug-binding kinetics, using more experimental data that are becoming available. It uses steered molecular dynamics (SMD) simulation for fast initial screening of chemical libraries, followed by evaluating the most promising subset by expensive but more rigorous methods, including the umbrella sampling technique, the Markov State Model, and the milestoning method. As it is still challenging to calculate absolute dissociation/association rate from molecular simulations, using several methods employing different approximations will help to draw robust and unbiased conclusions. After validation, the trajectories from the simulation will be used to decipher the molecular mechanisms of drug dissociation from protein kinases, including the examination of the generality of a two-step dissociation mechanism that has already been identified. Understanding the molecular mechanisms can give hint on the design of drugs with therapeutically useful drug-binding kinetics. The projects are designed to be performed by undergraduates. Senior scientists will work alongside the students often so that projects with higher impact can be included.

作为长期目标的一部分，开发和应用计算方法来辅助药物靶向的设计蛋白激酶及其相关蛋白的研究主要集中在蛋白激酶的开发和应用系综对接方法，以及药物结合动力学的研究。在这项研究中，蛋白激酶仍然是药物发现的主要靶点。大约60个人的批准作为药物的蛋白激酶抑制剂，主要用于治疗癌症，已证明蛋白激酶是重要的药物目标。由于人类体内存在500多种蛋白激酶，许多突变体正在推动对于疾病，可以通过靶向蛋白激酶来开发更多的药物。具体目标1继续开发和应用系综对接方法进行药物发现。AIM 1a测试假设来自整体对接的分数或其衍生品可以预测肺癌携带致病蛋白激酶突变的患者对批准的药物有反应。目标1b 继续验证机器学习对改进集成对接的使用。验证将包括所有用于评估对接性能的有用诱饵增强目录中的蛋白质方法：研究方法。这些蛋白质的整体对接/机器学习模型将提供给其他科学家通过网络服务器EDock-ML。科学家可以向EDock-ML提交化合物并收到该化合物具有活性的概率。Aim 1c发现蛋白激酶c-Met的新药物先导在EDock-ML的帮助下。特定目标2继续测试快速识别化合物的模拟方法组合治疗上有用的药物结合动力学，使用正在变得可用的更多实验数据。它使用引导分子动力学(SMD)模拟用于化学库的快速初始筛选，随后通过昂贵但更严格的方法评估最有希望的子集，包括雨伞抽样技术、马尔可夫状态模型和里程碑方法。因为它仍然是计算具有挑战性的来自分子模拟的绝对解离/缔合率，使用几种不同的方法近似值将有助于得出可靠和公正的结论。经过验证，来自模拟将被用来破译药物从蛋白激酶解离的分子机制，包括检查两步解离机制的一般性，该机制已经被确认身份。了解分子机制可以为药物的设计和治疗提供线索有用的药物结合动力学。这些项目是为本科生设计的。资深科学家将与学生经常这样做，这样就可以将影响更大的项目包括在内。