Computer-Aided Drug Design Targeting Protein Phosphorylation

作为长期目标的一部分，开发和应用计算方法来帮助药物靶向设计 蛋白激酶及其相关蛋白质的研究，本研究主要集中在蛋白激酶及其相关蛋白质的开发和应用， 系综对接方法，以及药物结合动力学的研究。 蛋白激酶仍然是本研究中药物发现的主要靶点。批准约60 作为药物的蛋白激酶抑制剂，主要用于治疗癌症，已经证明蛋白激酶 重要的药物靶点由于超过500种蛋白激酶存在于人类中，许多突变体正在驱动 疾病，可以通过靶向蛋白激酶开发更多的药物。 Specific Aim 1继续开发和应用集成对接方法进行药物发现。Aim 1a测试 假设从集合对接的分数或其衍生物可以预测肺癌是否 携带蛋白激酶的疾病驱动突变体的患者对批准的药物有反应。目标1b 继续验证使用机器学习来改进集合对接。验证将包括所有 为评估对接性能而开发的有用的Decoys-Enhanced目录中的蛋白质 方法.这些蛋白质的嵌入式对接/机器学习模型将提供给其他 科学家通过Web服务器EDock-ML。科学家可以向EDock-ML提交化合物， 该化合物具有活性的可能性。目的1c鉴定蛋白激酶c-MET的新药先导物， 在EDock-ML的帮助下。 具体目标2继续测试一种模拟方法的组合，用于快速识别化合物， 治疗有用的药物结合动力学，使用更多的实验数据，正在成为可用。它使用 操纵分子动力学（SMD）模拟，用于快速初步筛选化学文库， 通过昂贵但更严格的方法评估最有希望的子集，包括伞 抽样技术，马尔可夫状态模型和里程碑法。因为计算它仍然是一个挑战 分子模拟的绝对解离/缔合速率，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， 模拟将被用于破译药物从蛋白激酶解离的分子机制， 包括对两步解离机制的一般性的检查， 鉴定了解分子机制可以为设计具有治疗作用的药物提供提示 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，以更高的影响项目可以包括在内。

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.