Data Mining and Machine Learning Guided QM/MM and QM-Cluster Modeling of Enzymatic Reactions

项目摘要/摘要 计算建模方法已被广泛应用于蛋白质结构预测、药物 发现和酶生物工程，以提供对酶反应和 功能。准确和高效是推动新技术发展的两个目标 这一领域的研究方法。然而，方法论上的最佳实践仍然缺乏，无法实现高 吞吐量和准确性。量子力学/分子力学(QM/MM)和QM-簇 酶模拟，一系列决策，如分子划分为QM和MM区， 残基的质子化状态和计算设置依赖于对 关于酶的问题和知识，以及可用的计算方法。在这 提出的项目，机器学习方法将应用于计算酶建模 寻求更好、更系统的解决方案。拟议的项目具有创新性，因为它结合了 关于已发表的实验和计算工作的数据挖掘和机器学习 高效和系统地收集用于研究的知识；b)机器学习方法可以 对计算模型的不同组成部分进行权衡，并自动做出最佳决策； C)这项工作的结果将为准确和有效的质量管理/管理提供合理的战略，并 QM团簇模拟在未来不同蛋白质系统、药物设计甚至其他研究中的应用 科学研究领域。拟议的项目将专注于两个酶系统，这两个系统将 作为案例研究：a)氯酸变位酶，这是设计抗生素的潜在目标 和b)细胞色素P450金属酶超家族，与药物有很大关系 通过不同的反应机制进行代谢。

Project Summary/Abstract Computational modeling methods have been widely applied in protein structure prediction, drug discovery and enzyme bioengineering to provide atomic-level insight into enzymatic reactions and functions. Accuracy and efficiency are the two goals that motivate the development of new methods in this field. However, the methodological best practices are still lacking in achieving high throughput and accuracy. In quantum mechanics/molecular mechanics (QM/MM) and QM-cluster enzyme modeling, series of decisions such as molecule partitioning into QM and MM regions, protonation states of residues, and computational setting rely on good understanding of the problem and knowledge of the enzyme as well as available computational methods. In this proposed project, machine learning methods will be applied in computational enzyme modeling for a better and more systematic solution. The proposed project is innovative as it combines a) data mining and machine learning on published experimental and computational works which will efficiently and systematically collect knowledge for research; b) machine learning methods can weigh different components of computational modeling and make optimal decisions automatically; c) the results of this work will provide a rational strategy for accurate and efficient QM/MM and QM-cluster simulations in future studies of different protein systems, drug design and even other scientific research domains. The proposed project will focus on two enzyme systems that will serve as case studies: a) Chorismate Mutase which is a potential target for designing antibiotics and b) the Cytochrome P450 superfamily of metalloenzymes which are largely involved in drug metabolism via various reaction mechanisms.