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

项目总结/摘要 计算建模方法已广泛应用于蛋白质结构预测、药物代谢、生物医学等领域。 发现和酶生物工程，以提供对酶反应的原子级洞察， 功能协调发展的准确性和效率是推动新产品开发的两个目标。 方法在这个领域。然而，在实现高效率方面仍然缺乏方法上的最佳做法。 吞吐量和准确性。在量子力学/分子力学（QM/MM）和QM-集群 酶建模，一系列决策，如分子划分为QM和MM区域， 残基的质子化状态和计算设置依赖于对 问题和酶的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 更好更系统的解决方案。拟议的项目是创新的，因为它结合了a） 数据挖掘和机器学习对已发表的实验和计算工作， 有效和系统地收集研究知识; B）机器学习方法可以 权衡计算模型的不同组成部分，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QM/MM提供合理的策略， QM-集群模拟在未来不同蛋白质系统的研究，药物设计，甚至其他 科学研究领域。拟议的项目将集中在两个酶系统， 作为案例研究：a）分支酸突变酶，其是设计抗生素的潜在靶标 和B）金属酶的细胞色素P450超家族，其主要参与药物 通过各种反应机制进行代谢。

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.