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

数据挖掘和机器学习引导的酶反应 QM/MM 和 QM 簇建模

• 批准号: 10685949
• 负责人: Qianyi Cheng
• 金额: $ 32.74万
• 依托单位: UNIVERSITY OF MEMPHIS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10685949
• 关键词: Aromatic Amino Acids; Artificial Intelligence; Bacteria; Biochemical Reaction; Biomedical Engineering; Case Study; Chorismate Mutase; Communities; Computer Models; Computing Methodologies; Cytochrome P450; Development; Drug Design; Enzymes; Future; Goals; Knowledge; Machine Learning; Methodology; Methods; Modeling; Output; Pathway interactions; Play; Proteins; Protocols documentation; Publishing; Quantum Mechanics; Reaction; Research; Resources; Series; System; Techniques; Work; Xenobiotics; antibiotic design; data format; data mining; drug discovery; drug metabolism; enzyme model; enzyme structure; innovation; insight; machine learning method; machine learning model; metalloenzyme; model design; molecular mechanics; protein structure prediction; protonation; simulation

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.

项目总结/文摘

项目成果

The Glycine N-Methyltransferase Case Study: Another Challenge for QM-Cluster Models?

甘氨酸 N-甲基转移酶案例研究：QM 簇模型的另一个挑战？

• DOI: 10.1021/acs.jpcb.3c04138
• 发表时间: 2023
• 期刊: The journal of physical chemistry. B
• 作者: Cheng,Qianyi;DeYonker,NathanJ
• 通讯作者: DeYonker,NathanJ