CAREER: Innovation: The Three R's: A Model-Building Toolkit for Rational, Reproducible, and Rigorous Computational Enzymology

职业：创新：三个 R：合理、可重复且严格的计算酶学模型构建工具包

• 批准号: 1846408
• 负责人: Nathan DeYonker
• 金额: $ 74.21万
• 依托单位: University of Memphis
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1846408&HistoricalAwards=false
• 关键词: CAREER; Innovation; Three; Model; Building

Enzymology is the study of the structure, energetics, function, and chemical reactions of catalytic proteins. Atomic-scale computer modeling of enzymes is part of the multibillion-dollar research effort that aids the design of new pharmaceuticals, helps investigate protein structure and function, and advances our understanding of the molecular basis of disease. Despite the widespread use and success of computational enzymology, quantitative relationships between the composition / size of the models and accuracy of simulations are still poorly understood, and comparison of methodologies is nearly impossible. This project will design an automated protocol for the computational study of enzymes using rationally-created and reproducible models, where hypotheses can be rigorously tested via a data-driven approach. In keeping with the Division of Biological Infrastructure?s focus on empowering biological discovery by investing in the development and enhancement of biological research resources, the developed protocol will be made available via a web platform and user interface. In addition, the project will establish a laboratory module for introductory biology courses to familiarize undergraduate students with the Protein Data Bank, enzyme kinetics, and computer modeling. Rule discovery and model building automation will pave the way to a reproducible, rational, and rigorous approach to computational enzymology. Improved research and project design standards will allow, for the first time, a truly quantitative assessment of accuracy in biochemical simulations. This research will impact a large, multidisciplinary swath of the STEM research community, from structural biologists, pharmacologists, and computational chemists in academia and industry, to the next generation of biology and biochemistry undergraduates. The central goal of this project is to design an automated protocol for the computational study of enzymes using rationally-created and reproducible models, where hypotheses can be rigorously tested via a data-driven approach. Software design of an automated, rules-based software toolkit (RINRUS, short for Residue Interaction Network-based ResidUe Selector) will allow the computational study of enzymes at the atomic-level using reproducible and rationally-created models. RINRUS will guide research workflows by selecting crucial atoms in a protein structure to be included in computational models. RINRUS will then produce an enormous library of computationally tractable and chemically rigorous enzyme models, ready for production-quality simulations using molecular modeling software packages. Automated project design and standardization of research practices will allow the biochemical community to focus on higher-impact phenomena in protein structure and function. Community data sharing and calibration of enzyme models at an unprecedented scale will be facilitated by creation of a web-based repository and discussion forum. This research will create a fundamental, multidisciplinary shift, as computational and data scientists in several domains obtain an improved quantitative understanding of why their findings agree or disagree with experimental observation. Through novel, interactive lecture materials and a laboratory module, undergraduates in introductory biology and chemistry courses will be exposed to Nobel Prize-winning research and methodologies. These activities will forge bonds between introductory STEM courses and real-world scientific research to increase student attraction and retention, especially among underrepresented minority undergraduates in the STEM community. Results of this project can be found at www.memphis.edu/chem/faculty-deyonker/publications.php.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

酶学是研究催化蛋白质的结构、能量学、功能和化学反应的学科。酶的原子级计算机建模是数十亿美元研究工作的一部分，有助于设计新药，帮助研究蛋白质结构和功能，并促进我们对疾病分子基础的理解。尽管计算酶学的广泛使用和成功，模型的组成/大小和模拟的准确性之间的定量关系仍然知之甚少，方法学的比较几乎是不可能的。该项目将使用合理创建和可重复的模型设计酶计算研究的自动化协议，其中假设可以通过数据驱动的方法进行严格测试。与生物基础设施部门保持一致？通过投资于生物研究资源的开发和增强，该公司专注于增强生物发现，开发的协议将通过网络平台和用户界面提供。此外，该项目将建立一个实验室模块，介绍生物学课程，熟悉蛋白质数据库，酶动力学和计算机建模的本科生。规则发现和模型构建自动化将为计算酶学的可重复、合理和严格的方法铺平道路。改进的研究和项目设计标准将首次允许对生化模拟的准确性进行真正的定量评估。这项研究将影响STEM研究界的一个大型多学科领域，从学术界和工业界的结构生物学家，药理学家和计算化学家，到下一代生物学和生物化学本科生。该项目的中心目标是设计一个自动化的协议，用于使用合理创建和可重复的模型进行酶的计算研究，其中假设可以通过数据驱动的方法进行严格的测试。一个自动化的、基于规则的软件工具包（RINRUS，基于残基相互作用网络的残基网络的缩写）的软件设计将允许使用可重复的和合理创建的模型在原子水平上对酶进行计算研究。RINRUS将通过选择包含在计算模型中的蛋白质结构中的关键原子来指导研究工作流程。然后，RINRUS将产生一个巨大的计算上易于处理和化学上严格的酶模型库，准备使用分子建模软件包进行生产质量模拟。自动化的项目设计和研究实践的标准化将使生物化学界能够专注于蛋白质结构和功能中影响更大的现象。社区数据共享和酶模型的校准在一个前所未有的规模将通过创建一个基于网络的存储库和讨论论坛。这项研究将创造一个根本性的多学科转变，因为几个领域的计算和数据科学家对他们的研究结果与实验观察一致或不一致的原因有了更好的定量理解。通过新颖的，互动的讲座材料和实验室模块，本科生在介绍生物学和化学课程将接触到诺贝尔奖获奖的研究和方法。这些活动将在STEM入门课程和现实世界的科学研究之间建立联系，以增加学生的吸引力和保留率，特别是在STEM社区中代表性不足的少数民族本科生中。该项目的结果可以在www.example.com上找到www.memphis.edu/chem/faculty-deyonker/publications.php.This奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

Nitrile regio-synthesis by Ni centers on a siliceous surface: implications in prebiotic chemistry

Ni在硅质表面上进行腈区域合成：对生命起源前化学的影响

• DOI: 10.1039/d2cc04361k
• 发表时间: 2022
• 期刊: Chemical Communications
• 影响因子: 4.9
• 作者: Fioroni, Marco;DeYonker, Nathan J.
• 通讯作者: DeYonker, Nathan J.

Cheminformatic quantum mechanical enzyme model design: A catechol-O-methyltransferase case study

化学信息量子力学酶模型设计：儿茶酚-O-甲基转移酶案例研究

• DOI: 10.1016/j.bpj.2021.07.029
• 发表时间: 2021
• 期刊: Biophysical Journal
• 影响因子: 3.4
• 作者: Summers, Thomas J.;Cheng, Qianyi;Palma, Manuel A.;Pham, Diem-Trang;Kelso, Dudley K.;Webster, Charles Edwin;DeYonker, Nathan J.
• 通讯作者: DeYonker, Nathan J.

QM-Cluster Model Study of the Guaiacol Hydrogen Atom Transfer and Oxygen Rebound with Cytochrome P450 Enzyme GcoA

• DOI: 10.1021/acs.jpcb.0c10761
• 发表时间: 2021-03-30
• 期刊: JOURNAL OF PHYSICAL CHEMISTRY B
• 影响因子: 3.3
• 作者: Cheng, Qianyi;DeYonker, Nathan J.
• 通讯作者: DeYonker, Nathan J.

Siloxyl radical initiated HCN polymerization: computation of N-heterocycles formation and surface passivation

甲硅烷氧基自由基引发的 HCN 聚合：N-杂环形成和表面钝化的计算

• DOI: 10.1093/mnras/stac271
• 发表时间: 2022
• 期刊: Monthly Notices of the Royal Astronomical Society
• 影响因子: 4.8
• 作者: Fioroni, Marco;DeYonker, Nathan J.
• 通讯作者: DeYonker, Nathan J.

Evaluating the active site-substrate interplay between x-ray crystal structure and molecular dynamics in chorismate mutase

评估分支酸变位酶中 X 射线晶体结构和分子动力学之间的活性位点-底物相互作用

• DOI: 10.1063/5.0127106
• 发表时间: 2023
• 期刊: The Journal of Chemical Physics
• 作者: Summers, Thomas J.;Hemmati, Reza;Miller, Justin E.;Agbaglo, Donatus A.;Cheng, Qianyi;DeYonker, Nathan J.
• 通讯作者: DeYonker, Nathan J.