Open data-driven infrastructure for building biomolecular force fields for predictive biophysics and drug design

开放数据驱动的基础设施，用于构建用于预测生物物理学和药物设计的生物分子力场

• 批准号: 10592758
• 负责人: Michael R Shirts
• 金额: $ 1.08万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10592758
• 关键词: Area; Bayesian Analysis; Binding; Biological; Biology; Biophysical Process; Biophysics; Chemicals; Chemistry; Computer Simulation; Computer software; DNA; Data; Data Set; Development; Disease; Drug Design; Error Sources; Generations; Goals; Infrastructure; Learning; Life; Modeling; Modernization; Modification; Molecular; Nucleic Acids; Occupations; Performance; Pharmaceutical Preparations; Property; Proteins; RNA; Readability; Reproducibility; Research; Scheme; Science; Scientist; Techniques; Technology; Uncertainty; behavior influence; biophysical model; design; drug discovery; improved; machine learning method; macromolecule; molecular mechanics; next generation; novel therapeutics; nucleic acid-based therapeutics; open data; physical model; quantum; rational design; simulation; small molecule; tool; unnatural amino acids

PROJECT SUMMARY/ABSTRACT This Project Summary is unchanged from the original R01. The study of biomolecular interactions and design of new therapeutics requires accurate physical models of the atomistic interactions between small molecules and biological macromolecules. Over the least few decades, molecular mechanics force fields have demonstrated the potential that physical models hold for quantitative biophysical modeling and predictive molecular design. However, a significant technology gap exists in our ability to build force fields that achieve high accuracy, can be systemati- cally improved in a statistically robust manner, be extended to new areas of chemistry, can model post-translational and covalent modifications, are able to quantify systematic errors in predictions, and can be broadly applied across a high-performance software packages. In this project, we aim to bridge this technology gap to enable new gen- erations of accurate quantitative biomolecular modeling and (bio)molecular design for chemical biology and drug discovery. In Aim 1, we will produce a modern, open infrastructure to enable practitioners to rapidly and conve- niently construct and employ accurate and statistically robust physical force fields via automated machine learning methods. In Aim 2, we will construct open, machine-readable experimental and quantum chemical datasets that will accelerate next-generation force field development. In Aim 3, we will develop statistically robust Bayesian inference techniques to enable the auto- mated construction of type assignment schemes that avoid overfitting and selection of physical functional forms statistically justified by the data. This approach will also provide an estimate of the systematic error in predicted properties arising from uncertainty in parameters or functional form choices—generally the dominant source of error—to be quantified with little added expense. In Aim 4, we will integrate and apply this infrastructure to produce open, transferable, self-consistent force fields that achieve high accuracy and broad coverage for modeling small molecule interactions with biomolecules (including unnatural amino or nucleic acids and covalent modifications by organic molecules), with the ultimate goal of covering all major biomolecules. This research is significant in that the technology developed in this project has the potential to radically transform the study of biomolecular phenomena by providing highly accurate force fields with exceptionally broad chemical coverage via fully consistent parameterization of organic (bio)molecules. In addition, we will produce new tools to automate force field creation and tailoring to specific problem domains, quantify the systematic error in predictions, and identify new data for improving force field accuracy. This will greatly improve our ability to study diverse biophysical processes at the molecular level, and to rationally design new small-molecule, protein, and nucleic acid therapeutics. This approach will bring statistical rigor to the field of force field construction and application by providing a means to make data-driven decisions, while enhancing reproducibility by enabling it to become a rigorous and reproducible science using a fully open infrastructure and datasets.

项目摘要/摘要 该项目摘要与原始R01保持不变。生物分子相互作用和设计的研究 新的治疗剂需要准确的物理模型，使小分子与 生物大分子。在最少的几十年中，分子力学力领域已经证明了 物理模型具有定量生物物理建模和预测分子设计的潜力。然而， 我们建立高准确性的力场的能力存在着一个显着的技术差距，可以是系统的 以统计稳健的方式改进呼叫，扩展到新的化学领域，可以建模翻译后建模 和共价修改，能够量化预测中的系统错误，并且可以广泛应用 高性能软件包。在这个项目中，我们旨在弥合这一技术差距，以使新的Gen- 精确定量生物分子建模和（生物）化学生物学和药物的分子设计 发现。在AIM 1中，我们将建立一个现代开放的基础设施，以使从业者能够快速并传达 通过自动化的机器学习来构建并采用准确且统计上强大的物理力场 方法。在AIM 2中，我们将构建开放的机器可读实验和量子化学数据集 将加速下一代力领域的发展。在AIM 3中，我们将开发统计上强大的贝叶斯 推理技术以实现避免明显的类型分配方案的自动伴侣构建 并选择物理功能形式在统计学上是由数据合理的。这种方法还将提供 估计由参数或功能形式不确定性引起的预测属性中的系统误差的估计值 选择 - 从总体上讲，主要的误差来源 - 几乎没有增加费用。在AIM 4中，我们将 集成并应用此基础设施以产生开放的，可转移的，自符的力领域，达到高 用于建模与生物分子的小分子相互作用的准确性和广泛的覆盖范围（包括不自然 通过有机分子的氨基或核酸以及共价修饰），最终的目标是覆盖所有 主要的生物分子。 这项研究重要的是，该项目中开发的技术有可能从根本上转变 生物分子现象的研究通过提供高度准确的力场，具有异常广泛的化学 通过有机体（BIO）分子的完全一致的参数化覆盖范围。此外，我们将生产新工具 自动化力场创建和针对特定问题域的定制，量化预测中的系统误差， 并确定提高力场准确性的新数据。这将大大提高我们学习潜水的能力 分子水平的生物物理过程，并合理设计新的小分子，蛋白质和核酸 酸治疗。这种方法将使统计严格的统计局部建设和应用领域 通过提供一种方法来做出数据驱动的决策，同时通过使其成为一个 使用完全开放的基础架构和数据集进行了严格且可重复的科学。

项目成果

A transferable double exponential potential for condensed phase simulations of small molecules.

• DOI: 10.1039/d3dd00070b
• 发表时间: 2023-08-08
• 期刊: Digital discovery

Bayesian-Inference-Driven Model Parametrization and Model Selection for 2CLJQ Fluid Models.

• DOI: 10.1021/acs.jcim.1c00829
• 发表时间: 2022-02-28
• 期刊: JOURNAL OF CHEMICAL INFORMATION AND MODELING
• 影响因子: 5.6
• 作者: Madin, Owen C.;Boothroyd, Simon;Messerly, Richard A.;Fass, Josh;Chodera, John D.;Shirts, Michael R.
• 通讯作者: Shirts, Michael R.

Improving Force Field Accuracy by Training against Condensed-Phase Mixture Properties.

• DOI: 10.1021/acs.jctc.1c01268
• 发表时间: 2022-06-14
• 期刊: JOURNAL OF CHEMICAL THEORY AND COMPUTATION
• 影响因子: 5.5
• 作者: Boothroyd, Simon;Madin, Owen C.;Mobley, David L.;Wang, Lee-Ping;Chodera, John D.;Shirts, Michael R.
• 通讯作者: Shirts, Michael R.

Open Force Field Evaluator: An Automated, Efficient, and Scalable Framework for the Estimation of Physical Properties from Molecular Simulation.

• DOI: 10.1021/acs.jctc.1c01111
• 发表时间: 2022-06-14
• 期刊: JOURNAL OF CHEMICAL THEORY AND COMPUTATION
• 影响因子: 5.5
• 作者: Boothroyd, Simon;Wang, Lee-Ping;Mobley, David L.;Chodera, John D.;Shirts, Michael R.
• 通讯作者: Shirts, Michael R.

Open Force Field BespokeFit: Automating Bespoke Torsion Parametrization at Scale.

• DOI: 10.1021/acs.jcim.2c01153
• 发表时间: 2022-11-28
• 期刊: JOURNAL OF CHEMICAL INFORMATION AND MODELING
• 影响因子: 5.6
• 作者: Horton, Joshua T.;Boothroyd, Simon;Wagner, Jeffrey;Mitchell, Joshua A.;Gokey, Trevor;Dotson, David L.;Behara, Pavan Kumar;Ramaswamy, Venkata Krishnan;Mackey, Mark;Chodera, John D.;Anwar, Jamshed;Mobley, David L.;Cole, Daniel J.
• 通讯作者: Cole, Daniel J.