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: 9887804
• 负责人: Michael R Shirts
• 金额: $ 67.56万
• 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/9887804
• 关键词: Address; Area; Automobile Driving; Bayesian Analysis; Binding; Biological; Biology; Biophysical Process; Biophysics; Charge; Chemicals; Chemistry; Complex; Computer Simulation; Computer software; DNA; Data; Data Set; Databases; Development; Disease; Drug Design; Electrostatics; Ensure; Error Sources; Generations; Goals; Heart; Individual; Infrastructure; Investigation; Learning; Life; Measurement; Methods; Modeling; Modernization; Modification; Molecular; Nucleic Acids; Occupations; Perception; Performance; Pharmaceutical Preparations; Process; Property; Proteins; RNA; Readability; Reproducibility; Research; Roentgen Rays; Scheme; Science; Scientist; Specific qualifier value; Structure; System; Techniques; Technology; Temperature; Therapeutic; Thermodynamics; Training; Uncertainty; Validation; Work; base; behavior influence; biophysical model; chemical synthesis; cheminformatics; data infrastructure; design; drug discovery; experience; experimental study; improved; interest; machine learning method; macromolecule; models and simulation; molecular mechanics; multidisciplinary; new technology; next generation; novel therapeutics; nucleic acid-based therapeutics; open data; open source; physical model; physical property; quantum; simulation; simulation software; small molecule; software infrastructure; sound; tool; unnatural amino acids

PROJECT SUMMARY/ABSTRACT 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 systematically 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 generations of accurate quantitative biomolec- ular 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 conveniently 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 justfied 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 生物分子相互作用和新疗法设计的研究需要准确的物理模型 小分子与生物大分子之间的原子相互作用。至少几十年来， 分子力学力场已经证明了物理模型对定量的潜力 生物物理建模和预测分子设计。但是，我们的能力存在着重要的技术差距 为了建立能够实现高精度的力场，可以系统地以统计稳健的方式进行系统上的改进， 扩展到新的化学领域，可以模拟翻译后和共价修改，能够量化 预测的系统错误，可以在高性能软件包中广泛应用。 在这个项目中，我们旨在弥合这一技术差距，以使新一代的准确定量生物蛋白酶 - 化学生物学和药物发现的ULAR建模和（BIO）分子设计。在AIM 1中，我们将产生一个 现代开放的基础设施，使从业者能够快速，方便地构建和员工准确 并通过自动化的机器学习方法统计稳健的物理力领域。在AIM 2中，我们将构建 开放，可读的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，以实现自动 构造类型分配方案的构建，避免过度拟合和选择物理功能形式 从统计学上讲，数据是由数据所构成的。这种方法还将估算预测的系统错误 由参数或功能形式选择的不确定性引起的特性 - 通常是主要来源 错误 - 几乎没有增加费用来量化。在AIM 4中，我们将集成并应用此基础设施来生产 开放，转移，自洽的力场，可实现高精度和广泛的覆盖范围，以建模小 与生物分子的分子相互作用（包括非天然氨基或核酸以及通过 有机分子），其最终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。