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 生物分子相互作用的研究和新疗法的设计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。在至少几十年的时间里， 分子力学力fi场已经证明了物理模型对于定量计算的潜力 生物物理模型和预测性分子设计。然而，我们的能力存在着明显的技术差距 为了构建实现高精度的力fi场，可以以统计稳健的方式系统地改进，BE 扩展到新的化学领域，可以对翻译后和共价的Modifi阳离子进行建模，能够量化 系统误差预测，并可广泛应用于高性能软件包。 在这个项目中，我们的目标是弥合这一技术差距，使新一代准确的定量生物油- 用于化学生物学和药物发现的空间建模和(生物)分子设计。在目标1中，我们将产生一个 现代化、开放的基础设施，使从业者能够快速方便地构建和使用准确的 和统计稳健的物理力fi领域通过自动机器学习方法。在目标2中，我们将构建 开放、机器可读的实验和量子化学数据集，将加速下一代力量 fi现场开发。在目标3中，我们将开发统计稳健的贝叶斯推理技术，以使自动- 避免过fi设置和物理函数形式选择的类型分配方案的配对构造 从统计上讲，这些数据只是fi。该方法还将提供对预测的系统误差的估计 由参数或函数形式选择的不确定性引起的特性--通常是 错误-在几乎不增加费用的情况下被量化fi。在目标4中，我们将集成并应用该基础设施来生产 开放、可转移、自洽的力fi场，可实现高精度和广泛的覆盖范围，适用于小型 分子与生物分子(包括非天然氨基酸或核酸和共价Modifi阳离子)的相互作用 有机分子)，最终目标是覆盖所有主要的生物分子。 这项研究的意义在于，该项目开发的技术具有从根本上改变的潜力 用非常广泛的化学物质提供高精度的力场fi来研究生物分子现象 通过有机(生物)分子的完全一致的参数化覆盖。此外，我们将生产新的工具来 自动创建和定制Forcefifi以特定问题域，量化预测中的系统误差， 并为提高测力fi场精度确定新的数据。这将极大地提高我们学习多样化的能力 分子水平上的生物物理过程，合理设计新的小分子、蛋白质和核 酸疗法。这种方法将给fifiFeld的构建和应用带来统计上的严谨性 通过提供一种做出数据驱动的决策的方法，同时通过使其成为 使用完全开放的基础设施和数据集的严谨和可重现的科学。