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: 10580156
• 负责人: Michael R Shirts
• 金额: $ 60.16万
• 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 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10580156
• 关键词: Acceleration; 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; Formulation; 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; behavior influence; biophysical model; chemical synthesis; cheminformatics; data infrastructure; design; drug discovery; experience; experimental study; improved; interest; machine learning method; macromolecule; mechanical force; 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; rational design; 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.

项目总结/摘要 生物分子相互作用的研究和新疗法的设计需要精确的物理模型， 小分子和生物大分子之间的原子相互作用。在过去的几十年里， 分子力学力场已经证明了物理模型在定量分析中的潜力。 生物物理建模和预测分子设计。然而，在我们的能力方面存在着重大的技术差距， 建立达到高精度的力场，可以以统计上稳健的方式系统地改进， 扩展到化学的新领域，可以模拟翻译后和共价修饰，能够量化 预测中的系统误差，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，使新一代的准确定量生物分子， 化学生物学和药物发现的模拟和（生物）分子设计。在目标1中，我们将生成一个 现代化的开放式基础设施，使从业人员能够快速方便地构建和使用准确的 通过自动化机器学习方法获得统计上稳健的物理力场。在目标2中，我们将构建 开放的，机器可读的实验和量子化学数据集，将加速下一代力量 外地发展。在目标3中，我们将开发统计上鲁棒的贝叶斯推理技术，以实现自动 避免过拟合和物理函数形式选择的类型分配方案的匹配构造 只是由数据统计的艾德。这种方法还将提供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性质-通常是 误差--几乎不增加费用就可以量化艾德。在目标4中，我们将集成并应用此基础架构， 开放、可转移、自洽的力场，可实现高精度和广泛的覆盖范围， 分子与生物分子（包括非天然氨基或核酸以及通过共价修饰）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，该项目开发的技术有可能从根本上改变 生物分子现象的研究，通过提供高度精确的力场与非常广泛的化学 通过有机（生物）分子的完全一致的参数化覆盖。此外，我们还将生产新的工具， 自动化力场创建和定制特定的问题域，量化预测中的系统误差， 并识别新数据以提高力场精度。这将大大提高我们的能力，研究多样化 在分子水平上的生物物理过程，并合理设计新的小分子，蛋白质和核酸 酸疗法这种方法将为力场的构建和应用领域带来统计上的严格性 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种 使用完全开放的基础设施和数据集的严谨和可重复的科学。