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

项目摘要/摘要 本项目摘要与原始版本R01相同。生物分子相互作用的研究与设计 新疗法的发展需要精确的物理模型来描述小分子之间的原子相互作用 生物大分子。在至少几十年的时间里，分子力学力fi领域证明了 物理模型在定量生物物理建模和预测性分子设计方面的潜力。然而， 在我们建造高精度、可系统化的力fifi场的能力方面存在着显著的技术差距。 CALLY以统计稳健的方式改进，扩展到新的化学领域，可以模拟翻译后 和共价Modifi阳离子，能够量化预测中的系统误差，并可广泛应用于 一个高性能的软件包。在这个项目中，我们的目标是弥合这一技术差距，使新一代 化学生物学和药物的精确定量生物分子建模和(生物)分子设计的进展 发现号。在目标1中，我们将建立一个现代化、开放的基础设施，使从业者能够迅速和灵活地- 通过自动机器学习，巧妙地构建和使用准确且统计稳健的物理力fi场 方法：研究方法。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FORCEfi的发展。在目标3中，我们将开发统计稳健贝叶斯 支持类型赋值方案的自动匹配构造的推理技术，以避免过度fi设置 以及根据数据统计的物理函数形式的选择。这种方法还将提供一种 参数或函数形式不确定性引起的预测性质的系统误差的估计 选择-通常是主要的错误来源-被量化fi，几乎没有额外的费用。在目标4中，我们将 集成和应用这一基础设施，以产生开放、可转移、自洽的fi场，从而实现高 模拟小分子与生物分子(包括非自然的)相互作用的准确性和覆盖面广 氨基酸或核酸和有机分子的共价Modifi阳离子)，最终目标是覆盖所有 主要生物分子。 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。

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.