INSPIRE: Computational Parameterization of Nucleic Acid Secondary Structure Models

INSPIRE项目由数学与物理科学理事会化学系化学理论、模型与计算方法项目、计算机与信息科学理事会计算与通信基础部门算法基础项目以及综合活动办公室INSPIRE项目共同资助。该项目推进了国家战略计算计划（NSCI）的目标，旨在维持和加强美国在高性能计算（HPC）研究、开发和部署方面的科学、技术和经济领导地位。DNA和RNA碱基配对（DNA中的A对与T配对，C对与G配对；RNA中的A对与U配对，C对与G配对）在生物体的生物回路中起着核心作用。这种碱基配对也为分子编程和合成生物学等新的工程学科提供了丰富的设计空间。在开始更昂贵和耗时的实验室研究之前，使用计算算法来设计和分析DNA或RNA链的碱基配对（二级结构）特性，极大地辅助了这些工程工作。从历史上看，这些计算的二级结构模型是基于几十年来零星进行的实验进行参数化的，因此很难改进模型（经过45年的努力仍然不完整）或扩展模型（对现代应用至关重要的新材料和实验条件）。与这种实验参数化方法有很大不同的是，所提出的工作将建立一个计算参数化框架，在这个框架中，将首次使用最先进的计算化学方法，对精心选择的一套小模型问题进行原子模拟，从而从头开始自动参数化新的二级结构模型。这一策略需要高水平的跨学科性，超出任何单个研究小组的能力，需要计算和算法专业知识来执行原子水平和二级结构水平的建模，以及实验专业知识来测试从新参数集预测的关键集成特性。这项工作将三个实验室（Miller, Pierce, Winfree）聚集在一起，横跨加州理工学院的三个部门（生物与生物工程，化学与化学工程，工程与应用科学），通过显著改进当前的二级结构模型，并通过创建可重复的，可改进的，可扩展的计算框架，用于生成未来的新模型。在未来的几十年里，这些进步所赋能的领域将产生变革性的分子和细胞技术，解决科学和社会面临的挑战，从神经科学和发展，到诊断和治疗，从可再生能源到可持续制造。核酸碱基配对的可编程化学是协调生命的电路以及分子编程和合成生物学等新兴工程学科的核心。现有的二级结构模型在分析和设计功能DNA和RNA系统方面具有很大的实用性，但目前的平衡参数集是不完整的，适用于有限的实验条件，并且难以扩展或改进，因为它们是基于45年来测量的经验参数。此外，迄今为止基本上没有测量到动力学参数。从这种零碎的实验方法出发，提出的工作将首次使用原子分子模拟来参数化平衡和动力学二级结构模型。最先进的计算化学方法将用于建立一个向前兼容的框架，用于自动化和可重复的参数生成，使研究人员能够从头开始重新运行参数化套件，为一组新的实验条件（盐，温度，变性剂）生成完整的参数集，为新的合成类似物(LNA, 2？或混合物质相互作用（DNA/RNA, RNA/2'OMe-RNA, RNA/ RNA），这些对现代原位和体内应用至关重要。计算框架将首先通过与现有经验模型中最仔细测量的DNA和RNA参数子集进行比较来验证，然后根据从实验文献中提取的关键平衡和动力学结果进行验证，或者在实验室中进行测量。

This INSPIRE project is jointly funded by the Chemical Theory, Models, and Computational Methods program in the Division of Chemistry in the Directorate for Math and Physical Science, the Algorithmic Foundations program in the Division of Computing and Communication Foundations in the Directorate for Computer & Information Science, and the INSPIRE program in the Office of Integrative Activities. This project advances the objectives of the National Strategic Computing Initiative (NSCI), an effort aimed at sustaining and enhancing the U.S. scientific, technological, and economic leadership position in High-Performance Computing (HPC) research, development, and deployment. DNA and RNA base-pairing (A pairs with T, C pairs with G for DNA; A pairs with U, C pairs with G for RNA) play central roles in the biological circuits that operate within living organisms. This base-pairing also offers a rich design space for the new engineering disciplines of molecular programming and synthetic biology. These engineering efforts are greatly assisted by the use of computational algorithms to design and analyze the base-pairing (secondary structure) properties of DNA or RNA strands before beginning more costly and time-consuming laboratory studies. Historically, the secondary structure models underlying these calculations have been parameterized based on experiments performed piecemeal over a period of decades, making it difficult to improve the models (still incomplete after 45 years of effort) or to extend the models (to new materials and experimental conditions critical to modern applications). Departing dramatically from this experimental parameterization approach, the proposed work will establish a computational parameterization framework, in which state-of-the-art computational chemistry methods with be used - for the first time - to perform atomistic simulations on a carefully chosen suite of small model problems, enabling automated parameterization of new secondary structure models from scratch. This strategy requires a high level of inter-disciplinarity beyond the capabilities of any individual research group, demanding computational and algorithmic expertise to perform modeling at both the atomistic level and the secondary structure level, and experimental expertise to test key ensemble properties predicted from new parameter sets. This effort draws together three laboratories (Miller, Pierce, Winfree) spanning three Caltech Divisions (Biology & Biological Engineering, Chemistry & Chemical Engineering, and Engineering & Applied Science) to achieve major impact on the molecular programming, synthetic biology, and life sciences research communities by dramatically improving current secondary structure models and by creating a repeatable, improvable, extensible computational framework for generating new models long into the future. Over the coming decades, the fields empowered by these advances are poised to generate transformative molecular and cellular technologies addressing challenges to science and society ranging from neuroscience and development, to diagnosis and treatment, and from renewable energy to sustainable manufacturing. The programmable chemistry of nucleic acid base pairing is central to the circuits that orchestrate life and to the emerging engineering disciplines of molecular programming and synthetic biology. Existing secondary structure models have great utility for analyzing and designing functional DNA and RNA systems, but current equilibrium parameter sets are incomplete, apply to a limited set of experimental conditions, and are difficult to extend or improve as they are based on empirical parameters measured over the course of 45 years. Furthermore, essentially no kinetic parameters have been measured to date. Departing from this piecemeal experimental approach, the proposed work will parameterize equilibrium and kinetic secondary structure models - for the first time - using atomistic molecular simulations. State-of-the-art computational chemistry methods will be used to set up a forward compatible framework for parameter generation that is automated and repeatable, enabling researchers to rerun the parameterization suite from scratch to generate an entire parameter set for a new set of experimental conditions (salt, temperature, denaturant), for new synthetic analogs (LNA, 2?OMe-RNA), or for mixed-material interactions (DNA/RNA, RNA/2'OMe-RNA, RNA/LNA) that are crucial for modern in situ and in vivo applications. The computational framework will initially be validated via comparison to the subset of DNA and RNA parameters that have been most carefully measured in existing empirical models, followed by validation with respect to key equilibrium and kinetic results extracted from the experimental literature, or measured in the laboratory.