INSPIRE: Computational Parameterization of Nucleic Acid Secondary Structure Models

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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.