Collaborative Research: CDS&E: Theory and Methods for Implicit Molecular Solvation in Ligand and Ion Binding

合作研究：CDS

• 批准号: 2102595
• 负责人: David Case
• 金额: $ 11.15万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2102595&HistoricalAwards=false
• 关键词: Collaborative; Research; CDS& Theory; Methods

Tyler Luchko of California State University, Northridge (CSUN) and David Case of Rutgers University are supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to reduce the computational cost of calculating the binding of small molecules and ions to proteins, DNA and RNA. The ability of computer models to make accurate quantitative predictions about how effectively molecules bind with (or “stick” to) each other in a liquid environment is key to understanding the healthy functioning of biological systems or the structure and stability of advanced materials. But the models required to achieve the desired accuracy often come with impractical computational costs. To address this, Drs. Luchko and Case will develop methods to improve the computational efficiency of predicting the tight binding of drug-like molecules to biological and non-biological targets and also the diffuse (“cloud-like”) binding of ions around DNA and RNA. They will be pursuing new algorithms that offer a high degree of accuracy in the required calculations while also lowering the computational cost, by taking advantage of advancing hardware options, like GPUs, to speed up calculations. Both aspects will require iterative testing and tailoring to achieve maximum effectiveness. These methods and software will be freely available for broad application as part of the AmberTools molecular modeling suite. This work will directly involve students at CSUN, of whom over 50% are from traditionally underserved groups, thereby contributing to the training of a diverse STEM workforce.Central to the methods that will be developed is the 3D reference interaction site model (3D-RISM) of molecular liquids. 3D-RISM is a potentially powerful tool for molecular modeling, as it rapidly provides equilibrium solvent density distributions and accurate solvation free energies; however, its computational cost is too high for many molecular binding applications at present. To preserve the strengths of 3D-RISM while reducing the overall computational load, Drs. Luchko and Case will develop methods to address the specific challenges of the tight binding of small, drug-like molecules and the diffuse binding of ions around DNA and RNA. For small, drug-like molecules, he and his group will create a thermodynamic cycle that employs fast implicit solvent methods to efficiently include sampling, followed by a 3D-RISM correction. For diffuse ion binding, they will optimize ion force field parameters and solvers for 3D-RISM to predict the stability of DNA and RNA at different concentrations of divalent ions. In addition, they will implement 3D-RISM on GPUs, to directly reduce the computational cost of the model for all 3D-RISM for these and all other applications. Combined, these objectives will provide easy-to-use tools and solvent models for calculating binding free energies that do not sacrifice speed or accuracy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

加州州立大学北岭分校（CSUN）的泰勒卢奇科和罗格斯大学的大卫凯斯得到了化学系化学理论、模型和计算方法项目的一个奖项的支持，以减少计算小分子和离子与蛋白质、DNA和RNA结合的计算成本。计算机模型能够准确地定量预测分子在液体环境中如何有效地相互结合（或“粘”在一起），这是理解生物系统健康功能或先进材料结构和稳定性的关键。但是，实现所需精度所需的模型通常会带来不切实际的计算成本。为了解决这个问题，Luchko博士和Case博士将开发方法来提高预测药物样分子与生物和非生物靶标的紧密结合以及DNA和RNA周围离子的扩散（“云状”）结合的计算效率。他们将寻求新的算法，在所需的计算中提供高度的准确性，同时通过利用先进的硬件选项（如GPU）来加快计算速度，从而降低计算成本。这两个方面都需要反复测试和调整，以实现最大的有效性。这些方法和软件将作为AmberTools分子建模套件的一部分免费提供广泛应用。这项工作将直接涉及CSUN的学生，其中超过50%来自传统上服务不足的群体，从而有助于培养多样化的STEM劳动力。将开发的方法的核心是分子液体的3D参考相互作用位点模型（3D-RISM）。3D-RISM是一种潜在的强大的分子建模工具，因为它可以快速提供平衡溶剂密度分布和准确的溶剂化自由能，但是，它的计算成本太高，目前许多分子结合的应用。为了保持3D-RISM的优势，同时减少整体计算负荷，Luchko和Case博士将开发方法来解决小药物样分子的紧密结合以及DNA和RNA周围离子的扩散结合的具体挑战。对于小的药物样分子，他和他的团队将创建一个热力学循环，该循环采用快速隐式溶剂方法来有效地包括采样，然后进行3D-RISM校正。对于扩散离子结合，他们将优化3D-RISM的离子力场参数和求解器，以预测DNA和RNA在不同二价离子浓度下的稳定性。此外，他们还将在GPU上实现3D-RISM，以直接降低这些和所有其他应用程序的所有3D-RISM模型的计算成本。结合起来，这些目标将提供易于使用的工具和溶剂模型，用于计算结合自由能，而不牺牲速度或准确性。该奖项反映了NSF的法定使命，并已被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。