RUI: A Systematic, Computational Study of the Effect of Macromolecular Crowding on Electrostatic Interactions, Biomolecular Recognition, and Molecular Design

RUI：大分子拥挤对静电相互作用、生物分子识别和分子设计影响的系统计算研究

• 批准号: 1615313
• 负责人: Mala Radhakrishnan
• 金额: $ 24.76万
• 依托单位: Wellesley College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1615313&HistoricalAwards=false
• 关键词: RUI; Systematic; Computational; Study; Effect

In recent years, sophisticated computer modeling has enabled scientists to understand and address challenging problems in materials science, engineering, environmental science, and other fields. In order for a computational model to be effective, it must be accurate and efficient. In this project, the research team will evaluate, improve upon, and apply computational models used to study molecular interactions in biological environments. Specifically, they will focus on computational models used to study how interactions between biological molecules are affected by the physical nature of other molecules in their immediate environment. It has been shown that cells are highly crowded environments, and the nature of this "crowding" can significantly affect how crucial molecules interact with each other. A robust way to model such environments will allow for better predictions of cellular processes, which can have important impacts on society's collective understanding of biological systems and its ability to develop solutions when things go "wrong" in such systems. This research will be conducted at Wellesley College, an all-female undergraduate institution, catalyzing women's contribution to computational science, a discipline in which women have long been very underrepresented. The research team will also conduct outreach activities at a local diverse high school, introducing youth to the field of computational modeling of biological systems. The activities developed as part of this collaborative, outreach effort will be made available online to help excite students across the country about using computers and physical science to address real-world issues in biology.In more technical terms, the goal of the project is to study how macromolecular crowding within the cell affects electrostatic interactions and molecular recognition between biomolecules via a controlled set of computational models that are informed through experimental data. Through computation, the team will first study systems in which the interacting biomolecules and macromolecular crowders are "virtual", whose physical properties can be exhaustively and systematically sampled, in order to understand how molecular and crowding agent properties such as shape, size, and charge distribution can affect interaction energies. They will use multiple computational models, including ones that treat the solvent implicitly and explicitly, in order to assess the extent to which the method used to model the system affects the predictions made. The team will also simulate experimentally realizable biological systems, including DNA-protein and protein-protein complexes within crowded environments. Through a tight cycle of comparing simulation predictions with experimental outcomes, they will assess and improve computational models of crowded biological environments. Finally, the team will assess whether macromolecular crowding can affect the outcome of a molecular design application. Through computationally designing molecules meant to bind with specificity to a partner in either a crowded or uncrowded environment and experimentally testing those designs, they will determine whether accounting for macromolecular crowding in the design process is necessary for ensuring the desired binding properties of the designed molecule. As a whole, this project will increase our understanding of how biological environments can crucially affect biomolecular recognition.

近年来，复杂的计算机建模使科学家能够理解和解决材料科学、工程、环境科学和其他领域的挑战性问题。为了使计算模型有效，它必须是准确和高效的。在这个项目中，研究小组将评估、改进和应用用于研究生物环境中分子相互作用的计算模型。具体来说，他们将专注于计算模型，用于研究生物分子之间的相互作用如何受到其直接环境中其他分子的物理性质的影响。研究表明，细胞是高度拥挤的环境，这种“拥挤”的性质可以显著影响关键分子之间的相互作用。建立这种环境模型的可靠方法将允许更好地预测细胞过程，这可能对社会对生物系统的集体理解以及在这种系统中出现问题时制定解决方案的能力产生重要影响。这项研究将在韦尔斯利学院（Wellesley College）进行，这是一所全为女性的本科院校，促进女性对计算科学的贡献，这是一门女性长期以来代表性不足的学科。研究小组还将在当地一所多元化高中开展外展活动，向青少年介绍生物系统的计算建模领域。作为这一合作推广努力的一部分，开发的活动将在网上提供，以帮助全国各地的学生利用计算机和物理科学来解决生物学中的现实问题。用更专业的术语来说，该项目的目标是研究细胞内的大分子拥挤如何影响静电相互作用和生物分子之间的分子识别，通过一组受控的计算模型，这些模型是通过实验数据获得的。通过计算，该团队将首先研究相互作用的生物分子和大分子拥挤者是“虚拟”的系统，其物理性质可以被详尽和系统地采样，以了解分子和拥挤剂的性质，如形状、大小和电荷分布如何影响相互作用能量。他们将使用多种计算模型，包括隐式和显式处理溶剂的模型，以评估用于系统建模的方法对所做预测的影响程度。该团队还将模拟实验中可实现的生物系统，包括拥挤环境中的dna -蛋白质和蛋白质-蛋白质复合物。通过比较模拟预测和实验结果的紧密循环，他们将评估和改进拥挤生物环境的计算模型。最后，该团队将评估大分子拥挤是否会影响分子设计应用的结果。通过计算设计在拥挤或不拥挤的环境中与伴侣特异性结合的分子，并对这些设计进行实验测试，他们将确定在设计过程中考虑大分子拥挤是否对确保设计分子的期望结合特性是必要的。总的来说，这个项目将增加我们对生物环境如何至关重要地影响生物分子识别的理解。