Quantitative, Mechanistic Studies of Biomolecular Recognition

 描述(申请人提供)：生物分子研究正日益从现象学和描述性向定量和预测性转变。拟议研究的总体目标是促进生物分子识别机制研究中的这一范式转变，生物分子识别发生在广泛的空间尺度上。蛋白质-配体结合和变构调节是典型的分子识别过程。在空间尺度上，现在已经识别出许多本质上无序的蛋白质，通常通过与其细胞靶标结合来参与信号或调节。在亚细胞尺度上，令人兴奋的发现是 是关于无膜形式的微隔室，其中特定的蛋白质和RNA被浓缩但保持液体。这些“细胞内小体”响应调控信号可逆组装，并能识别排除的“旁观者”成分。高浓度的旁观者大分子总是存在于细胞环境中，并影响所有这些分子识别过程。与空间尺度无关，分子识别的基本基础是分子的物理性质，包括分子的相互作用和运动。为了获得关于所有这些分子识别过程的深入机制知识，拟议的研究将使用三种互补的方法。将开发理论模型来测试力学假说，指导实验设计，并建立将热力学和力学性质与分子物理性质联系起来的框架。该框架将通过分子模拟和原子水平的计算来实现。将进行实验测量以获得关键信息，这些信息也将用于激励理论模型和验证计算结果。通过这三种方法的整合，将表征大分子拥挤的影响，以便将稀溶液研究中的知识转移到细胞环境中。对生物分子识别的深入、定量的理解将使人们能够准确地预测机械特性，并通过改变机械途径来获得药物设计的机会。

 DESCRIPTION (provided by applicant): Biomolecular research is increasingly changing from phenomenological and descriptive to quantitative and predictive. The overall goal of the proposed research is to facilitate this paradigm shift in mechanistic studies of biomolecular recognition, which occurs at a wide range of spatial scales. Protein-ligand binding and allosteric regulation are the prototypical molecular recognition processes. Moving up the spatial scale, many intrinsically disordered proteins have now been identified, often involved in signaling or regulation by binding to their cellular targets. At the subcellular scale, exciting discoveries are being made about a membraneless form of micro-compartments, wherein specific proteins and RNAs are condensed but remain fluid. These "intracellular bodies" assemble reversibly in response to regulatory signals and can recognize "bystander" components for exclusion. High concentrations of bystander macromolecules ("crowders") are always present in the cellular environments and affect all these molecular recognition processes. Irrespective of spatial scales, the fundamental basis of molecular recognition is the molecular physical properties, including molecular interactions and motions. To gain deep mechanistic knowledge on all these molecular recognition processes, the proposed research will use three complementary approaches. Theoretical models will be developed to test mechanistic hypotheses and guide experimental design and to establish the framework for relating thermodynamic and mechanistic properties to molecular physical properties. The framework will be implemented computationally, through molecular simulations and atomistic-level calculations. Experimental measurements will be made to obtain critical information, which will also serve to inspire theoretical models and validate computational results. Through the integration of the three approaches, the effects of macromolecular crowding will be characterized, such that the knowledge from dilute-solution studies can be transferred to the cellular context. The deep, quantitative understanding of biomolecular recognition to be achieved will enable accurate predictions of mechanistic properties and yield opportunities for drug design through altering mechanistic pathways.