Discovering and Manipulating Macromolecular Conformational Ensembles

项目摘要 大分子在构象系综的不同结构状态之间波动。的一个主要 配体和突变的作用是改变这些不同状态的相对稳定性。但大部分 我们的结构生物学模型围绕着独特和单一结构的范例。我们的主要目标 是超越生物大分子的静态图像，同时保留询问 由此产生的模型，以改善配体设计和突变工程。我们也有兴趣创造 实验方法来扰乱这些构象的相对群体，使用温度或 化学扰动，使他们进入窗口，在那里他们可以观察和建模。在前两次 在NIGMS的资助下，我们专注于三项主要技术：1）集成建模，其中 在X射线（现在，越来越多，cryoEM）密度图中存在的替代构象是明确的， 作为构象系综或多构象模型识别和细化; 2）多温度 晶体学，其中X射线数据收集的温度被转移，同时避免辐射损伤， 改变不同种群的相对平衡; 3）模型验证，其中特定点的密度是 量化以支持或伪造模型。我们广泛地、协作地应用了这些范例， 致力于开放的方法和软件。两个主要焦点是：1）使用组合的配体发现 多温晶体学和经验X射线碎片筛选（最值得注意的是， 变构抑制磷酸酶PTP1B）; 2）蛋白质突变工程（最值得注意的是， 蛋白质设计和理解构象动力学和催化之间的关系）。与 MIRA的支持，我们将继续我们的计算发展，以进一步改善cryoEM建模 替代构象，以进行配体结合对蛋白质构象的影响的大规模测试 异质性，以改善不同集合模型类型的验证和比较，并量化密度 替代构象、氢和修饰的信号。与此同时，我们的实验工作将 专注于新配体的结构基础，以对抗抗生素耐药性，并定义构象 寡聚酶谷氨酰胺合成酶的景观。我们的实验工作提供了重要的 新的计算创新和验证新模型替代方案重要性的方法的试验平台 构象MIRA的支持也将使我们能够以透明和开放的方式进行研究， 进一步致力于早期数据披露（例如预印本和我们网站上的帖子）和数据重用 (e.g.主要衍射和EM数据的沉积），这已经通过使其他 研究人员总之，我们的研究将创建强大的实验和计算方法来访问 构象集合并提供利用构象异质性用于有用目的的途径。

PROJECT SUMMARY Macromolecules fluctuate between different structural states of a conformational ensemble. One of the major effects of ligands and mutations is to change the relative stability of these different states. However, most of our structural biology modeling revolves around a paradigm of distinct and singular structures. Our major goal is to move beyond static images of biological macromolecules, while retaining the ability to interrogate the resulting models to improve ligand design and mutational engineering. We are also interested in creating experimental methods to perturb the relative populations of these conformations, using temperature or chemical perturbation to bring them into the window where they can be observed and modeled. In two previous grants supported by NIGMS, we have focused three primary technologies: 1) ensemble modeling, where alternative conformations present in X-ray (and now, increasingly, cryoEM) density maps are explicitly identified and refined as a conformational ensemble or multiconformer model; 2) multitemperature crystallography, where the temperature of X-ray data collection is shifted, while avoiding radiation damage, to change the relative balance of different populations; 3) model validation, where the density at specific points is quantified to support or falsify modelling. We have applied these paradigms broadly and collaboratively, with a commitment to open methods and software. Two major foci have been: 1) ligand discovery using combinations of multitemperature crystallography and empirical X-ray fragment screening (most notably to identify new ways to allosterically inhibit the phosphatase PTP1B); 2) protein mutational engineering (most notably in the context of protein design and in understanding the relationship between conformation dynamics and catalysis). With MIRA support, we will continue our computational developments to further improve cryoEM modeling of alternative conformations, to perform large scale test of the effects of ligand binding on protein conformational heterogeneity, to improve validation and comparison of distinct ensemble model types, and to quantify density signals for alternative conformations, hydrogens, and modifications. In parallel, our experimental work will focus on the structural basis of new ligands to counter antibiotic resistance and on defining the conformational landscape of the oligomeric enzyme glutamine synthetase. Our experimental work provides an important testbed for new computational innovations and ways to validate the importance of newly modeled alternative conformations. MIRA support will also enable us to conduct our research in a transparent and open manner, dedicating ourselves further into early data disclosure (e.g. preprints and posts on our website) and data reuse (e.g. deposition of primary diffraction and EM data), which are already paying dividends by enabling other researchers. In summary, our research will create robust experimental and computational methods to access conformational ensembles and provide avenues to exploit conformational heterogeneity for useful ends.