ensemble descriptions of disordered proteins and their complexes

蛋白质就像一串珠子，在这种情况下，由不同类型的氨基酸残基链组成。虽然许多蛋白质采用相对静态和有序的三维形状以发挥功能，但最近已经清楚的是，一大类“无序”蛋白质并不如此。它们以高度灵活的状态发挥作用，主要是与其他蛋白质结合。结合后，一些无序的蛋白质确实变得有序，但其他蛋白质保留了大部分的灵活性。从病毒到植物和动物，在整个生物学中都可以发现无序蛋白质，并且与控制关键生物事件以及疾病有关。为了更好地理解这些蛋白质的性质和它们的生物学行为，对无序蛋白质及其动态复合物的现实描述是必不可少的。虽然静态蛋白质需要一个结构来描述它，但无序蛋白质或动态复合物需要许多结构（系综）来表示它，而标准结构方法是不可能的。因此，我们一直在开发计算机方法ENSEMBLE，该方法计算与大量实验数据一致的结构集合，并共同代表无序蛋白质。这种方法的许多方面必须进一步发展，因为没有足够的数据来定义集合，并且存在数据解释和算法优化的问题。因此，我们提出以下目标：（1）发展ENSEMBLE，以更好地表征分离的无序蛋白质。使用模型系统，我们将获得实验数据，并探索更好的方法来从中提取结构信息，执行不同类型的计算模拟，并确定在ENSEMBLE中使用的最佳算法。(2)开发表征无序蛋白质复合物的方法。使用不同的模型系统，将探索新的方法，以减少由于动态复合体中的运动而导致的核磁共振数据差的问题，并扩展ENSEMBLE描述动态复合体的能力。我们的努力将提供一个强大的工具，将广泛提供给结构社区研究这类重要但具有挑战性的高动态蛋白质。

Proteins are like strands of beads, in this case made up of a chain of different types of amino acid residues. While many proteins adopt a relatively static and ordered 3-dimensional shape in order to function, it has become clear recently that a large class of "disordered" proteins do not. They function in a highly flexible state, primarily in binding to other proteins. Upon binding, some disordered proteins do become ordered but others retain much of their flexibility. Disordered proteins are found throughout biology, from viruses to plants and animals, and are associated with controlling critical biological events as well as in disease. In order to better understand the nature of these proteins and the biology they perform, realistic descriptions of disordered proteins and their dynamic complexes are essential. While a static protein needs one structure to describe it, a disordered protein or dynamic complex requires many structures (an ensemble) to represent it, not possible with standard structural approaches. Thus, we have been developing the computer approach ENSEMBLE that calculates collections of structures that together are consistent with a large amount of experimental data and together represent the disordered protein. Many aspects of this approach must be further developed since there is not enough data to define the ensemble and there are issues with the interpretation of the data and the optimization of the algorithm. Thus, we propose the following aims: (1) Develop ENSEMBLE to better characterize isolated disordered proteins. Using a model system, we will obtain experimental data and explore better ways to extract structural information from them, perform different types of computational simulations and determine the best algorithms to utilize within ENSEMBLE. (2) Develop approaches to characterize complexes of disordered proteins. Using a different model system, will explore new methods to reduce problems with poor NMR data due to motion in dynamic complexes and expand ENSEMBLE's capability to describe dynamic complexes. Our efforts will provide a powerful tool that will be widely available to the structural community for studying this important yet challenging class of highly dynamic proteins.