To understand the workings of a living cell, we need to know the architectures of its macromolecular assemblies. Here we show how proteomic data can be used to determine such structures. The process involves the collection of sufficient and diverse high-quality data, translation of these data

biomolecular mechanisms and for relating structure to function. Here we report a highly accurate technique for inferring the major modes of structural correlation in macromolecules using likelihood-based statistical analysis of sets of structures. This method is generally applicable to any ensemble of related

are intimately related to the timescale of conformational exchange within the ensemble, which govern target recognition and how these proteins function. In this work, we investigated the macromolecular crowding effects on the dynamics of several IDPs by measuring the NMR spin relaxation parameters of three

are indicated by a high crystallographic B-factor or highly divergent conformations in an NMR ensemble. If a particular part of a structure is important to the question being asked, such telltale signs should always be heeded.

. Although NMR spectroscopy remains the favored tool, a number of new biophysical techniques are proving exceptionally useful in defining the limits of the conformational ensembles. Advances have been made in prediction of the recognition elements in disordered proteins, in elucidating the kinetics