Despite the success of spin labeling in identifying and mapping conformational changes, evidenced by work highlighted so far, transformation of EPR distances between spin labels to corresponding restraints between Cα carbons is challenging. For spin labeling EPR to become a platform for discovery, computational methods for structural and dynamic interpretation of EPR parameters need to be developed. As extrinsic probes, spin labels shape the methodology and interpretation of EPR in fundamental ways. Not only is there the potential for functional and structural perturbation but the spin label linking arm introduces intrinsic uncertainties to models constructed from EPR restraints. In contrast to the determination of EPR parameters, which is firmly established in rigorous treatment of the spin Hamiltonian, structural interpretation of the data necessitates a model of the spin label relative to the backbone, an internally consistent transfer function that links spectral and structural parameters. An additional consequence of using reporter groups is the sparseness of EPR data sets. Limited by experimental throughput, the number of EPR restraints per residue is typically many fewer than that used in NMR structure determination. Importantly, as discussed below, in the absence of a crystal structure, the restraints are not necessarily optimal or of uniform value for modeling structure and dynamics.
Although a benchmarked strategy for EPR-based modeling of structures is not yet available, simplifying approximations and assumptions have been applied to the systems reviewed here, and elsewhere in the literature, to successfully model structure and conformational changes with outcomes subsequently verified by other techniques. In most cases, the sampling of conformational space was restricted by prior data that suggested particular folds or motifs (Koteiche et al., 1998
; Poirier et al., 1998
), by structural simplicity such as small size and high symmetries (Cortes et al., 2001
), or by restriction on the type of motion to rigid body or simple helix rotation (Altenbach et al., 2008
; Perozo et al., 1999
The more general question of whether EPR restraints restrict conformational space to a set of convergent models of acceptable resolution or enable detailed description of structural rearrangements starting from a high resolution structure has only been recently addressed. Alexander et al. (2008)
carried out a systematic feasibility analysis of de novo
protein structure determination from EPR restraints in T4L. This study also aimed to directly define the information content of EPR restraints and the impact of the sparse data on model quality. The distance between spin labels was converted into a distance range between β-carbons using a simple “motion on a cone” model, treating the spin label as an average vector relative to the β-carbon. Because of an assumed isotropic distribution of the label in this model, the function relating the distance between the two spin labels to that between the corresponding β-carbon was relatively broad, i.e.
the derived restraint has large uncertainty. This study made two novel contributions. First, it heralded the use of the Rosetta folding algorithm (Das and Baker, 2008
) as an alternative computation platform to MD simulations. Second, it demonstrated that a detailed model of the spin label conformations at each site may not be required. Even with a simple boundary function to interpret the restraints (), 25 EPR restraints were sufficient to generate models with the correct fold. Subsequent high resolution refinement yielded structures that are within 1 Å RMSD from the crystal structure. This remarkable outcome was rationalized by the robust Rosetta knowledge-based energy function, which captures the principle of protein assembly encoded in known structures, compensating for the sparseness of EPR restraints. In turn, the EPR restraints efficiently restrict conformational space enabling Rosetta to find the global energy minimum.
Structure Determination by EPR and Rosetta
This study set the stage for an analysis of the information content of EPR restraints. Alexander et al.
demonstrated the importance of high information content (defined as the ratio between sequence separation and Euclidean distance) as a criterion for restraint quality. The improvement in model quality by Rosetta folding was attributed to 8 (of 25) restraints with the highest information content. Because throughput of EPR methods and the ensuing restraint sparseness is defined by the number of pairs to be constructed, a rational approach for the selection of spin labeled sites is required. Kazmier et al. (2010)
developed an algorithm for selection of distance restraints with optimized information content. An optimal algorithm was generated, and its applicability was experimentally established through prediction of a set of pairs for spin label incorporation, experimental determination of the distances and then restrained Rosetta modeling of the T4L C-terminal domain. Improvement in model quality required a limited number of restraints determined by the pairwise connectivity of T4L α-helices (21 restraints for 7 helices). Finally this study introduced a practical criterion for identifying the “native-like” model out of the thousands generated by Rosetta. These models have simultaneously low Rosetta energy and restraint violation scores.
The ultimate goal of the RosettaEPR project (Hirst et al., 2011
) is to establish a suite of algorithms that guide experimentalists in the selection of labeling sites and provide a platform for structural interpretation of the data. Its extension to membrane proteins (illustrated in ), currently under development, will tackle the challenges of the less robust Rosettamembrane energy function (implemented in Rosettamembrane) (Yarov-Yarovoy et al., 2006
). A parallel effort is underway to establish methodology for restrained modeling of conformers starting from a high resolution structure.