Mar 222010
 
ResearchBlogging.orgThe proposition that general fold architecture is preserved within a family of evolutionarily-related proteins is not controversial. The amino acid sequence of a protein determines its structure, and countless studies have substantiated the idea that proteins with similar sequences will adopt similar folded conformations. Because structure and dynamics are intrinsically linked, one could reasonably assume that many features of a protein’s dynamics get conserved along with the fold. A growing number of experiments show that this is indeed the case, including a recent paper in Structure (1).

We already have some evidence of fold-dependent dynamics. An NMR study from my mentor Andrew Lee’s lab comparing fast fluctuations of side chains among three related proteins from the PDZ family suggested that motions on this timescale could be evolutionarily conserved (2). That study compared the model-free order parameters of methyl groups from one protein to those of their counterparts in other PDZ domains. Predicting an order parameter using dynamics data from a structurally equivalent residue in another protein was shown to be slightly more accurate than calculations from structural considerations such as packing or methyl type. In a similar vein, I have previously discussed studies on adenylate kinase enzymes from E. coli and a thermophilic organism that show they have similar backbone dynamics under conditions where their enzymatic activity is about equal, although they differ substantially from each other at room temperature.

Of course, these studies were limited and involved just a few proteins, because getting experimental data about dynamics is costly and time-consuming. For comparisons across large numbers of different proteins, computational approaches may therefore be of great value. Previously, other groups have made use of short molecular dynamics simulations or normal mode analysis. Raimondi et al. continue in this vein, combining normal-mode analysis of single structures with principal component analysis of a large set of structures from the Ras superfamily of proteins.

The Ras superfamily encompasses several groups of related folds with nucleotide-dependent activity. When GTP is bound to them, they are active and propagate a particular signal. Over time, the GTP gets hydrolyzed to GDP and the signal turns off. This catalytic process is pretty inefficient, but it can be enhanced by the action of a GTPase Activating Protein (GAP). The exchange of GDP for GTP can be enhanced by the action of a Guanine nucleotide Exchange Factor (GEF). The GTP/GDP state manifests primarily in the positioning of two loops, termed the switch regions (SwI and SwII). This mechanism allows for several different modes of control, so the Ras architecture has been repurposed many times throughout evolution for a variety of different roles.

Because the different members of the superfamily play key roles in their respective pathways, there are many structures available, often in several different states (GTP-bound, GDP-bound, GEF-bound, etc.). Raimondi et al. aligned these structures using the common features of the Ras fold and used PCA to identify flexibility across this evolutionary ensemble. The goal of PCA is to take a dataset with many potentially correlated data points (in this case, the relative positions of the backbone Cα atoms) and identify a small set of variables that explain as much of the variance as possible. Here, the principal components (PC) are expected to describe the structural variability of the fold.

The first PC, which is expected to explain the largest amount of the variability, can separate the structures by their families. That is, the displacement along PC1 can distinguish a Rho family domain from an Arf family domain. The authors call this variability function-independent, because this principal component doesn’t seem to make any meaningful distinction between the GTP/active and GDP/inactive states. That appears to be a property of the second PC, which for some families does a very good job of separating the GTP from the GDP-bound forms (for others there appears to be more mixing). According to this analysis, function-dependent variability appears to be confined to one half of the protein, while function-independent variability seems to be distributed across the whole fold.

The authors also performed normal mode analysis on individual proteins from the Ras superfamily using an elastic network model. In this kind of simulation the protein is modeled as a group of Cα “nodes” connected by spring-like harmonic potentials representing covalent and non-covalent interactions. Although any one of these “bonds” can be stretched, compressed, and moved, such deformations exert a force on other bonds connected to the nodes involved, which tends to damp most motions. Certain collective deformations will be favored as a result, and these can be calculated as “normal modes” that probably reflect slow fluctuations of the fold.

The deformations detected by ENM for all individual proteins overlapped significantly with the second PC identified in the evolutionary analysis. That is, the conformational variability of a conserved domain over evolutionary time is correlated with the conformational fluctuations of a single domain on a biological time scale. This makes sense, especially in this case, because the switch regions are areas of significant conformational variability, and are connected with the conserved catalytic function of these proteins. The fact that PC1 doesn’t line up with the low-frequency normal modes probably means that the conformational transitions between different family members cannot be mimicked by ordinary thermal motion, i.e. the fold cannot change this way without the aid of mutations.

Although the results in these studies might seem rather pedestrian and expected, I find them quite encouraging. We’re not particularly good at predicting structure from sequence yet, and our understanding of protein dynamics is even more primitive. What these studies indicate is that it should be possible to predict the conformational fluctuations of a given protein or domain using our knowledge of a related, homologous protein. This could have positive consequences for fields such as rational drug design and protein design, which have met with limited success in part, perhaps, because they do not sufficiently account for a protein’s structural fluctuations.

(1) Raimondi, F., Orozco, M., & Fanelli, F. (2010). Deciphering the Deformation Modes Associated with Function Retention and Specialization in Members of the Ras Superfamily. Structure, 18 (3), 402-414 DOI: 10.1016/j.str.2009.12.015

(2) Law, A., Fuentes, E., & Lee, A. (2009). Conservation of Side-Chain Dynamics Within a Protein Family. Journal of the American Chemical Society, 131 (18), 6322-6323 DOI: 10.1021/ja809915a

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