|dc.description.abstract||Proteins are beautiful materials evolved to channel specific energetic perturbations into particular functions. At the core of virtually every biological process are two features of a protein: the energetic architecture and the mechanisms of energy propagation. Structural, dynamics, and mutagenesis experiments have revealed that anisotropy and cooperativity are common features of the energy propagation in proteins; however, a complete understanding of the patterns and mechanisms of energy propagation remain unclear from these studies.
Previous work in our lab developed a methodology, termed the Statistical Coupling Analysis (SCA), to estimate energetic interactions between residues in a protein from their statistical co-variation through evolution. The results of this algorithm revealed a small subset of the residues in a protein have significant energetic interactions and form a connected substructure in proteins and show excellent agreement with mutagenesis data in several systems.
Using the same fundamental concepts of the original SCA, we have developed an improved version of SCA. This new algorithm provides, for the first time, a global map of the co-evolutionary interactions between residues in a protein from a multiple sequence alignment. The results of the new SCA are consistent with the original method but produce values for all pairs of positions.
We then used the energetic map provided by SCA to understand the physical basis of specificity in the PDZ domain. The co-evolutionary energetic map of the PDZ domain predicts a long range interaction between position 372, a known specificity determinant that directly interacts with ligand, and position 322. Thermodynamic measurements in one PDZ domain reveal that position 322 modulates the specificity-determining interaction between 372 and its ligand contact. Structural studies show that flexibility at 322 is tuned to make conformational change on one side of the binding pocket sensitive to interactions at the distant specificity-determining contact. This designed mechanical coupling allows the domain to have AND gate-like behavior in screening for specific binding interactions. Understanding the logic and mechanism of a co-evolved interaction gives confidence in the ability of SCA to identify the functionally critical interactions in a protein, even when not structurally obvious.
Given the functional and structural relevance of SCA predictions, we next addressed the topology of the energetic map in proteins. Analysis of several structurally and functionally diverse proteins revealed several common striking features in their energetic maps. First, the highly co-evolved positions in a protein show a high degree of mutual co-evolution so that, together, they form a nearly completely co-evolved sub-cluster. Secondly, the pattern of energetic interactions in proteins is highly heterogeneous, and fit a power-law distribution where most residues have very few co-evolutionary links with other residues and a few residues have many co-evolutionary links. The data is very consistent with extensive mutagenesis studies in several systems. Together, these experiments begin to demonstrate that the contiguous networks identified by SCA reflect structural regions capable of cooperatively channeling energy to produce functionality.||en