Statistical Coupling Analysis of the Photosystem II D1 Protein

1. Statistical Coupling Analysis of the Photosystem II D1 Protein Janan Zhu1; Nicholas Polizzi2; 1Department of Mathematics, Duke University; 2Department of Chemistry, Duke University Abstract D1 Protein Cofactors in the Electron Transfer Pathway Sector Definition Statistical coupling analysis can be applied to alignments of related proteins to examine “sectors” in the sequences that are evolving independently of each other. Our application of this technique to the D1 protein of photosystem II identified 4 significant sectors and we assigned different functions to each of the based on their location within the protein. The resulting sectors appear to be clustered around the cofactors of the protein that are involved in the photosynthetic electron transport pathway. These results suggest that the cofactor binding sites in the D1 protein have evolved independently to some degree, a fact that may contribute to further biochemical and structural studies. Positions in the eigenmodes with a weight greater than 0.09 were considered to be part of the corresponding sectors. Some of the sectors obtained from the raw eigenmodes contained large regions of overlap with other sectors and were merged to simplify functional description. Introduction The photosystem II D1 protein is vital to the photosynthetic electron transport pathway as it houses the reaction center chlorophylls in addition to the cofactors that channel the excited electrons to other proteins in the membrane. Studies of protein conservation promote a better understanding of how the protein facilitates the transfer of electrons between cofactors. Figure depicting the merging process for three sectors. The unified sector consisted of the union of the pairwise intersections of the sectors. Description of Sectors The flow of electrons between the cofactors is shown in green. Plastoquinone B is the final electron acceptor and dissociates from the protein into the thylakoid membrane. The number of sectors was determined from empirical observation of their 3D mapping onto the T. vulcanusstructure. A defining characteristic of a sector aside from statistical independence is physical connectivity in space (Halabi et al. 2009). Eigenmodes with higher eigenvalues accounts for a greater degree of the variance in the correlation matrix. Therefore, the eigenmodes were considered in order of descending eigenvalues until they yielded sectors with disparate amino acids of low descriptive value. This method resulted in four sectors being defined. Method: Eigenvalue Decomposition of Positional Correlations A set of 152 representative sequences was generated using a BLAST search for sequences similar to that of the T. vulcanus photosystem II D1 protein (length 344 AA). These sequences were then aligned and the alignments were weighted by the Kullback-Leibler relative entropy, a measure of the degree of conservation at a particular position. These weighted pairwise alignments were used to create the 344 x 344 positional correlation matrix, which indicated the degree of correlated evolution between each pair of positions. Overview of the sectors in the D1 protein Summary and Discussion The application of statistical coupling analysis to the photosystem II D1 protein yielded independent sectors that appear to have biological relevance. The sectors are clustered around the different cofactors of the photosynthetic electron transport pathway. We conclude that the different steps in electron transport appear to be facilitated by evolutionarily independent parts of the protein. This has important implications, as the overall proton-coupled electron transfer is believed to be a concerted reaction, yet separate sectors were found for each step. It remains to be shown if this is true for electron transfer proteins in general and this study has demonstrated the viability of statistical coupling analysis for this purpose. The photosystem II complex, with the D1 protein highlighted A graphical representation of the positional correlation matrix Spectral decomposition of the positional correlation matrix was used to identify statistically independent groups of amino acids (sectors). The eigenvectors (also called eigenmodes) used to represent the matrix contain weights for the significance of the position within the sector. References N. Halabi, O. Rivoire, S. Leibler, R. Ranganathan. Cell, 138: 774-786, 2009 Source: BiochemistryMathews,VanHolde, Ahern 3E