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Conformational diversity modulates evolutionary rate in protein evolution Diego Zea1, Cristina Marino Buslje2, Gustavo Parisi1 Universidad Nacional de Quilmes. Roque Sáenz Peña 352 . Bernal. B1876BXD Buenos Aires Argentina. 2 Fundación Instituto Leloir. Avda. Patricias Argentinas 435. C1405BWE. Argentina 1 The study of evolutionary rates during protein evolution is a central issue to understand the mechanism underling molecular evolution. Several factors have been associated as modulating evolutionary rate such as the differences in genomic location, functional importance of the protein, expression level, structural constraints, protein stability and developmental time [1]. It was established that one of the strongest and consistent correlations between genomic data and rates of evolutionary change is the expression level of genes [2]. Previous estimations have established that structural constraints could explain as much as 10% of the variation of evolutionary rate in proteins [3]. However, recent findings indicate that structure-functional features and translation rates could have comparable contributions to explain evolutionary rates [4]. In this work we study how the presence of conformational diversity in proteins could influence the rate of evolution. Conformational diversity relates with the description of the native state of proteins as an ensemble of different conformers in dynamical equilibrium [5]. Previous studies have found that conformational diversity in proteins increase the number of structural constraints [6]. In this sense, we expect an inverse relationship between the extension of the conformational diversity and the rate of protein evolution. To study this relationship we used the PCDB database (Protein Conformational Data Base) [7]. This database contains almost 8000 proteins with different degrees of structural diversity measured as the maximum RMS (RMSmax) found between the different conformers for each protein. We have used a subset of the total number of proteins deposited in PCDB. This set consists in those proteins differing in the number of ligands associated to each conformer for each protein. Each of these proteins was linked to OMA database (The Orthologs Matrix Project) [8] of orthologs to estimate the evolutionary rate. Each set of orthologs were further associated with its corresponding coding DNA and the evolutionary rates were estimated using PAML 4 (Phylogenetic Analysis by Maximum Likelihood) [9,10] using different codon models. For this purpose we used a tree constructed with Protpars from Phylip package [11] for each set of orthologs. The final set we obtained is composed by 50 proteins with an average RMS of 1.02 and a maximum RMS of 4.7. Using this set we obtained that the evolutionary rate decrease with the increase of conformational diversity measured by RMSmax between conformers. We found that the dN/dS (the number of nonsynonimous to synonymous substitutions) decrease from 0.15 (proteins with less than 1 RMSmax) in average to 0.05 (proteins above 2 RMSmax) in average. These results support the vision that proteins with large native conformational space have high content of structural constraints and then produce a decrease in the evolutionary rate. Although the results are very promising, further studies are required to shed light in such an important issue in molecular evolution. References 1. Csaba Pál, Balázs Papp y Martin J. Lercher. 2006. An integrated view of protein evolution. Nature Reviews. Genetics. Volume 7. 337 2. Drummond, D. A., J. D. Bloom, C. Adami, C. O. Wilke, and F. H. Arnold. 2005. Why highly expressed proteins evolve slowly. Proc. Natl. Acad. Sci. USA 102:1433814343. 3. Bloom JD, Labthavikul ST, Otey CR, Arnold FH. 2006. Protein stability promotes evolvability. Proc Natl Acad Sci USA 103: 5869 4. Wolf, Y. I., I. V. Gopich, D. J. Lipman, and E. V. Koonin. 2010. Relative contributions of intrinsic structural-functional constraints and translation rate to the evolution of protein-coding genes. Genome Biol Evol 2:190-199. 5. Tsai, C. J., B. Ma, and R. Nussinov. 1999. Folding and binding cascades: shifts in energy landscapes. Proc Natl Acad Sci U S A 96:9970-9972. 6. Juritz Ezequiel, Palopoli Nicolas, Fornasari Maria Silvina, Fernandez- Alberti Sebastian, and Parisi Gustavo. Protein conformational diversity modulates sequence divergence. Enviado a Molecular Biology and Evolution febrero 2011. 7. Juritz, Ezequiel; Fernandez Alberti, Sebastian; Parisi, Gustavo. 2010. PCDB: A database of protein conformational diversity. Nucleic Acids Research. Accepted paper. 8. Dessimoz Christophe, Cannarozzi Gina, Gil Manuel, Margadant Daniel, Roth Alexander, Schneider Adrian, and Gonnet Gaston H., OMA, a Comprehensive, Automated Project for the Identification of Orthologs from Complete Genome Data: Introduction and First Achievements A. McLysaght et al. (Eds): RECOMB 2005 Workshop on Comparative Genomics, LNBI 3687, pp. 61-72,. 9. Yang, Z. 1997 PAML: a program package for phylogenetic analysis by maximum likelihood. Comput. Appl. Biosci. 13, 555-556. 10. Yang, Z. 2007 PAML 4: Phylogenetic Analysis by Maximum Likelihood. Mol. Biol. Evol. 24, 1586-1591. 11. Felsenstein, J. 1993. PHYLIP (Phylogeny Inference Package) version 3.5c. Distributed by the author. Department of Genetics, University of Washington, Seattle.