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Experimental approaches to transient protein interactions are laborious, and our understanding of fundamental questions like the identification of interaction surfaces or the specificity of molecular recognition between interacting proteins is far from being complete. We propose a computational approach based on techniques from statistical physics, which exploits correlations in the natural sequence variability in families of homologous proteins across hundreds of species. Using bacterial two-component signal transduction as a test case, we show that our method is able (i) to identify inter-protein residue contacts and to facilitate the prediction of protein complex strutures, and (ii) to reconstruct a molecular recognition code which elucidates specificity in signal transduction in bacteria.

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Unraveling the evolutionary forces responsible for variations of neutral substitution patterns among taxa or along genomes is a major prerequisite for detecting selection within sequences. In general, one assumes that mutations acted upon the two strands of the DNA molecule symmetrically, i.e. the substitution process does not distinguish between the forward and backward strand. For example, under this symmetry the rate for C->T transitions equals to the rate of G->A transitions. However, several cellular mechanisms (transcription of a gene being one) might break this symmetry of the two strands. We establish a comparative analysis of three or more genomes that allows us to quantify the 12 different rates for exchanges of one nucleotide by another as well as the rate of the neighbor dependent CpG deamination process. A regional analysis of nucleotide substitution rates along human genes and their flanking regions reveals two distinct patterns of reverse complement symmetry breaking: (1) a…

It has been reported [1] that in prokaryotic genomes the number of transcription factors is proportional to the **square** of the total number of genes. As a consequence of this trend the fraction of regulators (the so-called "regulatory overhead") is less than 0.5% in small (< 500 genes) bacterial genomes, while in large genomes (~10,000 genes) it reaches as high as 10%. We recently proposed [2] a general explanation of this empirical scaling law and illustrated it using a simple model in which metabolic and regulatory networks co-evolve together. In our model prokaryotic organisms acquire new metabolic functions by the virtue of horizontal gene transfer of entire co-regulated metabolic pathways from a shared gene pool (the "universal metabolic network"). This transfer is followed by the removal of redundant enzymes. Pathways can also be lost following long-term changes in the environment or lifestyle of the organism. The whole process can be compared to…

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Ce séminaire porte sur les interactions entre protéines, abordées d'un point de vue structural. Le fait qu'une protéine donnée puisse reconnaître différents partenaires protéiques, en utilisant le même site d'interaction, suggère l'existence de "motifs communs" reconnus sur ces différents partenaires. Que sont ces "motifs communs", présents chez des protéines adoptant des structures 3D différentes ? En utilisant une description des interfaces par leurs propriétés physico-chimiques, nous avons trouvé peu de preuves de l'existence de "motifs communs" et concluons donc que la reconnaissance se fait selon un autre mécanisme.