A configuration space of homologous proteins conserving mutual information and allowing a phylogeny inference based on pair-wise Z-score probabilities
1 UMR 5019 CNRS-CEA-INRA-Université Joseph Fourier, Laboratoire de Physiologie Cellulaire Végétale; Département Réponse et Dynamique Cellulaire; CEA Grenoble, 17 rue des Martyrs, F-38054, Grenoble cedex 09, France
2 Gene-IT, 147 avenue Paul Doumer, F-92500 Rueil-Malmaison, France
3 Département d'Ecophysiologie Végétale et de Microbiologie; CEA Cadarache, F-13108 Saint Paul-lez-Durance, France
4 Laboratoire de Biologie, Informatique et Mathématiques; Département Réponse et Dynamique Cellulaire, CEA Grenoble, 17 rue des Martyrs, F-38054, Grenoble cedex 09, France
BMC Bioinformatics 2005, 6:49 doi:10.1186/1471-2105-6-49Published: 10 March 2005
Popular methods to reconstruct molecular phylogenies are based on multiple sequence alignments, in which addition or removal of data may change the resulting tree topology. We have sought a representation of homologous proteins that would conserve the information of pair-wise sequence alignments, respect probabilistic properties of Z-scores (Monte Carlo methods applied to pair-wise comparisons) and be the basis for a novel method of consistent and stable phylogenetic reconstruction.
We have built up a spatial representation of protein sequences using concepts from particle physics (configuration space) and respecting a frame of constraints deduced from pair-wise alignment score properties in information theory. The obtained configuration space of homologous proteins (CSHP) allows the representation of real and shuffled sequences, and thereupon an expression of the TULIP theorem for Z-score probabilities. Based on the CSHP, we propose a phylogeny reconstruction using Z-scores. Deduced trees, called TULIP trees, are consistent with multiple-alignment based trees. Furthermore, the TULIP tree reconstruction method provides a solution for some previously reported incongruent results, such as the apicomplexan enolase phylogeny.
The CSHP is a unified model that conserves mutual information between proteins in the way physical models conserve energy. Applications include the reconstruction of evolutionary consistent and robust trees, the topology of which is based on a spatial representation that is not reordered after addition or removal of sequences. The CSHP and its assigned phylogenetic topology, provide a powerful and easily updated representation for massive pair-wise genome comparisons based on Z-score computations.