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Ochoa D.,CSIC - National Center for Biotechnology | Ochoa D.,European Bioinformatics Institute | Juan D.,Structural Bioinformatics Group | Valencia A.,Structural Bioinformatics Group | Pazos F.,CSIC - National Center for Biotechnology
Bioinformatics | Year: 2015

Motivation: The evolution of proteins cannot be fully understood without taking into account the coevolutionary linkages entangling them. From a practical point of view, coevolution between protein families has been used as a way of detecting protein interactions and functional relationships from genomic information. The most common approach to inferring protein coevolution involves the quantification of phylogenetic tree similarity using a family of methodologies termed mirrortree. In spite of their success, a fundamental problem of these approaches is the lack of an adequate statistical framework to assess the significance of a given coevolutionary score (tree similarity). As a consequence, a number of ad hoc filters and arbitrary thresholds are required in an attempt to obtain a final set of confident coevolutionary signals. Results: In this work, we developed a method for associating confidence estimators (P values) to the tree-similarity scores, using a null model specifically designed for the tree comparison problem. We show how this approach largely improves the quality and coverage (number of pairs that can be evaluated) of the detected coevolution in all the stages of the mirrortree workflow, independently of the starting genomic information. This not only leads to a better understanding of protein coevolution and its biological implications, but also to obtain a highly reliable and comprehensive network of predicted interactions, as well as information on the substructure of macromolecular complexes using only genomic information. Availability and implementation: The software and datasets used in this work are freely available at: http://csbg.cnb.csic.es/pMT/. © 2015 The Author 2015. Published by Oxford University Press. All rights reserved. Source


Herman D.,CSIC - National Center for Biotechnology | Herman D.,University of Birmingham | Ochoa D.,CSIC - National Center for Biotechnology | Juan D.,Structural Bioinformatics Group | And 3 more authors.
BMC Bioinformatics | Year: 2011

Background: The prediction and study of protein interactions and functional relationships based on similarity of phylogenetic trees, exemplified by the mirrortree and related methodologies, is being widely used. Although dependence between the performance of these methods and the set of organisms used to build the trees was suspected, so far nobody assessed it in an exhaustive way, and, in general, previous works used as many organisms as possible. In this work we asses the effect of using different sets of organism (chosen according with various phylogenetic criteria) on the performance of this methodology in detecting protein interactions of different nature.Results: We show that the performance of three mirrortree-related methodologies depends on the set of organisms used for building the trees, and it is not always directly related to the number of organisms in a simple way. Certain subsets of organisms seem to be more suitable for the predictions of certain types of interactions. This relationship between type of interaction and optimal set of organism for detecting them makes sense in the light of the phylogenetic distribution of the organisms and the nature of the interactions.Conclusions: In order to obtain an optimal performance when predicting protein interactions, it is recommended to use different sets of organisms depending on the available computational resources and data, as well as the type of interactions of interest. © 2011 Herman et al; licensee BioMed Central Ltd. Source


Mitson M.,Weatherall Institute of Molecular Medicine | Kelley L.A.,Structural Bioinformatics Group | Sternberg M.J.,Structural Bioinformatics Group | Higgs D.R.,Weatherall Institute of Molecular Medicine | Gibbons R.J.,Weatherall Institute of Molecular Medicine
Human Molecular Genetics | Year: 2011

ATRX is a member of the Snf2 family of chromatin-remodelling proteins and is mutated in an X-linked mental retardation syndrome associated with alpha-thalassaemia (ATR-X syndrome). We have carried out an analysis of 21 disease-causing mutations within the Snf2 domain of ATRX by quantifying the expression of the ATRX protein and placing all missense mutations in their structural context by homology modelling. While demonstrating the importance of protein dosage to the development of ATR-X syndrome, we also identified three mutations which primarily affect function rather than protein structure. We show that all three of these mutant proteins are defective in translocating along DNA while one mutant, uniquely for a human diseasecausing mutation, partially uncouples adenosine triphosphate (ATP) hydrolysis from DNA binding. Our results highlight important mechanistic aspects in the development of ATR-X syndrome and identify crucial functional residues within the Snf2 domain of ATRX. These findings are important for furthering our understanding of how ATP hydrolysis is harnessed as useful work in chromatin remodelling proteins and the wider family of nucleic acid translocating motors. © The Author 2011. Published by Oxford University Press. All rights reserved. Source


Ochoa D.,CSIC - National Center for Biotechnology | Garcia-Gutierrez P.,CSIC - National Center for Biotechnology | Garcia-Gutierrez P.,Metropolitan Autonomous University | Juan D.,Structural Bioinformatics Group | And 2 more authors.
Molecular BioSystems | Year: 2013

A widespread family of methods for studying and predicting protein interactions using sequence information is based on co-evolution, quantified as similarity of phylogenetic trees. Part of the co-evolution observed between interacting proteins could be due to co-adaptation caused by inter-protein contacts. In this case, the co-evolution is expected to be more evident when evaluated on the surface of the proteins or the internal layers close to it. In this work we study the effect of incorporating information on predicted solvent accessibility to three methods for predicting protein interactions based on similarity of phylogenetic trees. We evaluate the performance of these methods in predicting different types of protein associations when trees based on positions with different characteristics of predicted accessibility are used as input. We found that predicted accessibility improves the results of two recent versions of the mirrortree methodology in predicting direct binary physical interactions, while it neither improves these methods, nor the original mirrortree method, in predicting other types of interactions. That improvement comes at no cost in terms of applicability since accessibility can be predicted for any sequence. We also found that predictions of protein-protein interactions are improved when multiple sequence alignments with a richer representation of sequences (including paralogs) are incorporated in the accessibility prediction. © 2013 The Royal Society of Chemistry. Source

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