Incorporating information on predicted solvent accessibility to the co-evolution-based study of protein interactions
Por:
Ochoa D., García-Gutiérrez P., Juan D., Valencia A., Pazos F.
Publicada:
1 ene 2013
Resumen:
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.
Filiaciones:
Ochoa D.:
Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), C/Darwin 3, Cantoblanco, 28049 Madrid, Spain
García-Gutiérrez P.:
Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), C/Darwin 3, Cantoblanco, 28049 Madrid, Spain
Chemistry Department, Universidad Autónoma Metropolitana-Iztapalapa, 09340 México D.F., Mexico
Juan D.:
Structural Bioinformatics Group, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernández Almagro 3, 28029 Madrid, Spain
Valencia A.:
Structural Bioinformatics Group, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernández Almagro 3, 28029 Madrid, Spain
Pazos F.:
Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), C/Darwin 3, Cantoblanco, 28049 Madrid, Spain
|