TSEMA: Interactive prediction of protein pairings between interacting families

José M.G. Izarzugaza, David Juan, Carles Pons, Juan A.G. Ranea, Alfonso Valencia, Florencio Pazos

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


An entire family of methodologies for predicting protein interactions is based on the observed fact that families of interacting proteins tend to have similar phylogenetic trees due to co-evolution. One application of this concept is the prediction of the mapping between the members of two interacting protein families (which protein within one family interacts with which protein within the other). The idea is that the real mapping would be the one maximizing the similarity between the trees. Since the exhaustive exploration of all possible mappings is not feasible for large families, current approaches use heuristic techniques which do not ensure the best solution to be found. This is why it is important to check the results proposed by heuristic techniques and to manually explore other solutions. Here we present TSEMA, the server for efficient mapping assessment. This system calculates an initial mapping between two families of proteins based on a Monte Carlo approach and allows the user to interactively modify it based on performance figures and/or specific biological knowledge. All the explored mappings are graphically shown over a representation of the phylogenetic trees. The system is freely available at http://pdg.cnb.uam.es/TSEMA. Standalone versions of the software behind the interface are available upon request from the authors.

Original languageEnglish (US)
Pages (from-to)W315-W319
JournalNucleic acids research
Issue numberWEB. SERV. ISS.
StatePublished - Jul 2006
Externally publishedYes

Bibliographical note

Funding Information:
We would like to acknowledge Diego Díez (IIB-CSIC) and the members of the Protein Design Group (CNB-CSIC) for discussion and suggestions. We are specially grateful to Ana M. Rojas for her help on Bayesian trees, Michael Tress for critical reading of the manuscript, and Eduardo Andres and Angel Carro for technical assistance. F.P. is the recipient of a ‘Ramón y Cajal’ contract from the Spanish Ministry for Education and Science. C.P. work is supported by a grant from ‘Genoma España’ to the National Institute for Bioinformatics. This work has been partially funded by the GeneFun (LSHG-CT-2004-503567) and BioSapiens (LSHC-CT-2003-505265) EU projects and a grant from the ‘Fundación BBVA’. Standalone versions of the software behind the interface are available upon request from the authors. Funding to pay the Open Access publication charges for this article was provided by the BioSapiens EU project (LSHC-CT-2003-505265).


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