Most of the cellular functions are the result of the concerted action of complexes of proteins forming pathways and networks. For this reason, lot of efforts has been devoted to the study of protein-protein interactions. Large-scale experiments on whole genomes allowed the identification of interacting protein pairs but the residues involved in the interaction are generally not known and the majority of the interactions still lack a structural characterization. A crucial step towards the deciphering of the interaction mechanism of proteins is the recognition of their interacting surfaces, particularly in those structures for which also the most recent interaction network resources do not contain information. Furthermore, the prediction of interaction sites in protein structures is extremely valuable in the view of targeting specific disease-related interactions. Then the question arises if it is possible to highlight possible correlations among protein structures and their interaction at the proteome level. In a first step we focus on the human cell cycle that, as compared with other biological process, comprises the largest number of proteins known with atomic resolution both as single monomers and as interacting complexes. For predicting interaction patches we specifically develop a HM-SVM based method reaching a 67% overlapping among predicted and observed interaction patches and scoring among the best predictors of this type. To test the biological meaning of our predictions, we also explore whether interacting patches contain energetically important residues and/or disease related mutations and find that over-predicted patches are endowed with both features. Based on this we propose that mapping the protein with all the predicted interaction patches can trans-link to its interactome at the cell level. To test our hypothesis we download interactomic data from specific data bases and find that the number of predicted interaction patches significantly correlates (Pearson correlation value >0.3) with the number of their known interactions (edges) in the human interactome as contained in MINT and IntAct, two databases of protein-protein interactomes. We also show that the correlation increases (Pearson correlation value >0.5) when the subcellular co-localization of the interacting partners is taken into account.

Trans-linking the protein-protein interactome at the cell level.

CASADIO, RITA
2010

Abstract

Most of the cellular functions are the result of the concerted action of complexes of proteins forming pathways and networks. For this reason, lot of efforts has been devoted to the study of protein-protein interactions. Large-scale experiments on whole genomes allowed the identification of interacting protein pairs but the residues involved in the interaction are generally not known and the majority of the interactions still lack a structural characterization. A crucial step towards the deciphering of the interaction mechanism of proteins is the recognition of their interacting surfaces, particularly in those structures for which also the most recent interaction network resources do not contain information. Furthermore, the prediction of interaction sites in protein structures is extremely valuable in the view of targeting specific disease-related interactions. Then the question arises if it is possible to highlight possible correlations among protein structures and their interaction at the proteome level. In a first step we focus on the human cell cycle that, as compared with other biological process, comprises the largest number of proteins known with atomic resolution both as single monomers and as interacting complexes. For predicting interaction patches we specifically develop a HM-SVM based method reaching a 67% overlapping among predicted and observed interaction patches and scoring among the best predictors of this type. To test the biological meaning of our predictions, we also explore whether interacting patches contain energetically important residues and/or disease related mutations and find that over-predicted patches are endowed with both features. Based on this we propose that mapping the protein with all the predicted interaction patches can trans-link to its interactome at the cell level. To test our hypothesis we download interactomic data from specific data bases and find that the number of predicted interaction patches significantly correlates (Pearson correlation value >0.3) with the number of their known interactions (edges) in the human interactome as contained in MINT and IntAct, two databases of protein-protein interactomes. We also show that the correlation increases (Pearson correlation value >0.5) when the subcellular co-localization of the interacting partners is taken into account.
Proceedings of PRIB 2010
4
4
Casadio r.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/101607
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