BACKGROUND: Biological processes are usually distributed over various intracellular compartments. Proteins from diverse cellular compartments are often involved in similar signaling networks. However, the difference in the reaction rates between similar proteins among different compartments is usually quite high. We suggest that the estimation of frequency of intracompartmental as well as intercompartmental protein-protein interactions is an appropriate approach to predict the efficiency of a pathway. RESULTS: Using data from the databases STRING, ANDSystem, IntAct and UniProt, a PPI frequency matrix of intra/inter-compartmental interactions efficiencies was constructed. This matrix included 15 human-specific cellular compartments. An approach for estimating pathway efficiency using the matrix of intra/inter-compartmental PPI frequency, based on analysis of reactions efficiencies distribution was suggested. An investigation of KEGG pathway efficiencies was conducted using the developed method. The clusterization and the ranking of KEGG pathways based on their efficiency were performed. "Amino acid metabolism" and "Genetic information processing" revealed the highest efficiencies among other functional classes of KEGG pathways. "Nervous system" and "Signaling molecules interaction" contained the most inefficient pathways. Statistically significant differences were found between efficiencies of KEGG and randomly-generated pathways. Based on these observations, the validity of this approach was discussed. CONCLUSION: The estimation of efficiency of signaling networks is a complicated task because of the need for the data on the kinetic reactions. However, the proposed method does not require such data and can be used for preliminary analysis of different protein networks.
BACKGROUND: Biological processes are usually distributed over various intracellular compartments. Proteins from diverse cellular compartments are often involved in similar signaling networks. However, the difference in the reaction rates between similar proteins among different compartments is usually quite high. We suggest that the estimation of frequency of intracompartmental as well as intercompartmental protein-protein interactions is an appropriate approach to predict the efficiency of a pathway. RESULTS: Using data from the databases STRING, ANDSystem, IntAct and UniProt, a PPI frequency matrix of intra/inter-compartmental interactions efficiencies was constructed. This matrix included 15 human-specific cellular compartments. An approach for estimating pathway efficiency using the matrix of intra/inter-compartmental PPI frequency, based on analysis of reactions efficiencies distribution was suggested. An investigation of KEGG pathway efficiencies was conducted using the developed method. The clusterization and the ranking of KEGG pathways based on their efficiency were performed. "Amino acid metabolism" and "Genetic information processing" revealed the highest efficiencies among other functional classes of KEGG pathways. "Nervous system" and "Signaling molecules interaction" contained the most inefficient pathways. Statistically significant differences were found between efficiencies of KEGG and randomly-generated pathways. Based on these observations, the validity of this approach was discussed. CONCLUSION: The estimation of efficiency of signaling networks is a complicated task because of the need for the data on the kinetic reactions. However, the proposed method does not require such data and can be used for preliminary analysis of different protein networks.
Authors: Björn Sommer; Benjamin Kormeier; Pavel S Demenkov; Patrizio Arrigo; Klaus Hippe; Özgür Ates; Alexey V Kochetov; Vladimir A Ivanisenko; Nikolay A Kolchanov; Ralf Hofestädt Journal: J Bioinform Comput Biol Date: 2013-02-18 Impact factor: 1.122
Authors: Marta Cascante; Laszlo G Boros; Begoña Comin-Anduix; Pedro de Atauri; Josep J Centelles; Paul W-N Lee Journal: Nat Biotechnol Date: 2002-03 Impact factor: 54.908
Authors: Damian Szklarczyk; Andrea Franceschini; Michael Kuhn; Milan Simonovic; Alexander Roth; Pablo Minguez; Tobias Doerks; Manuel Stark; Jean Muller; Peer Bork; Lars J Jensen; Christian von Mering Journal: Nucleic Acids Res Date: 2010-11-02 Impact factor: 16.971