New Perspectives in Predicting Membrane Protein-protein Interactions

Cells are multi-molecular entities whose biological functions rely on stringent regulations both temporally and specially. These regulations are achieved through a variety of molecular interactions including protein-DNA interactions, protein-RNA interactions and protein-protein interactions (PPIs). PPIs are extremely important in a wide range of biological functions from enzyme catalysis, signal transduction and more structural functions. Owing to advanced large-scale techniques such as yeast two-hybrid and mass spectrometry, interactomes of several model organisms such as Saccharomyces cerevisiae (Gavin et al., 2006; Ho et al., 2002; Ito et al., 2001; Krogan et al., 2006; Uetz et al., 2000), Drosophila melanogaster (Formstecher et al., 2005; Giot et al., 2003) and Caenorhabditis elegans (Li et al., 2004) have recently been extensively studied. Such large-scale interaction networks have provided us with a good opportunity to explore and decipher new information from them. However, there are some limitations of these large-scale data sets: 1) the experimental techniques for detecting PPIs are time-consuming, costly and labor-intensive; 2) the quality of certain datasets is uneven; and 3) technical limitations such as the requirement to tag proteins of interest still exist. As a complementary alternative, computational approaches that identify PPIs have been studied intensively for years and have yielded some interesting results. Proteins with at least one transmembrane domain constitute 20% to 35% of all known proteins, and therefore account for an important fraction of the proteins involved in biological mechanisms. However, for several reasons, the research on membrane protein interactions has been lagging behind. First, although the current available interactomes contain adequate interactions for analysis, the data sets still have a large amount of false positives. For example, compared to a gold-standard data set, identified protein-protein interactions from three frequently-used high-throughput methods (yeast two-hybrid (Uetz, et al., 2000), tandem affinity purification (TAP) (Gavin, et al., 2006) and high-throughput mass spectrometry protein complex identification (HMS-PCI)) (Ho, et al., 2002) yielded very low accuracy, coverage and overlap (von Mering et al., 2002). Second, some large-scale experimental techniques are biased against membrane proteins. For instance, in order to check whether proteins interact or not, they need to be expressed in the nucleus which may not be their native living environment. Source: New Achievements in Evolutionary Computation, Book edited by: Peter Korosec, ISBN 978-953-307-053-7, pp. 318, February 2010, INTECH, Croatia, downloaded from SCIYO.COM