Using a Fuzzy Support Vector Machine Classifier to Predict Interactions of Membrane Protein

At present, about a quarter of all genes in most genomes contain transmembrane (TM) helices, and among the overall cellular interactome, helical membrane protein interactions are a major component. Interactions between membrane proteins play a significant role in a variety of cellular phenomena, including the transduction of signals across membranes, the transfer of membrane proteins between the plasma membrane and internal organelles, and the assembly of oligomeric protein structures. However, current experimental techniques for large-scale detection of protein-protein interactions are biased against membrane proteins. In this paper, a novel method is presented for the prediction of membrane protein interactions by using a fuzzy support vector machine (FSVM) classifier. The FSVM classifier is proposed to predict the interaction of integral membrane proteins. Jackknife tests on the working datasets indicate that the prediction accuracies are in the range of 51%-79%. The results show that the approach might hold a high potential to become a useful tool in prediction of membrane protein interactions.