Spectral Clustering Based Fuzzy C-Means Algorithm for Prediction of Membrane Cholesterol from ATP-Binding Cassette Transporters

In the human genome, biological membrane plays an imperative part in all cell organisms. Generally, cell membrane consists of two components such as lipids and proteins. Phospholipids and cholesterols are both treated as most abundant lipids in plasma membrane where cholesterol molecules are mostly hydrophobic in nature. In this paper, we described interaction of membrane cholesterol with transmembrane proteins. Cholesterol is a major constituent in membrane proteins which is not uniformly distributed in biological membrane, and it has other responsibility like membrane fluidity and lipid raft. In most eukaryotes, ATP-binding cassette (ABC) transporters are represented as superfamily among all transmembrane proteins. Here we focus on target of ABC transporters by membrane cholesterol and counting down of the binding sites between them. Basically, membrane cholesterol binds the membrane proteins for predicting valid signature motif from ABC transporter which gives significant value. In this paper, a computational approach has been implemented which is based on spectral clustering with fuzzy C-means algorithm to find different types of amino acid sequences from the binding region between ABC transporter and membrane cholesterol. Finally from our experiment, we achieved better prediction accuracy results which have biological significance.