Cellular cholesterol prediction of mammalian ATP-binding cassette (ABC) proteins based on fuzzy c-means with support vector machine algorithms

Abstract

Over the years protein interaction and prediction of membrane protein have been a pivotal research area for all researchers. For both prokaryotes and eukaryotes Adenosine Triphosphate-(ATP) binding cassette (ABC) genes plays a significant role. In our analysis, we concentrate on human part of ABC genes. In case of living organisms transport of precise molecules across lipid membranes has been treated as vital part and for that reason a bigger transporter is required to carry out the molecules. Here ABC transporter families are evolved to transport the specific molecules such as sugars, amino acid, peptides, proteins, ions etc. within the plasma membrane. As we know another important component of human being is cholesterol, which is a major component in cell membrane and its main functions are to maintain integrity and mechanical stability. Each and every time, membrane cholesterolsareinteracted with membrane protein in both N-C terminuses and target valid sequence(s) which has relevance in human diseases. In this manuscript we have applied Fuzzy C-Means (FCM) with Support Vector Machine (SVM) algorithm for prediction of cellular cholesterol with ABC genes. Our experiments have been performed well using ABCdata set.