Prediction of membrane protein types using maximum variance projection

Abstract

Predicting membrane protein types has a positive influence on further biological function analysis. To quickly and efficiently annotate the type of an uncharacterized membrane protein is a challenge. In this work, a system based on maximum variance projection (MVP) is proposed to improve the prediction performance of membrane protein types. The feature extraction step is based on a hybridization representation approach by fusing Position-Specific Score Matrix composition. The protein sequences are quantized in a high-dimensional space using this representation strategy. Some problems will be brought when analysing these high-dimensional feature vectors such as high computing time and high classifier complexity. To solve this issue, MVP, a novel dimensionality reduction algorithm is introduced by extracting the essential features from the high-dimensional feature space. Then, a K-nearest neighbour classifier is employed to identify the types of membrane proteins based on their reduced low-dimensional features. As a result, the jackknife and independent dataset test success rates of this model reach 86.1 and 88.4%, respectively, and suggest that the proposed approach is very promising for predicting membrane proteins types.