A Fractal Dimension and Wavelet Transform Based Method for Protein Sequence Similarity Analysis

Abstract

One of the key tasks related to proteins is the similarity comparison of protein sequences in the area of bioinformatics and molecular biology, which helps the prediction and classification of protein structure and function. It is a significant and open issue to find similar proteins from a large scale of protein database efficiently. This paper presents a new distance based protein similarity analysis using a new encoding method of protein sequence which is based on fractal dimension. The protein sequences are first represented into the 1-dimensional feature vectors by their biochemical quantities. A series of Hybrid method involving discrete Wavelet transform, Fractal dimension calculation (HWF) with sliding window are then applied to form the feature vector. At last, through the similarity calculation, we can obtain the distance matrix, by which, the phylogenic tree can be constructed. We apply this approach by analyzing the ND5 (NADH dehydrogenase subunit 5) protein cluster data set. The experimental results show that the proposed model is more accurate than the existing ones such as Su's model, Zhang's model, Yao's model and MEGA software, and it is consistent with some known biological facts.