Abstract
This paper presents a novel feature vector based on physicochemical property of amino acids for prediction protein structural classes. The proposed method is divided into three different stages. First, a discrete time series representation to protein sequences using physicochemical scale is provided. Later on, a wavelet-based time-series technique is proposed for extracting features from mapped amino acid sequence and a fixed length feature vector for classification is constructed. The proposed feature space summarizes the variance information of ten different biological properties of amino acids. Finally, an optimized support vector machine model is constructed for prediction of each protein structural class. The proposed approach is evaluated using leave-one-out cross-validation tests on two standard datasets. Comparison of our result with existing approaches shows that overall accuracy achieved by our approach is better than exiting methods.
Highlights
Determination of protein structure from its primary sequence is an active area of research in bioinformatics
The concept of protein structural classes was originally introduced by Levitt and Chothia [1] based on a visual inspection of polypeptide chain topologies in a dataset of 31 globular proteins
The first dataset consists of 277 domains, of which 70 are all-α domains, 61 all-β domains, 81 are α/β domains, and 65 are α + β domains
Summary
Determination of protein structure from its primary sequence is an active area of research in bioinformatics. Understanding the rules relating the amino acid sequence to the three-dimensional structure of the protein is one of the major goals of contemporary molecular biology. The concept of protein structural classes was originally introduced by Levitt and Chothia [1] based on a visual inspection of polypeptide chain topologies in a dataset of 31 globular proteins. A protein (domain) is usually classified into one of the following four structural classes: all-α, all-β, α/β, and α + β. Structural class categorizes various proteins into groups that share similarities in the local folding patterns. The all-α and all-β classes represent structures that consist of mainly α-helices and β-strands, respectively. Prediction of structural classes is based on identifying these folding patterns based on thousands of already categorized proteins, and applying these patterns to unknown structures but known amino acid sequences. Structural Classification of Proteins (SCOP) [2] is one of the most accurate classifications of protein structural classes and has been constructed by visual inspection and comparison of structures by experts
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