Abstract
In this study, based on the predicted secondary structures of proteins, we propose a new approach to predict protein structural classes (α,β,α/β,α+β) for three widely used low-homology data sets. Fist, we obtain two time siries from the chaos game representation of each predicted secondary structure; second, based on two time series, we construct visibility and horizontal visibility network, respectively and generate a set of features using 17 network features; finaly, we predict each protein structure class using support vector machine and Fisher’s linear discriminant algorithm, respectively. In order to evaluate our method, the leave one out cross-validating test is employed on three data sets. Results show that our approach has been provided as a effective tool for the prediction of low-homology protein structural classes.
Highlights
The roles of proteins are varied and complex. Levitt and Chothia (1976) first propose the protein structural classes
When Support Vector Machine (SVM) is used to implement the classification prediction, the overall accuracies of 82.07, 79.03 and 80% are achieved for the data sets 25PDB, 1189 and 640, respectively; when Fisher’s linear discriminant algorithm is used to implement the classification prediction, the overall accuracies of 80.69, 79.40 and 80% are achieved for the data sets 25PDB, 1189 and 640, respectively
If comparing the four protein structural classes to each other, the predictions of proteins in the α classes are always the best
Summary
The roles of proteins are varied and complex. Levitt and Chothia (1976) first propose the protein structural classes. Levitt and Chothia (1976) first propose the protein structural classes. In their pioneering work, four structural classes of protein, namely all-α, all-β, α/β and α +β can be obtained. The all-α and all-β classes represent structures that consist of mainly α-helices and β-strands, respectively. The α/β and α +β classes contain both αhelices and β-strands which are mainly interspersed and segregated, respectively (Murzin et al, 1995). A knowledge of protein structure class is very important in both theoretical and experimental studies in protein science. For newly-found proteins, the structural class prediction method of automated and accurate are urgently needed. The problem of protein structural class prediction is very important towards the protein structure prediction problem. Despite the significance of this problem, when the sequence similarity rate is low, finding the most precise computational method to solve this problem still remains an unsolved problem
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