Proposing a highly accurate protein structural class predictor using segmentation-based features

Abstract

BackgroundPrediction of the structural classes of proteins can provide important information about their functionalities as well as their major tertiary structures. It is also considered as an important step towards protein structure prediction problem. Despite all the efforts have been made so far, finding a fast and accurate computational approach to solve protein structural class prediction problem still remains a challenging problem in bioinformatics and computational biology.ResultsIn this study we propose segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins. By applying SVM to our extracted features, for the first time we enhance the protein structural class prediction accuracy to over 90% and 85% for two popular low-homology benchmarks that have been widely used in the literature. We report 92.2% and 86.3% prediction accuracies for 25PDB and 1189 benchmarks which are respectively up to 7.9% and 2.8% better than previously reported results for these two benchmarks.ConclusionBy proposing segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins, we are able to enhance the protein structural class prediction performance significantly.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-S1-S2) contains supplementary material, which is available to authorized users.

Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳s general PseAAC

“…It is also widely used in Bioinformatics and has outperformed other classifiers and obtained promising results for protein subcellular localization as well as similar studies (Dehzangi et al, 2014a(Dehzangi et al, , 2014bDong et al, 2009;Yang and Chen, 2011;Lyon et al, 2014). It aims to reduce the prediction error rate by finding the hyperplane that produces the largest margin based on the concept of support vector theory.…”

“…In this study, we have used the logodds values to extract our features. It was shown in the literature that using these numbers produce similar output as using the probability values (Dehzangi et al, 2014a(Dehzangi et al, , 2014b. In the following subsections these four feature extraction methods will be explained in detail.…”

“…protein consensus sequence, we produce the occurrence feature group as a 20 dimensional feature vector consisting of the number of occurrence of each amino acid along the protein consensus sequence. Despite its simplicity, the occurrence feature group has shown its effectiveness in maintaining global discriminatory information with respect to the length of a protein sequence Taguchi and Gromiha, 2007;Dehzangi et al, 2014b).…”

“…To capture this information, we propose two segmentation-based feature extraction techniques, namely: segmented distribution method to capture local density and distribution information and segmented auto covariance method to capture local information about the interaction and correlation of neighboring amino acids. These feature groups have attained promising results in similar studies (Dehzangi et al, 2014a(Dehzangi et al, , 2013c(Dehzangi et al, , 2014b. Furthermore, we employ the occurrence feature group to capture global information from evolutionary information embedded in PSSM and to complement local information extracted using the segmented distribution and the segmented auto covariance features (Taguchi and Gromiha, 2007).…”

Low-Homology Protein Structural Class Prediction from Secondary Structure Based on Visibility and Horizontal Visibility Network

“…Yu et al (2017) use Chous pseudo amino acid composition and wavelet denoising to prediction structural class. From 2014 to now, several papers (Dehzangi et al, 2014;Wang et al, 2014;Jones, 1999;Faraggi et al, 2012) show that the protein secondary structure is significanc to predict protein structural classes. Firstly the features are extracted, secondly all kinds of algorithms can be used to implement the classification prediction, such as Fisher's linear discriminant algorithm (Yang et al, 2009), Support Vector Machine (SVM) (Cai et al, 2003) and so on.…”