A Segmentation-Based Method to Extract Structural and Evolutionary Features for Protein Fold Recognition

Abstract

Abstract-Protein fold recognition (PFR) is considered as an important step towards the protein structure prediction problem. Despite all the efforts that have been made so far, finding an accurate and fast computational approach to solve the PFR still remains a challenging problem for bioinformatics and computational biology. In this study, we propose the concept of segmented-based feature extraction technique to provide local evolutionary information embedded in position specific scoring matrix (PSSM) and structural information embedded in the predicted secondary structure of proteins using SPINE-X. We also employ the concept of occurrence feature to extract global discriminatory information from PSSM and SPINE-X. By applying a support vector machine (SVM) to our extracted features, we enhance the protein fold prediction accuracy for 7.4 percent over the best results reported in the literature. We also report 73.8 percent prediction accuracy for a data set consisting of proteins with less than 25 percent sequence similarity rates and 80.7 percent prediction accuracy for a data set with proteins belonging to 110 folds with less than 40 percent sequence similarity rates. We also investigate the relation between the number of folds and the number of features being used and show that the number of features should be increased to get better protein fold prediction results when the number of folds is relatively large.

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.…”

“…The main reason is that previous studies failed to capture local discriminatory information embedded in PSSM properly. They have mainly tried to extract this local information using the protein sequence as a single block which has failed to achieve this goal (Nanni et al, 2013b(Nanni et al, , 2013aDehzangi et al, 2014a).…”

“…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).…”

A Tri-Gram Based Feature Extraction Technique Using Linear Probabilities of Position Specific Scoring Matrix for Protein Fold Recognition

“…It is also shown that by fusion of features the recognition rates can be improved [30]- [33]. For the latter task case, several classifiers have been developed or used including linear discriminant analysis [34], [35], Bayesian classifiers [2], Bayesian decision rule [36], k-nearest neighbor [25], [37], Hidden Markov model [38], [39], artificial neural network [40], [41], support vector machine (SVM) [6], [20], [21], [42], [43], and ensemble classifiers [20], [33], [41], [44], [45]. Among these classifiers, SVM (or SVM-based for ensemble strategy) classifier exhibits quite promising results [21], [26], [27].…”