Fold Prediction Problem: The Application of New Physical and Physicochemical- Based Features

Abstract

One of the most important goals in bioinformatics is the ability to predict tertiary structure of a protein from its amino acid sequence. In this paper, new feature groups based on the physical and physicochemical properties of amino acids (size of the amino acids' side chains, predicted secondary structure based on normalized frequency of β-Strands, Turns, and Reverse Turns) are proposed to tackle this task. The proposed features are extracted using a modified feature extraction method adapted from Dubchak et al. To study the effectiveness of the proposed features and the modified feature extraction method, AdaBoost.M1, Multi Layer Perceptron (MLP), and Support Vector Machine (SVM) that have been commonly and successfully applied to the protein folding problem are employed. Our experimental results show that the new feature groups altogether with the modified feature extraction method are capable of enhancing the protein fold prediction accuracy better than the previous works found in the literature.

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

“…Among a wide range of classification techniques used to tackle this problem, SVM classifier has attained the best results for this task [5, 22, 26, 27]. The second group consists of studies that have mainly focused on proposing novel features that capture local and global discriminatory information to address protein structural class prediction problem such as sequence based information [10, 28–30], pseudo amino acid composition [31–33], physicochemical-based information [15, 22, 28, 34–36], and structural based information [5, 33, 37–40]. The most important enhancements in protein structural class prediction accuracy have been based on relying on these techniques rather than exploring the impact of classification techniques.…”

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

“…Among the classifiers employed to tackle the PFR, using support vector machine have attained the best results [26], [27], [28], [29], [30], [31], [32]. Similarly, a wide range of features have been extracted and used to tackle the PFR such as, physicochemical-based features [19], [23], [33], [34], sequence-based features [6], [14], [15], [32] evolutionarybased features [18], [25], [28], [30], and structural-based features [17], [18], [23], [35], [36], [37], [38]. Achieved results have shown that the most significant enhancement for the protein fold prediction accuracy has been achieved by relying on the feature extraction approaches rather than the classification techniques being used [4], [15], [19], [27], [28], [29], [39].…”

“…In most of the studies that addressed the PFR by feature extraction techniques, global discriminatory information has been represented using the composition of the amino acids feature group (the percentage of the occurrence of the amino acids along the protein sequence divided by the length of protein sequence [19], [22], [30]). However, it has been shown that this feature group is not able to adequately reveal global information as it is not able to capture information regarding the length of the protein sequence [39], [40] which was shown as effective feature for the PFR [33], [41].…”

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

“…The AAC features comprise of 20 features and physicochemical-based features comprise of 105 features (21 features for each of the attributes used). The features proposed by [1] have been widely used in the field of protein fold recognition [2]- [11]. Apart from the above mentioned 5 attributes used by [1], features also extracted by incorporating other attributes of the amino acids; and if the number of features is large then top few can be selected [12], [13].…”

“…Apart from the above mentioned 5 attributes used by [1], features also extracted by incorporating other attributes of the amino acids; and if the number of features is large then top few can be selected [12], [13]. Some of the other attributes used are: solvent accessibility [14], flexibility [15], bulkiness [16], first and second order entropy [17], and size of the side chain of the amino acids [11]. These physicochemical attributes are usually selected in an arbitrary way and recently a systematic way of selecting physicochemical attributes was proposed by [18].…”