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

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A Tri-Gram Based Feature Extraction Technique Using Linear Probabilities of Position Specific Scoring Matrix for Protein Fold Recognition A Tri-Gram Based Feature Extraction Technique Using Linear Probabilities of Position Specific Scoring Matrix for Protein Fold Recognition Author Paliwal, Kuldip K, Sharma, Alok, Lyons, James, Dehzangi, Abdollah Published 2014 Journal Title IEEE Transactions on NanoBioscience DOI https://doi.org/10.1109/TNB.2013.2296050 Copyright Statement © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Downloaded from http://hdl.handle.net/10072/67288 Griffith Research Online https://research-repository.griffith.edu.au A Tri-gram Based Feature Extraction Technique Using Linear Probabilities of Position Specific Scoring Matrix for Protein Fold Recognition Kuldip K. Paliwal, Member, IEEE, Alok Sharma, Member, IEEE, James Lyons, Abdollah Dhezangi, Member, IEEE protein structure in a reasonable amount of time. Abstract— In biological sciences, the deciphering of a three The prime objective of protein fold recognition is to find the dimensional structure of a protein sequence is considered to be an fold of a protein sequence. Assigning of protein fold to a important and challenging task. The identification of protein folds protein sequence is a transitional stage in the recognition of from primary protein sequences is an intermediate step in three dimensional structure of a protein. The protein fold discovering the three dimensional structure of a protein. This can be done by utilizing feature extraction technique to accurately recognition broadly covers feature extraction task and extract all the relevant information followed by employing a classification task. For the former task, several feature suitable classifier to label an unknown protein. In the past, several extraction techniques have been developed. Dubchak et al., feature extraction techniques have been developed but with have proposed syntactical and physicochemical-based features limited recognition accuracy only. In this study, we have for protein fold recognition [1]. They used amino acids’ developed a feature extraction technique based on tri-grams composition (AAC) as syntactical-based features and 5 computed directly from Position Specific Scoring Matrices. The effectiveness of the feature extraction technique has been shown following attributes of amino acids for deriving on two benchmark datasets. The proposed technique exhibits up physicochemical-based features namely, hydrophobicity (H), to 4.4% improvement in protein fold recognition accuracy predicted secondary structure based on normalized frequency compared to the state-of-the-art feature extraction techniques. of α-helix (X), polarity (P), polarizability (Z) and van der Waals volume (V). They used three descriptors (composition, Index Terms— Protein fold recognition, Tri-gram, Position transition and distribution) to compute the features. The AAC Specific Scoring Matrix (PSSM), feature extraction technique, Support Vector Machine (SVM). features comprise of 20 features and physicochemical-based features comprise of 105 features (21 features for each of the I. INTRODUCTION attributes used). The features proposed by [1] have been widely used in the field of protein fold recognition [2], [3], [4], [5], [6], ROTEIN fold recognition is an important and challenging [7], [8], [9], [10], [11]. Apart from the above mentioned 5 task in biological science, biomedicine, bioinformatics and P attributes used by [1], features also extracted by incorporating drug design. The identification of a three dimensional structure other attributes of the amino acids; and if the number of features of a protein sequence provides objective information about the is large then top few can be selected [12], [13]. Some of the characterization of a protein. This would assist in other attributes used are: solvent accessibility [14], flexibility understanding protein heterogeneity, protein-protein [15], bulkiness [16], first and second order entropy [17], and interactions and protein-peptide interactions. Though it is size of the side chain of the amino acids [11]. These possible to determine the structure of a protein by physicochemical attributes are usually selected in an arbitrary crystallography methods, it is usually a very slow and time way and recently a systematic way of selecting consuming process. The copiousness of protein data in this era physicochemical attributes was proposed by [18]. Taguchi and requires the advancements of computational ways to decipher Gromiha [19] proposed features which are based on amino acids’ occurrence; Shamim et al., [20] have extracted features Kuldip K. Paliwal is with School of Engineering, Griffith University, from the structural information of amino acid residues and Brisbane, Australia. Email: [email protected] amino acid residue pairs; Ghanty and Pal, [21] proposed Alok Sharma is with School of Engineering and Physics, University of the pairwise frequencies of amino acids separated by one residue South Pacific, Fiji and also with the Institute for Integrated and Intelligent Systems (IIIS), Griffith University. Email: [email protected] (PF1) and pairwise frequencies of adjacent amino acid residues James Lyons is with School of Engineering, Griffith University, Brisbane, (PF2). There are 400 features each in PF1 and PF2. These Australia. Email: [email protected] pairwise frequency features (PF) are used as in the augmented Corresponding author: Abdollah Dehzangi is with Institute for Integrated and Intelligent Systems (IIIS), Griffith University, and National ICT Australia form in the study conducted by [22], thereby, having 800 (NICTA), Brisbane, Australia. Email: [email protected] features. Thus, the feature vector of PF has 800 features. Chou [23] proposed pseudo-amino acid composition (A) based instead, then the problem of having mostly zeros in a feature features to effectively represent protein sequence. Dong et al., vector still remains. [24] have shown autocross-covariance (ACC) transformation Instead of computing tri-gram features either from the for protein fold recognition. Shen and Chou [25], Kurgan et al., primary protein sequence or the consensus sequence, we [26] and Liu et al., [27] have shown autocorrelation features for compute in this paper tri-gram features directly from PSSM. protein sequence, and Dehzangi et al., [28] derived features by This is done by accumulating the probabilities of each of the considering more physicochemical properties. Sharma et al. tri-gram using the probability information contained in PSSM. [29] have derived bi-gram features using evolutionary Since in this procedure we are utilizing linear probabilities to information (PSSM). It is also shown that by fusion of features compute tri-gram features and all the combinations of tri-grams the recognition rates can be improved [30], [31], [32], [33]. For occur in PSSM, we avoid having zeros in the feature vector. the latter task case, several classifiers have been developed or Therefore, our procedure would retrieve more information used including linear discriminant analysis [34], [35], Bayesian useful for the protein fold recognition. Note that we can classifiers [2], Bayesian decision rule [36], k-nearest neighbor interpret our procedure of computing the tri-gram features from [25], [37], Hidden Markov model [38], [39], artificial neural PSSM as the soft procedure, while the procedure used in earlier network [40], [41], support vector machine (SVM) [6], [20], studies (where tri-gram features are computed by counting the [21], [42], [43] and ensemble classifiers [20], [33], [41], [44], occurrence of individual amino acid triplets from the protein [45]. Among these classifiers, SVM (or SVM-based for sequence) can be considered as the hard procedure. ensemble strategy) classifier exhibits quite promising results In experiment, we apply our procedure on two benchmarks [21], [26], [27]. namely Taguchi and Gromiha (TG), [19] dataset and extended In order to decipher protein structure properly, the features Ding and Dubchak (EDD) [6] dataset. We performed k-fold extracted from a protein sequence should have relevant cross-validation on the datasets and obtained very promising information for fold discrimination. This implies the necessity recognition performance. On TG dataset we get protein fold of carefully developing the feature extraction technique. recognition accuracy of 72.5% and on EDD-dataset we get Therefore, in this paper we focus on developing feature 86.2% using SVM classifier. extraction technique to examine the recognition performance. Since SVM classifier provides high recognition accuracy, we II. DATASET use SVM classifier to compare the performance of our feature In this study, two datasets TG and EDD are utilized. The TG extraction technique with other feature extraction techniques. dataset extracted by [19] consists of 1612 proteins belonging to Our proposed feature extraction technique is based on the novel 30 most populated folds from the SCOP 1.73. The sequence way of finding the neighborhood information of amino acids in similarity of protein of TG datasets is no more than 25%. We a protein sequence via tri-grams. extract the EDD dataset from the latest version of the SCOP Markowetz et al. [46] have shown the importance of using
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