Support Vector Machine-based classification of protein folds using the structural properties of amino acid residues and amino acid residue pairs

Shamim, Mohammad Tabrez Anwar; Anwaruddin, Mohammad; Nagarajaram, Hampapathalu A.

doi:10.1093/bioinformatics/btm527

Cited by 96 publications

(96 citation statements)

References 23 publications

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“…Machine learning methods, such as support vector machine and neural networks, are powerful classifiers which are being used for protein structure prediction and fold prediction problems with features based on amino acid sequence [15]. In this paper, we illustrated classifiers, namely decision tree with the principal objective of gaining understanding of the results generated for each class [19] Figure: 4 shows performance measure, Actual True Positive rate (Recall).…”

Section: Classification Resultsmentioning

confidence: 99%

Mining Dense Patterns from Off Diagonal Protein Contact Maps

Swaroopa¹,

Vani²

2012

IJCA

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The three dimensional structure of proteins is useful to carry out the biophysical and biochemical functions in a cell. Protein contact maps are 2D representations of contacts among the amino acid residues in the folded protein structure. Proteins are biochemical compounds consisting of one or more polypeptides, facilitating a biological function. Many researchers make note of the way secondary structures are clearly visible in the contact maps where helices are seen as thick bands and the sheets as orthogonal to the diagonal. In this paper, we explore several machine learning algorithms to data driven construction of classifiers for assigning protein off diagonal contact maps. A simple and computationally inexpensive algorithm based on triangle subdivision method is implemented to extract twenty features from off diagonal contact maps. This method successfully characterizes the offdiagonal interactions in the contact map for predicting specific folds. NaiveBayes, J48 and REPTree classification results with Recall 76.38%, 91.66% and 80.32% are obtained respectively. General TermsProtein Contact Map.

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Section: Classification Resultsmentioning

confidence: 99%

Mining Dense Patterns from Off Diagonal Protein Contact Maps

Swaroopa¹,

Vani²

2012

IJCA

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“…A wide range of classification techniques such as, artificial neural networks (ANN) [3], [4], [5], [6], [7], [8], meta classifiers [9], [10], [11], [12], [13], K-nearest neighbors [14], [15], [16], [17], [18] and support vector machines (SVM) [19], [20], [21], [22], [23], [24], [25], [26], [27] have been used for the PFR. 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], …”

Section: Introductionmentioning

confidence: 99%

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

Dehzangi

Paliwal

Lyons

et al. 2014

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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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.

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“…At first, amino acid composition (AAC) was used to predict T3SEs because researchers have detected amino acid composition biases in T3SEs [17]. Later, the approaches based on amino acid pair composition (AAPC) or motif emerged [18], [19]. However, the classification results are not satisfying because no defined consensus motifs or features have been discovered for T3SEs.…”

Section: Introductionmentioning

confidence: 99%

Sequence-Based Feature Extraction for Type III Effector Prediction

Sui¹,

Yang²

2013

IJBBB

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Abstract-The type III secretion system (T3SS) is a complex structure which allows gram-negative pathogens to destroy eukaryotic cell biology by injecting virulence factors directly into the host cell cytoplasm. Composed of around 30 proteins, T3SS is among the most complex secretion systems identified in Gram-negative bacteria. Since type III secreted effectors (T3SEs) are essential for the pathogenicity, identification of T3SEs is one of the core problems in computational biology. This paper puts forward a new method for the prediction of T3SEs. The method is a sequence-based approach which can extract useful features from amino acid sequences. By calculating the frequency of the features from different segments of protein sequences, the data set is represented by the feature vectors and classified by Support Vector Machine (SVM). The experimental results show superiority over other available approaches on classification accuracy.Index Terms-Type III secreted effector prediction, sequence-based approach, feature extraction, type III secretion, word segmentation, hybrid feature system.

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Support Vector Machine-based classification of protein folds using the structural properties of amino acid residues and amino acid residue pairs

Abstract: Dataset and stand-alone program are available upon request.

Cited by 96 publications

References 23 publications

Mining Dense Patterns from Off Diagonal Protein Contact Maps

Mining Dense Patterns from Off Diagonal Protein Contact Maps

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

Sequence-Based Feature Extraction for Type III Effector Prediction

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