Success: evolutionary and structural properties of amino acids prove effective for succinylation site prediction.

BACKGROUND: Post-translational modification is considered an important biological mechanism with critical impact on the diversification of the proteome. Although a long list of such modifications has been studied, succinylation of lysine residues has recently attracted the interest of the scientific community. The experimental detection of succinylation sites is an expensive process, which consumes a lot of time and resources. Therefore, computational predictors of this covalent modification have emerged as a last resort to tackling lysine succinylation. RESULTS: In this paper, we propose a novel computational predictor called 'Success', which efficiently uses the structural and evolutionary information of amino acids for predicting succinylation sites. To do this, each lysine was described as a vector that combined the above information of surrounding amino acids. We then designed a support vector machine with a radial basis function kernel for discriminating between succinylated and non-succinylated residues. We finally compared the Success predictor with three state-of-the-art predictors in the literature. As a result, our proposed predictor showed a significant improvement over the compared predictors in statistical metrics, such as sensitivity (0.866), accuracy (0.838) and Matthews correlation coefficient (0.677) on a benchmark dataset. CONCLUSIONS: The proposed predictor effectively uses the structural and evolutionary information of the amino acids surrounding a lysine. The bigram feature extraction approach, while retaining the same number of features, facilitates a better description of lysines. A support vector machine with a radial basis function kernel was used to discriminate between modified and unmodified lysines. The aforementioned aspects make the Success predictor outperform three state-of-the-art predictors in succinylation detection.

SucStruct: Prediction of succinylated lysine residues by using structural properties of amino acids.

Post-Translational Modification (PTM) is a biological reaction which contributes to diversify the proteome. Despite many modifications with important roles in cellular activity, lysine succinylation has recently emerged as an important PTM mark. It alters the chemical structure of lysines, leading to remarkable changes in the structure and function of proteins. In contrast to the huge amount of proteins being sequenced in the post-genome era, the experimental detection of succinylated residues remains expensive, inefficient and time-consuming. Therefore, the development of computational tools for accurately predicting succinylated lysines is an urgent necessity. To date, several approaches have been proposed but their sensitivity has been reportedly poor. In this paper, we propose an approach that utilizes structural features of amino acids to improve lysine succinylation prediction. Succinylated and non-succinylated lysines were first retrieved from 670 proteins and characteristics such as accessible surface area, backbone torsion angles and local structure conformations were incorporated. We used the k-nearest neighbors cleaning treatment for dealing with class imbalance and designed a pruned decision tree for classification. Our predictor, referred to as SucStruct (Succinylation using Structural features), proved to significantly improve performance when compared to previous predictors, with sensitivity, accuracy and Mathew's correlation coefficient equal to 0.7334-0.7946, 0.7444-0.7608 and 0.4884-0.5240, respectively.

PSSM-Suc: Accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction.

Post-translational modification (PTM) is a covalent and enzymatic modification of proteins, which contributes to diversify the proteome. Despite many reported PTMs with essential roles in cellular functioning, lysine succinylation has emerged as a subject of particular interest. Because its experimental identification remains a costly and time-consuming process, computational predictors have been recently proposed for tackling this important issue. However, the performance of current predictors is still very limited. In this paper, we propose a new predictor called PSSM-Suc which employs evolutionary information of amino acids for predicting succinylated lysine residues. Here we described each lysine residue in terms of profile bigrams extracted from position specific scoring matrices. We compared the performance of PSSM-Suc to that of existing predictors using a widely used benchmark dataset. PSSM-Suc showed a significant improvement in performance over state-of-the-art predictors. Its sensitivity, accuracy and Matthews correlation coefficient were 0.8159, 0.8199 and 0.6396, respectively.

Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams.

Post-translational modification refers to the biological mechanism involved in the enzymatic modification of proteins after being translated in the ribosome. This mechanism comprises a wide range of structural modifications, which bring dramatic variations to the biological function of proteins. One of the recently discovered modifications is succinylation. Although succinylation can be detected through mass spectrometry, its current experimental detection turns out to be a timely process unable to meet the exponential growth of sequenced proteins. Therefore, the implementation of fast and accurate computational methods has emerged as a feasible solution. This paper proposes a novel classification approach, which effectively incorporates the secondary structure and evolutionary information of proteins through profile bigrams for succinylation prediction. The proposed predictor, abbreviated as SSEvol-Suc, made use of the above features for training an AdaBoost classifier and consequently predicting succinylated lysine residues. When SSEvol-Suc was compared with four benchmark predictors, it outperformed them in metrics such as sensitivity (0.909), accuracy (0.875) and Matthews correlation coefficient (0.75).

Gene masking - a technique to improve accuracy for cancer classification with high dimensionality in microarray data.

BACKGROUND: High dimensional feature space generally degrades classification in several applications. In this paper, we propose a strategy called gene masking, in which non-contributing dimensions are heuristically removed from the data to improve classification accuracy. METHODS: Gene masking is implemented via a binary encoded genetic algorithm that can be integrated seamlessly with classifiers during the training phase of classification to perform feature selection. It can also be used to discriminate between features that contribute most to the classification, thereby, allowing researchers to isolate features that may have special significance. RESULTS: This technique was applied on publicly available datasets whereby it substantially reduced the number of features used for classification while maintaining high accuracies. CONCLUSION: The proposed technique can be extremely useful in feature selection as it heuristically removes non-contributing features to improve the performance of classifiers.