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

• Published in: BMC genomics Vol. 19; no. Suppl 1; pp. 923 - 114
• Main Authors: López, Yosvany, Sharma, Alok, Dehzangi, Abdollah, Lal, Sunil Pranit, Taherzadeh, Ghazaleh, Sattar, Abdul, Tsunoda, Tatsuhiko
• Format: Journal Article
• Language: English
• Published: London BioMed Central 19.01.2018; BioMed Central Ltd; BMC
• Subjects: Amino acid sequence; Amino acids; Animal Genetics and Genomics; Biomedical and Life Sciences; Genetic engineering; Genetic research; Life Sciences; Lysine; Lysine succinylation; Methods; Microarrays; Microbial Genetics and Genomics; Plant Genetics and Genomics; Post-translational modification; Prediction; Properties; Protein sequences; Proteomics; Post-translational modification; Protein sequences; Amino acids; Lysine succinylation; Prediction
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-017-4336-8

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.