SumSec: Accurate Prediction of Sumoylation Sites Using Predicted Secondary Structure

• Published in: Molecules (Basel, Switzerland) Vol. 23; no. 12; p. 3260
• Main Authors: Dehzangi, Abdollah, López, Yosvany, Taherzadeh, Ghazaleh, Sharma, Alok, Tsunoda, Tatsuhiko
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 10.12.2018; MDPI
• Subjects: Accuracy; Amino acid sequence; Amino acids; bagging; Bias; Computer applications; Datasets; ensemble classifier; Experimental methods; Gene regulation; Learning algorithms; Localization; Machine learning; post translational modification; Predictions; profile-bigram; Protein structure; Proteins; Secondary structure; Signal transduction; SUMO protein; sumoylation; Transcription; secondary structure; post translational modification; ensemble classifier; sumoylation; profile-bigram; bagging
• ISSN: 1420-3049; 1420-3049
• DOI: 10.3390/molecules23123260

Post Translational Modification (PTM) is defined as the modification of amino acids along the protein sequences after the translation process. These modifications significantly impact on the functioning of proteins. Therefore, having a comprehensive understanding of the underlying mechanism of PTMs turns out to be critical in studying the biological roles of proteins. Among a wide range of PTMs, sumoylation is one of the most important modifications due to its known cellular functions which include transcriptional regulation, protein stability, and protein subcellular localization. Despite its importance, determining sumoylation sites via experimental methods is time-consuming and costly. This has led to a great demand for the development of fast computational methods able to accurately determine sumoylation sites in proteins. In this study, we present a new machine learning-based method for predicting sumoylation sites called SumSec. To do this, we employed the predicted secondary structure of amino acids to extract two types of structural features from neighboring amino acids along the protein sequence which has never been used for this task. As a result, our proposed method is able to enhance the sumoylation site prediction task, outperforming previously proposed methods in the literature. SumSec demonstrated high sensitivity (0.91), accuracy (0.94) and MCC (0.88). The prediction accuracy achieved in this study is 21% better than those reported in previous studies. The script and extracted features are publicly available at: https://github.com/YosvanyLopez/SumSec.