SumSec: Accurate Prediction of Sumoylation Sites Using Predicted Secondary Structure

Abstract

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: .

Current Methods of Post-Translational Modification Analysis and Their Applications in Blood Cancers

“…SILAC, iTRAQ and LFQ have been used for quantitation of sumoylation [ 138 , 143 , 144 ]. Various bioinformatic tools such as SUMmOn, SUMOhydro, SumSec, etc., have been introduced for sumoylation site identification [ 129 , 145 , 146 , 147 ].…”

Drug design targeting active posttranslational modification protein isoforms

“…Due to the limitations of one specific ML algorithm, the ensemble learning methods are used to build the models, where the use of multiple learning algorithms other than standalone one could improve the predictive outcomes. Case studies have utilized, random forests 157 or ensemble random forest, 158 AdaBoost, 178 light gradient boosting machine, 160 and bagging 179 to gain better prediction performance. These ML algorithms have been successfully used to predict the binding site of acetylation, 180 methylation, 181 malonylation, 55 and sumoylation 182 .…”

PupStruct: Prediction of Pupylated Lysine Residues Using Structural Properties of Amino Acids

“…Furthermore, SPIDER2 also gives the local structure with the highest probability as one L × 3 matrix, where L depicts the protein length, and the three columns are the corresponding probabilities contribution to each local structure ph, pe and pc. Hence, to simplify, we denote this matrix as SSPre [69].…”