A simple and efficient predictor for identifying succinylation sites. SuccinSite predicts protein succinylation sites by incorporating three sequence encodings, i.e., k-spaced amino acid pairs, binary and amino acid index properties. Then, the random forest classifier was trained with these encodings to build the predictor. The SuccinSite predictor achieves an AUC score of 0.802 in the 5-fold cross-validation set and performs significantly better than existing predictors on a comprehensive independent test set. Furthermore, informative features and predominant rules (i.e. feature combinations) were extracted from the trained random forest model for an improved interpretation of the predictor. Finally, we also compiled a database covering 4411 experimentally verified succinylation proteins with 12 456 lysine succinylation sites.
Predicts lysine succinylation sites in proteins. iSuc-PseOpt has been developed by incorporating the sequence-coupling effects into the general pseudo amino acid composition and using KNNC (K-nearest neighbors cleaning) treatment and IHTS (inserting hypothetical training samples) treatment to optimize the training dataset. Users can easily get their desired results without needing to go through the complicated mathematical equations involved.
Predicts lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. Compared with the existing predictors in this area, pSuc-Lys can achieve remarkably higher success rates. For the convenience of most experimental scientists, we have provided its web-server and a step-by-step guide, by which users can easily obtain their desired results without the need to go through the mathematical formulations.
A succinylation site predictor is proposed by incorporating the peptide position-specific propensity into the general form of pseudo amino acid composition. The accuracy is 79.94%, sensitivity 51.07%, specificity 89.42% and MCC 0.431 in leave-one-out cross validation with support vector machine algorithm. It demonstrated by rigorous leave-one-out on stringent benchmark dataset that the predictor is quite promising and may become a useful high throughput tool in this area.
Improves lysine succinylation prediction. SucStruct is designed to discriminate succinylated from non-succinylated lysine residues. It is based on nine structural features like accessible surface area, backbone torsion angles and probability of amino acid contribution to local structure conformations. The tool is able to classify succinylated lysine residues with 0.7334 sensitivity, 0.7444 accuracy and 0.4884 Mathew’s correlation coefficient.
Offers a method dedicated to stoichiometry quantification. StoichiolyzeR proposes a package leaning on the combined exploitation of information related to precursor and fragment ions data gathered from data-independent acquisitions (DIAs). This application is an approach which is able to consider peptides that include multiple lysine residues to census site-specific acylation stoichiometry.
Predicts protein succinylation sites by using multiple feature encoding schemes. Promising results were obtained by the SucPred predictor with an accuracy of 88.65% using 5-fold cross validation on the training dataset and an accuracy of 84.40% on the independent testing dataset, which demonstrated that the positive samples only learning algorithm presented here was particularly useful for identification of protein succinylation sites.