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Succinylation site detection software tools | Post-translational modification data analysis

Compared with the labor-intensive and time-consuming experimental approaches, computational predictions of succinylation sites are much desirable due to their convenient and fast speed.

Source text:
(Zhao et al., 2015) Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique. J Theor Biol.

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Identifies multiple lysine post-translational modification (PTM) sites and their different types. iPTM-mLys represents the first multi-label PTM predictor ever established. The novel predictor is featured by incorporating the sequence-coupled effects into the general PseAAC, and by fusing an array of basic random forest classifiers into an ensemble system. Rigorous cross-validations via a set of multi-label metrics indicate that the first multi-label PTM predictor is very promising and encouraging.
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.
SucStruct / Succinylation using Structural features
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.
PSSM-Suc / Position Specific Scoring Matrix into bigram for Succinylation prediction
Uses an efficient combination of position specific scoring matrix (PSSM) + bigram for succinylation prediction. PSSM-Suc is a predictor that efficiently utilizes evolutionary features for predicting succinylated lysines. In this application, the PSSM was computed for each protein. A segment comprising 15 amino acids upstream and downstream corresponding to each lysine residue was considered for feature extraction.
pSuc-PseRat / Predicting lysine succinylation in proteins by exploiting the ratios of sequence-coupling and properties
Predicts lysine succinylation sites in proteins. pSuc-PseRat is available through a web server for convenient use by researchers. It achieves remarkably higher performance measurements. The tool will be a valuable high-throughput tool that aids basic research and drug development. It can classify compounds as succinylation sites and non-succinylation sites using only their protein sequence.
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