Prediction of sumoylation sites in proteins using linear discriminant analysis

• AAIndex, position-specific amino acid propensity and modification of composition of k-space amino acid pairs were used to feature construction.
• 178 features were selected as the optimal features according to the MCC values in 10-fold cross validation.
• Linear Discriminant Analysis was the first applied in the Sumoylation prediction.
• The accuracy was 86.92% in the benchmark dataset.

Sumoylation is a multifunctional post-translation modification (PTM) in proteins by the small ubiquitin-related modifiers (SUMOs), which have relations to ubiquitin in molecular structure. Sumoylation has been found to be involved in some cellular processes. It is very significant to identify the exact sumoylation sites in proteins for not only basic researches but also drug developments. Comparing with time exhausting experiment methods, it is highly desired to develop computational methods for prediction of sumoylation sites as a complement to experiment in the post-genomic age. In this work, three feature constructions (AAIndex, position-specific amino acid propensity and modification of composition of k-space amino acid pairs) and five different combinations of them were used to construct features. At last, 178 features were selected as the optimal features according to the Mathew's correlation coefficient values in 10-fold cross validation based on linear discriminant analysis. In 10-fold cross-validation on the benchmark dataset, the accuracy and Mathew's correlation coefficient were 86.92% and 0.6845. Comparing with those existing predictors, SUMO_LDA showed its better performance.