No abstract

Protein-peptide interactions are essential for all cellular processes including DNA repair, replication, gene-expression, and metabolism. As most protein-peptide interactions are uncharacterized, it is cost effective to investigate them computationally as the first step. All existing approaches for predicting protein-peptide binding sites, however, are based on protein structures despite the fact that the structures for most proteins are not yet solved. This article proposes the first machine-learning method called SPRINT to make Sequence-based prediction of Protein-peptide Residue-level Interactions. SPRINT yields a robust and consistent performance for 10-fold cross validations and independent test. The most important feature is evolution-generated sequence profiles. For the test set (1056 binding and non-binding residues), it yields a Matthews' Correlation Coefficient of 0.326 with a sensitivity of 64% and a specificity of 68%. This sequence-based technique shows comparable or more accurate than structure-based methods for peptide-binding site prediction. SPRINT is available as an online server at: http://sparks-lab.org/. © 2016 Wiley Periodicals, Inc.

No abstract

Supplementary data are available at Bioinformatics online.

Post-translational modification refers to the biological mechanism involved in the enzymatic modification of proteins after being translated in the ribosome. This mechanism comprises a wide range of structural modifications, which bring dramatic variations to the biological function of proteins. One of the recently discovered modifications is succinylation. Although succinylation can be detected through mass spectrometry, its current experimental detection turns out to be a timely process unable to meet the exponential growth of sequenced proteins. Therefore, the implementation of fast and accurate computational methods has emerged as a feasible solution. This paper proposes a novel classification approach, which effectively incorporates the secondary structure and evolutionary information of proteins through profile bigrams for succinylation prediction. The proposed predictor, abbreviated as SSEvol-Suc, made use of the above features for training an AdaBoost classifier and consequently predicting succinylated lysine residues. When SSEvol-Suc was compared with four benchmark predictors, it outperformed them in metrics such as sensitivity (0.909), accuracy (0.875) and Matthews correlation coefficient (0.75).