iSuc-PseAAC

iSuc-PseAAC predicts lysine succinylation sites in proteins to identify candidate succinylated residues for proteomic and functional studies.

Key Features:

• Sequence encoding: Pseudo amino acid composition (PseAAC) incorporating peptide position-specific propensity was used to represent peptide sequences.
• Algorithm and validation: A support vector machine (SVM) classifier was employed and performance was evaluated by leave-one-out cross-validation on a benchmark dataset.
• Performance metrics: Accuracy 79.94%, Sensitivity 51.07%, Specificity 89.42%, Matthews Correlation Coefficient (MCC) 0.431.

Scientific Applications:

• Proteomic site prediction: Predicting lysine succinylation sites in proteins to support identification of succinylated proteins, hypothesis generation, and experimental design in proteomics and molecular biology.

Methodology:

Pseudo amino acid composition with peptide position-specific propensity was used for sequence representation; a support vector machine was trained and assessed by leave-one-out cross-validation on a benchmark dataset.