PROBselect

PROBselect selects the most suitable sequence-based predictor of protein-binding residues (PBRs) for each input protein to improve residue-level prediction accuracy and reduce cross-prediction between interaction partners.

Key Features:

• Dynamic predictor selection: Selects the most suitable PBR predictor for each input protein based on sequence-derived characteristics rather than applying a fixed consensus.
• Regression-based models: Uses regression models to estimate expected performance of candidate PBR predictors from the input sequence and recommend the top-performing method per protein.
• Empirical performance assessment: Built from empirical comparisons across multiple representative PBR predictors and leverages complementarity between methods to inform selection.
• Predictive quality information: Outputs residue-level predictions together with an expected predictive-quality estimate for each result.

Scientific Applications:

• Protein function prediction: Infers functional mechanisms and interaction roles by identifying protein-binding residues.
• Protein–protein docking calculations: Prioritizes plausible interface regions to guide docking search and scoring.

Methodology:

Empirical evaluation of nine representative PBR predictors, analysis of predictor complementarity, association of sequence-derived protein characteristics with expected predictor accuracy, and implementation of a dynamic per-protein selection strategy using regression models to estimate predictor performance.