SPOTONE

SPOTONE predicts protein hot spots from amino acid sequences to identify residues critical for small ligand binding and protein–protein interaction interfaces.

Key Features:

• Sequence-Based Prediction: Predicts hot spots using sequence-only features without relying on three-dimensional structural data.
• Machine Learning Algorithm: Employs Extremely Randomized Trees (ERT) to classify hot-spot residues from sequence-derived features.
• Performance Metrics: Achieves accuracy 0.82, AUROC 0.83, precision 0.91, recall 0.82, and F1-score 0.85 on an independent test set.
• Input Format: Accepts protein sequences in FASTA format as input.

Scientific Applications:

• Molecular design: Identifies residues that serve as structural landmarks for small-ligand binding to guide molecular design.
• Drug discovery: Highlights candidate hot-spot residues that may influence binding affinity and target prioritization.
• Structural biology: Aids analysis of protein–protein interfaces when experimental three-dimensional structures are unavailable or incomplete.

Methodology:

Trains an Extremely Randomized Trees (ERT) machine-learning model on sequence-derived features to recognize patterns associated with hot spots, avoiding reliance on three-dimensional structural data.