PPA-Pred

PPA-Pred predicts binding affinities of protein–protein complexes from amino acid sequence features using models developed from experimental binding affinity data for 135 protein–protein complexes to characterize interaction strength and inform recognition mechanisms.

Key Features:

• Sequence-Based Feature Analysis: Uses features derived from amino acid sequences and evaluates correlations between those features and binding affinity drawn from an initial set of 642 sequence-derived properties.
• Classification by Function and Binding Site Residues: Classifies complexes by biological function and by the predicted percentage of binding site residues, accounting for factors such as complex type, molecular weight, and specific binding sites.
• Regression Models for Affinity Prediction: Employs class-specific regression models using three to five properties per model, validated by jack-knife testing with reported correlation coefficients ranging from 0.739 to 0.992.

Scientific Applications:

• Understanding Recognition Mechanisms: Enables investigation of how sequence-derived properties relate to molecular recognition in protein–protein interactions.
• Identifying Strong Binding Partners: Facilitates prioritization of strong binding partners within interaction networks based on predicted affinities.
• Class-Based Affinity Prediction: Provides affinity predictions that incorporate biological function and structural characteristics to reflect class-specific determinants of binding strength.

Methodology:

Analyzed relationships between binding affinity and 642 properties derived from amino acid sequences, found poor overall correlation without complex-specific classification, then applied class-based classification and regression modeling (three to five properties per model) validated by jack-knife testing with correlations 0.739–0.992 on a dataset of 135 experimental complexes.