EnsemPPIS

EnsemPPIS predicts protein-protein interaction (PPI) sites by integrating transformer-based residue interaction extraction and gated convolutional networks into an ensemble framework that combines global and local sequence features for proteome-wide, sequence-based PPI site prediction.

Key Features:

• Transformer-Based Residue Interaction Extraction: Leverages transformer networks to extract residue interactions directly from protein sequences, enabling prediction without requiring experimental structural data.
• Gated Convolutional Networks Integration: Incorporates gated convolutional networks to capture local sequential features that complement transformer-derived global patterns.
• Ensemble Learning Strategy: Employs an ensemble learning strategy to integrate global and local sequential features with extracted residue interactions for PPI site prediction.
• Proteome-Wide Applicability: Designed for proteome-scale application to enable comprehensive profiling of PPI sites across entire proteomes.
• Interpretability and Pattern Analysis: Provides interpretability to analyze patterns learned by the model, revealing residue interaction signals within local sequence contexts.

Scientific Applications:

• Functional Annotation of Proteins: Predicts interaction residues to inform protein functional annotation.
• Drug Target Identification: Maps PPI sites to assist identification and prioritization of druggable interaction interfaces.
• Structural Biology Studies: Supplies sequence-based PPI site predictions to support studies when structural data are incomplete or unavailable.

Methodology:

Uses transformer networks to extract residue interactions from sequences, gated convolutional networks to capture local sequential features, and an ensemble learning strategy to integrate these components for PPI site prediction.