PAIRPred

PAIRpred predicts partner-specific protein-protein interaction interfaces by using combined sequence and structural data from both interacting proteins to identify interacting residue pairs across protein complexes.

Key Features:

• Partner-specific prediction: Leverages combined sequence and structural data from both interacting proteins to predict residue interactions at protein complex interfaces.
• Pairwise kernels and SVM classifier: Encodes detailed sequence and structural information with pairwise kernels and trains a support vector machine (SVM) to distinguish interacting versus non-interacting residue pairs.
• Robustness to conformational changes: Maintains predictive reliability in the presence of binding-associated conformational changes.
• Accuracy and validation: Predicts protein-level binding sites and inter-protein residue contacts with validated performance on Docking Benchmark 4.0 and recent CAPRI targets.
• Performance analysis: Includes analysis of sequence and structural feature contributions and comparative evaluation against other interface prediction techniques.

Scientific Applications:

• Interface elucidation: Provides insights into the nature and specificity of interaction interfaces to aid study of complex biological processes.
• Binding-site and contact prediction: Identifies protein-level binding sites and inter-protein residue contacts in protein complexes.
• Viral-host interaction study: Applied to analyze the interaction between human ISG15 and influenza A virus NS1 protein as a case study of viral-host interactions.

Methodology:

PAIRpred encodes sequence and structural information from both proteins using pairwise kernels and trains a support vector machine (SVM) classifier to predict interacting versus non-interacting residue pairs.