PhosPPI

PhosPPI predicts the effects of phosphorylation on protein-protein interactions (PPIs) using a sequence-based machine learning approach to assess regulation of cellular signaling pathways.

Key Features:

• Machine Learning Approach: Sequence-based machine learning models analyze protein sequences to infer phosphorylation-induced changes in PPIs.
• High Accuracy and Performance: Demonstrates superior performance metrics, including higher accuracy and area under the curve (AUC), when benchmarked against Betts, HawkDock, and FoldX.
• Functional Site Identification: Identifies phosphorylation sites predicted to modulate PPIs and thereby affect protein function.

Scientific Applications:

• Disease Mechanism Exploration: Facilitates investigation of molecular mechanisms in diseases such as cancer and Alzheimer's disease by predicting phosphorylation-mediated PPI alterations.
• Drug Development: Supports identification of phosphorylation-associated pathways and potential therapeutic targets for drug discovery.

Methodology:

Uses sequence data and sequence-based machine learning models trained on known datasets to predict how specific phosphorylation events alter protein-protein interactions.