MolPhase

MolPhase predicts protein phase separation behavior by utilizing diverse physicochemical features and extensive experimental datasets.

Key Features:

• Physicochemical feature integration: MolPhase incorporates electrostatic pi-interactions, disorder, and prion-like domains as key contributing factors for prediction.
• Sequence-based feature comparison: MolPhase compares distinct biophysical features side-by-side along protein sequences.
• Structural prediction integration: MolPhase enables comparison with structural predictions for efficient prediction of new phase-separating proteins.
• Hypothesis generation support: MolPhase guides hypothesis generation and experimental design for phase-separating proteins.

Scientific Applications:

• Phase-separating protein prediction: MolPhase predicts new phase-separating proteins and evaluates how biomolecule phase separation functions in biological systems.
• Bacterial effector analysis: MolPhase identifies that phytobacterial type III effectors are highly prone to homotypic phase separation, with patterns of association for multivalent interactions influencing the material properties of phase-separating droplets based on the surrounding microenvironment.

Methodology:

MolPhase applies a machine-learning algorithm utilizing physicochemical features and experimental datasets.