A machine learning framework for predicting and modulating condition-dependent protein phase separation.

Abstract excerpt

Protein phase separation is a fundamental process in organizing membraneless organelles and is implicated in pathological conditions. Importantly, this process is dynamic and depends on conditions such as concentration, temperature, and solvent composition. However, current machine learning models infer phase separation propensity solely from amino acid sequences, failing to capture these context-dependent...

Topics

• Phase Separation
• Machine Learning
• alpha-Synuclein
• Mutation
• Predictive Learning Models
• Proteins
• Parkinson Disease