Abstract
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 behaviors. Here we show that LLPSense, a machine learning framework that integrates protein language model embeddings with environmental parameters, achieves accurate, condition-aware predictions of phase separation. Multiple experimental validations confirm LLPSense's predictive power and utility. The model reveals complex, temperature-dependent reentrant behavior in SGTA, previously unrecognized as phase-separating. Moreover, LLPSense accurately predicts mutations in Parkinson's disease-associated α-synuclein that either enhance or suppress phase separation. Beyond predictive accuracy, model-guided mutagenesis enables the modulation of phase behavior. Collectively, LLPSense establishes a robust computational framework for interrogating phase landscapes, facilitating mechanistic disease studies and programmable condensate design.