Abstract
Liquid-liquid phase separation plays a critical role in cellular processes, including protein aggregation and RNA metabolism, by forming membraneless subcellular structures. Accurate identification of phase-separated proteins is essential for understanding and controlling these processes. Traditional identification methods are effective but often costly and time-consuming. The recent machine learning methods have reduced these costs, but most models are restricted to classifying scaffold and client proteins with limited experimental conditions. To address this limitation, we developed a Mamba-based encoder using contrastive learning that incorporates separation probability, protein type, and experimental conditions. Our model achieved 95.2% accuracy in predicting phase-separated proteins and an ROCAUC score of 0.87 in classifying scaffold and client proteins. Further validation in the DgHBP-2 drug delivery system demonstrated its potential for condition modulation in drug development. This study provides an effective framework for the accurate identification and control of phase separation, facilitating advancements in biomedical research and therapeutic applications.