A machine learning model called PSPHunter has been developed to precisely predict key residues involved in protein phase separation. Phase separation is a critical process in cellular functions such as transcriptional control, cell fate transitions, and disease mechanisms. PSPHunter uses a combination of sequence and functional features to identify these key residues, which are crucial for phase separation dynamics. The model was validated using in vivo and in vitro experiments, showing that truncating key residues in GATA3 disrupts phase separation, enhances tumor cell migration, and inhibits growth. Glycine and proline are enriched in key residues, and PSPHunter identifies nearly 80% of disease-associated phase-separating proteins. The model also reveals that mutations in key residues, particularly glycine and proline, significantly impact phase separation, linking them to disease mechanisms. PSPHunter was developed by integrating protein sequences and functional features from existing databases. It was trained on a dataset of 167 human phase-separating proteins and used to predict phase-separating proteins and key residues. The model demonstrated high accuracy in predicting phase-separating proteins and identified key residues that significantly influence phase separation. PSPHunter's predictions were validated through experiments showing that truncating key residues in GATA3 disrupts phase separation, while restoring phase separation in mutated GATA3 rescues tumor cell phenotypes. The model also identified that glycine and proline are enriched in key residues, and mutations in these residues have a significant impact on phase separation. PSPHunter provides a comprehensive landscape of phase-separating proteins and key residues, offering insights into the mechanisms of phase separation in transcriptional control, cell fate transitions, and disease development. The model's ability to predict key residues and their impact on phase separation highlights its potential as a tool for understanding the molecular mechanisms underlying diseases. PSPHunter's integration of sequence and functional features, along with its ability to predict the impact of mutations on phase separation, makes it a valuable resource for studying phase separation and its role in disease.A machine learning model called PSPHunter has been developed to precisely predict key residues involved in protein phase separation. Phase separation is a critical process in cellular functions such as transcriptional control, cell fate transitions, and disease mechanisms. PSPHunter uses a combination of sequence and functional features to identify these key residues, which are crucial for phase separation dynamics. The model was validated using in vivo and in vitro experiments, showing that truncating key residues in GATA3 disrupts phase separation, enhances tumor cell migration, and inhibits growth. Glycine and proline are enriched in key residues, and PSPHunter identifies nearly 80% of disease-associated phase-separating proteins. The model also reveals that mutations in key residues, particularly glycine and proline, significantly impact phase separation, linking them to disease mechanisms. PSPHunter was developed by integrating protein sequences and functional features from existing databases. It was trained on a dataset of 167 human phase-separating proteins and used to predict phase-separating proteins and key residues. The model demonstrated high accuracy in predicting phase-separating proteins and identified key residues that significantly influence phase separation. PSPHunter's predictions were validated through experiments showing that truncating key residues in GATA3 disrupts phase separation, while restoring phase separation in mutated GATA3 rescues tumor cell phenotypes. The model also identified that glycine and proline are enriched in key residues, and mutations in these residues have a significant impact on phase separation. PSPHunter provides a comprehensive landscape of phase-separating proteins and key residues, offering insights into the mechanisms of phase separation in transcriptional control, cell fate transitions, and disease development. The model's ability to predict key residues and their impact on phase separation highlights its potential as a tool for understanding the molecular mechanisms underlying diseases. PSPHunter's integration of sequence and functional features, along with its ability to predict the impact of mutations on phase separation, makes it a valuable resource for studying phase separation and its role in disease.