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Significance The tendency of many cellular proteins to form protein-rich biomolecular condensates underlies the formation of subcellular compartments and has been linked to various physiological functions. Understanding the molecular basis of this fundamental process and predicting protein phase behavior have therefore become important objectives. To develop a global understanding of how protein sequence determines its phase behavior, we constructed bespoke datasets of proteins of varying phase separation propensity and identified explicit biophysical and sequence-specific features common to phase-separating proteins. Moreover, by combining this insight with neural network-based sequence embeddings, we trained machine-learning classifiers that identified phase-separating sequences with high accuracy, including from independent external test data.

Learning the molecular grammar of protein condensates from sequence determinants and embeddings