A compact generative language model learns the compositional grammar of phase-separating proteins

Liquid-liquid phase separation (LLPS) of intrinsically disordered proteins drives the formation of biomolecular condensates, yet the sequence-level grammar that promotes condensation remains difficult to specify from first principles. Existing protein language models are typically trained on large, folded-protein-rich sequence corpora and may not optimally capture the compositional biases of LLPS-prone disordered proteins. Here, we show that a compact generative language model, with an architecture and size (∼17 M parameters) selected via systematic hyperparameter optimization and cross-validation, trained from scratch on a small set of phase-separating protein sequences, can learn sequence features associated with condensate formation. The model generates thousands of sequences that reproduce length-dependent trends in disorder, charge decoration, hydropathy patterning, and sequence entropy, with training and generative behavior stable across independent seeds. Homology analysis indicates that generated sequences are not full-length copies of known proteins, but instead combine short segments matching natural proteins with a novel, model-generated sequence. Descriptor-based screening of short generated sequences yielded a pool of disordered candidates, from which five synthetically feasible peptides were selected for experimental testing. Four of them show condensate-like signatures under near-physiological conditions, including droplet-like assemblies and increased turbidity. Coarse-grained phase-coexistence simulations of a subset of generated sequences with longer lengths show a broad range of phase-separation tendencies, with 12 of 89 sequences comparable to the strongly phase-separating hnRNPA1 low-complexity domain. These results suggest that compact, task-specific generative models can learn information-rich sequence grammars from small curated datasets and propose experimentally testable condensate-forming peptides.