Generative modeling of intrinsically disordered protein regions by reinforcing sparse autoencoder features

Intrinsically disordered protein regions (IDRs) are ubiquitous across all kingdoms of life. These structurally heterogeneous regions play central roles in cellular processes such as transcriptional regulation, cellular signaling, and subcellular localization, yet their functional design remains challenging for existing generative methods. Structure-based design methods do not readily apply to IDRs, and existing protein language models are trained on full-length sequences, learning priors which are biased towards folded domains. Here, we present IDiom, an autoregressive protein language model trained on IDiom-DB, a dataset of 54 million predicted IDRs curated from the AlphaFold Database, enabling generation of diverse and biologically realistic IDRs. For explicit control over sequence features, we also introduce reinforcement learning with sparse autoencoder (SAE) features (RL-SAE), which rewards the model for generating sequences that activate a specified set of SAE features. Together, IDiom and RL-SAE enable the interpretable and composable design of IDRs that carry the sequence motifs and predicted functionality of natural IDRs. More broadly, RL-SAE could extend to other protein design settings where interpretable features provide useful design targets.