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Generative design of intrinsically disordered protein regions with IDiom
Authors
Liu, J.; Ibarraran, S.; Hu, F.; Park, A.; Dunn, A.; Rotskoff, G.
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Liu, J.; Ibarraran, S.; Hu, F.; Park, A.; Dunn, A.; Rotskoff, G.
Intrinsically disordered protein regions 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 organization, yet they have remained largely inaccessible to rational design. Structure-based generative methods are not applicable to proteins that lack a stable fold, and existing sequence-based approaches for disordered regions rely on sampling methods that do not capture the evolutionary statistics of natural disordered regions. Here, we introduce IDiom, an autoregressive protein language model trained on 37 million intrinsically disordered region sequences curated from the AlphaFold Database. Trained using a fill-in-the-middle data augmentation, IDiom generates disordered region sequences conditioned on their surrounding structured context, as well as fully disordered proteins without any context. The model generates diverse sequences that recapitulate biologically relevant sequence features of natural disordered regions, and we demonstrate that post-training via reinforcement learning with a subcellular localization reward model produces sequences with features which are consistent with known sequence determinants of compartment-specific localization. These results establish IDiom as a general platform for the generative design of intrinsically disordered proteins and regions.
While everyone obsesses over rigid protein structures, this team flips the script with language-model RL to design messy intrinsically disordered proteins, complete with a playful web demo that feels like AI mad-libs for molecular chaos.
Posted by Grant Rotskoff (@grantrotskoff) highlighting the shift from "structure-first" diffusion, it gained solid engagement and bookmarks from the protein design/ML crowd
View discussion on XPeer review in progress...
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