Generative modeling of intrinsically disordered protein regions by reinforcing sparse autoencoder features
Intrinsically disordered protein regions (IDRs) play central roles in cellular processes such as transcriptional regulation, signal transduction, and subcellular localization, yet their functional design remains challenging. Structure-based design methods do not readily apply to IDRs, and existing protein language models are trained on full-length protein sequences, thus learni
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Intrinsically disordered protein regions (IDRs) play central roles in cellular processes such as transcriptional regulation, signal transduction, and subcellular localization, yet their functional design remains challenging. Structure-based design methods do not readily apply to IDRs, and existing protein language models are trained on full-length protein sequences, thus learni
This paper introduces a specialized generative model for intrinsically disordered protein regions (IDRs), addressing a gap in existing protein language models. For ML engineers, this highlights the importance of domain-specific model training and dataset curation for biological applications, moving beyond general-purpose models.