A deep learning method for predicting interactions for intrinsically disordered regions of proteins.

Intrinsically disordered proteins or regions (IDPs/IDRs) adopt diverse binding modes with different partners, ranging from ordered to multivalent to fuzzy conformations in the bound state. Characterizing IDR interfaces is challenging experimentally and computationally. Alphafold-multimer and Alphafold3, the state-of-the-art structure prediction methods, are less accurate at predicting IDR binding sites at their benchmarked confidence cutoffs. Their performance improves upon lowering the confidence cutoffs. Here, we developed Disobind, a deep-learning method that predicts inter-protein contact maps and interface residues for an IDR and a partner protein, given their sequences. It outperforms AlphaFold-multimer and AlphaFold3 at multiple confidence cutoffs. Combining the Disobind and AlphaFold-multimer predictions further improves the performance. In contrast to most current methods, Disobind considers the context of the binding partner and does not depend on structures and multiple sequence alignments. Its predictions can be used to localize IDRs in integrative structures of large assemblies and characterize and modulate IDR-mediated interactions.