Numerous human proteins are either partially or fully classified as intrinsically disordered proteins (IDPs). Due to their properties, high-resolution structural information about IDPs is generally lacking. On the other hand, IDPs are known to adopt local ordered structures upon interactions with ligands, which could be e.g. other proteins or lipid membrane surfaces. While recent developments in protein structure prediction have been revolutionary, their impact on IDP research at high resolution remains limited. We took a specific example of two myelin-specific IDPs, the myelin basic protein (MBP) and the cytoplasmic domain of myelin protein zero (P0ct). Both of these IDPs are known to be crucial for normal nervous system development and function, and while they are disordered in solution, upon membrane binding, they partially fold into helices, being embedded into the lipid membrane. We carried out AlphaFold2 predictions of both proteins and analysed the models in light of previously published data related to solution structure and molecular interactions. We observe that the predicted models have helical segments that closely correspond to the characterised membrane-binding sites on both proteins. Hence, artificial intelligence-based models of IDPs appear to be able to provide detailed information on the ligand-bound state of these proteins, instead of the form dominating free in solution. We further discuss the implications of the predictions for normal mammalian nervous system myelination and their relevance to understanding disease aspects of these IDPs.