The intrinsically disordered protein glue of myelin: Linking AlphaFold2 predictions to experimental data

Author:

Krokengen Oda C.,Raasakka ArneORCID,Kursula PetriORCID

Abstract

AbstractNumerous 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. We furthermore analyse the fits of the models to SAXS data from the same IDPs. 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.

Publisher

Cold Spring Harbor Laboratory

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Perspective on the Prospective Use of AI in Protein Structure Prediction;Journal of Chemical Information and Modeling;2023-12-21

2. Best Practices of Using AI-Based Models in Crystallography and Their Impact in Structural Biology;Journal of Chemical Information and Modeling;2023-06-12

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