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

Krokengen, Oda C.; Raasakka, Arne; Kursula, Petri

doi:10.1101/2022.09.13.507838

Cited by 3 publications

(7 citation statements)

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“…From bilayer structures to evidence of hybrid lipid crystals X-ray diffraction has been used to investigate the membrane stacking function of P0ct, MBP, and P2 [7,20,39,49]. Bicelles have shown enhanced stacking order and reveal latent protein-induced phase transitions beyond the usual gel (L β )-to-liquid crystalline (L α ) phase [73].…”

Section: Membrane Surface Bindingmentioning

confidence: 99%

“…Accordingly, a common denominator for P0ct, P2, and MBP is that they all reside within the MDL of PNS compact myelin. Both P0ct [7,10,11,[36][37][38][39][40][41][42], MBP [20,23,37,38,[43][44][45][46][47][48], and P2 [26,27,32,33,35,[49][50][51] have been studied separately with respect to their structure and membrane interactions, while their effects on lipid membranes together have received little attention. A few studies have pursued myelin protein-protein associations: P0 with myelin protein 22 (PMP22) [52,53], MBP and P2 [54], PMP22 with calnexin [55], L-periaxin and dystrophinrelated protein [56] as well as β4 integrin [57], and 2′,3′-cyclic nucleotide 3′phosphodiesterase (CNPase) with actin [58,59].…”

Section: Introductionmentioning

confidence: 99%

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On the synergy between myelin proteins P0, MBP, and P2 in peripheral nerve major dense line formation

Krokengen,

Raasakka,

Klenow

et al. 2024

Preprint

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Myelin is a proteolipid membrane multilayer held together by a set of proteins. The proper formation and function of the myelin sheath relies on the coordinated action of several key myelin proteins. Research exploring how proteins from the peripheral myelin cytoplasmic apposition – myelin basic protein (MBP), the cytoplasmic tail of myelin protein zero (P0ct), and peripheral myelin protein 2 (P2) – interact with each other and with myelin-like membranes was conducted using various techniques, such as small-angle X-ray diffraction (SAXD), differential scanning calorimetry (DSC), surface plasmon resonance (SPR), as well as electron and live epifluorescence microscopy. DSC revealed changes in lipid interactions depending on the protein combination, with MBP and P0ct binding more tightly to lipid membranes than P2, resulting in altered membrane fluidity and stability. These results were supported by SPR, which indicated that the myelin proteins may compete for membrane surface binding. Analysis of the Bragg peaks induced by the myelin proteins in lipidic environments showed both lamellar and non-lamellar phases in protein-lipid complexes. The results indicate both synergy and competition between the three main proteins residing in the PNS myelin major dense line. Furthermore, the observed direct effects of myelin proteins on lipid membrane properties may be relevant to their function in myelinating cells.

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Section: Membrane Surface Bindingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the synergy between myelin proteins P0, MBP, and P2 in peripheral nerve major dense line formation

Krokengen,

Raasakka,

Klenow

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…On the other hand, high confidence pLDDTs have been shown to sometimes correspond to residues belonging to disordered protein fragments in the monomeric unit that will fold conditionally, for example, when binding another protein partner. , Again, AF2 models should be taken with caution, as several studies show how they are likely to predict a protein bound structure instead of its unbound structure in solution. , …”

Section: The Impact Of Ai-generated Models Beyond Molecular Replacementmentioning

confidence: 99%

“…114,115 Again, AF2 models should be taken with caution, as several studies show how they are likely to predict a protein bound structure instead of its unbound structure in solution. 116,117…”

Section: Low Confidence Predictions: Should We Always Put the Blame O...mentioning

confidence: 99%

Best Practices of Using AI-Based Models in Crystallography and Their Impact in Structural Biology

Graille

Sacquin-Mora

Taly

2023

J. Chem. Inf. Model.

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The recent breakthrough made in the field of three-dimensional (3D) structure prediction by artificial intelligence softwares, such as initially AlphaFold2 (AF2) and RosettaFold (RF) and more recently large Language Models (LLM), has revolutionized the field of structural biology in particular and also biology as a whole. These models have clearly generated great enthusiasm within the scientific community, and different applications of these 3D predictions are regularly described in scientific articles, demonstrating the impact of these high-quality models. Despite the acknowledged high accuracy of these models in general, it seems important to make users of these models aware of the wealth of information they offer and to encourage them to make the best use of them. Here, we focus on the impact of these models in a specific application by structural biologists using X-ray crystallography. We propose guidelines to prepare models to be used for molecular replacement trials to solve the phase problem. We also encourage colleagues to share as much detail as possible about how they use these models in their research, where the models did not yield correct molecular replacement solutions, and how these predictions fit with their experimental 3D structure. We feel this is important to improve the pipelines using these models and also to get feedback on their overall quality.

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“…One should note that disorder in proteins comes in different flavors, with conditional order that can arise from different experimental conditions . High-confidence pLDDTs have been shown to sometimes correspond to residues that belong to disordered protein fragments in a monomeric unit that will fold conditionally, for example, when binding to another protein partner. , As a consequence, AF2 models should be taken with caution, as several studies show how they are likely to predict a protein-bound structure instead of its unbound structure in solution. − …”

Section: Intrinsically Disordered Proteins/domainsmentioning

confidence: 99%

A Perspective on the Prospective Use of AI in Protein Structure Prediction

Versini,

Sritharan,

Aykac Fas

et al. 2023

J. Chem. Inf. Model.

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AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.

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

Cited by 3 publications

References 78 publications

On the synergy between myelin proteins P0, MBP, and P2 in peripheral nerve major dense line formation

On the synergy between myelin proteins P0, MBP, and P2 in peripheral nerve major dense line formation

Best Practices of Using AI-Based Models in Crystallography and Their Impact in Structural Biology

A Perspective on the Prospective Use of AI in Protein Structure Prediction

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