Predicting glycosaminoglycan surface protein interactions and implications for studying axonal growth

PNAS, 2017 · DOI: 10.1073/pnas.1715093115 · Published: December 26, 2017

Simple Explanation

Cell-surface carbohydrates, particularly glycosaminoglycans (GAGs), interact with proteins and play roles in biological processes like neuronal development and immune response. The research aims to understand these interactions better. The authors developed a computational method called GAG-Dock to predict how GAGs bind to proteins. This method was validated by comparing its predictions to known protein-GAG structures. GAG-Dock can be used to predict the structure of GAG and protein complexes, understand how changes to GAGs affect their binding, and predict the effects of mutations in the protein that alter GAG binding.

Study Duration

Not specified

Participants

Not specified

Evidence Level

Computational modeling

Key Findings

1
The GAG-Dock method accurately reproduces the binding poses of heparin to proteins, as validated against known crystal structures.
2
GAG-Dock predicts the binding poses of heparin and chondroitin sulfate derivatives to axon guidance proteins RPTPσ and NgR1-3.
3
The method can identify mutations in proteins that either increase or decrease GAG binding affinity, offering a way to tune protein-GAG interactions.

Research Summary

Glycosaminoglycans (GAGs) are important in biological processes, but structural information on their protein complexes is limited. The study introduces GAG-Dock, a computational method to predict GAG-protein binding. GAG-Dock was validated against known heparin-protein structures and then used to predict binding poses for GAGs with proteins involved in axon guidance, RPTPσ and NgR1-3. The method can identify mutations to modulate GAG binding affinity and help understand the mechanisms by which GAGs influence axon growth and regeneration.

Practical Implications

Understanding Neuronal Development

The method provides insights into GAG-protein interactions that regulate neuronal development.

Developing Targeted Therapies

The ability to predict and tune GAG binding could lead to new therapies for diseases involving GAG-protein interactions, like cancer or neurodegenerative disorders.

Improving Axon Regeneration

Predicting structures of GAG-protein complexes can inform the design of experimental probes to elucidate mechanisms by which GAGs modulate axon growth and regeneration.

Study Limitations

1
The method may not fully account for the role of water molecules and ions in GAG-protein binding.
2
The accuracy of predictions depends on the quality of the available protein structures.
3
Polarization effects of water and ions are not accounted for in the model.

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