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Published on: October 21, 2016
Predicting stable binding modes from simulated dimers of the D76N mutant of 2-microglobulin
Nuno F B Oliveira1,2, Filipe E P Rodrigues1,2, João N M Vitorino1,2
1BioISI - Biosystems & Integrative Sciences Institute, Faculty of Sciences, University of Lisboa, Campo Grande, C8 bdg, Lisboa 1749-016, Portugal.
The D76N protein mutant, a model for protein aggregation, forms dimers stabilized by hydrophobic interactions. Most stable dimer interfaces suggest self-limited growth, preventing long chain formation.
Area of Science:
- Biochemistry
- Molecular Biology
- Computational Biology
Background:
- The D76N mutant of the protein serves as a model for studying protein aggregation.
- Experimental and simulation data indicate D76N populates a dynamic conformation exposing aggregation-prone regions due to terminal region detachment.
Purpose of the Study:
- Investigate the stability of protein dimers formed by the D76N mutant.
- Analyze the structural basis of dimer interface stabilization.
- Determine the oligomerization potential of stable D76N dimer interfaces.
Main Methods:
- Molecular Dynamics (MD) simulations to study dimer stability.
- Protein-protein docking to generate dimer ensembles.
- Molecular Mechanics with the Poisson-Boltzmann and Solvent Accessible Surface Area (MM-PBSA) calculations for binding energy assessment.
- Geometric rule-based propagation of dimer interfaces.
Main Results:
- Hydrophobic interactions are the primary drivers of dimer interface stabilization at physiological pH.
- The most stable dimer interfaces involve N- and C-termini, and BC/DE-loops.
- Less stable interfaces are stabilized by CD/EF-loop interactions.
- Propagation of the most stable interfaces suggests a self-limited growth process, not indefinite polymerization.
Conclusions:
- D76N dimer stability is governed by specific residue interactions, particularly hydrophobic forces.
- The structural characteristics of the most stable D76N dimer interfaces inherently limit further polymerization.
- This finding has implications for understanding and potentially controlling protein aggregation pathways.
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