Interpreting NMR data for beta-peptides using molecular dynamics simulations

Area of Science:

• Structural Biology
• Computational Chemistry
• Biophysics

Background:

• Nuclear Magnetic Resonance (NMR) spectroscopy is crucial for determining protein and peptide structures in solution.
• Conformational heterogeneity in polypeptides can lead to inconsistencies when using traditional NMR data (NOE, (3)J-values) for structure determination.
• Restraint-based methods may fail to accurately represent systems with multiple conformations.

Purpose of the Study:

• To demonstrate that unrestrained molecular dynamics (MD) simulations can generate conformational ensembles consistent with experimental NMR data.
• To resolve inconsistencies in NMR data by interpreting it within the context of simulated conformational ensembles.
• To investigate the conformational differences between protected and unprotected forms of a beta-peptide.

Main Methods:

• Performed four 100 ns unrestrained MD simulations of a nine-residue beta-peptide in methanol at two temperatures.
• Generated conformational ensembles from MD simulations.
• Calculated inter-hydrogen distances to predict Nuclear Overhauser Effects (NOEs) and compared them with experimental data.

Main Results:

• MD simulations produced conformational ensembles largely compatible with experimental NMR data, resolving inconsistencies.
• The beta-peptide predominantly adopts a 12/10-helical structure, but simulations revealed the presence of 3(14)()-helical structures in the unprotected form.
• The presence of 3(14)()-helical structures correlated with the fulfillment of a specific experimental NOE.
• MD ensembles highlighted small, specific conformational differences between the protected and unprotected beta-peptide forms.

Conclusions:

• Unrestrained MD simulations are a powerful tool for generating accurate conformational ensembles of peptides and proteins in solution.
• MD simulations facilitate a detailed and consistent interpretation of experimental NMR data, especially for conformationally heterogeneous systems.
• This approach successfully identified and explained discrepancies in NMR data by accounting for multiple conformations.