Top-Down Scoring of Spectral Fitness by Image Analysis for Protein Structure Validation

Area of Science:

• Biophysics
• Structural Biology
• Spectroscopy

Background:

• Nuclear magnetic resonance (NMR) spectroscopy is crucial for protein structure determination.
• Traditional NMR methods necessitate extensive manual resonance assignments, posing a significant bottleneck.

Purpose of the Study:

• To introduce NMRFAM-BPHON, a novel "top-down" computational approach for protein structure determination.
• To develop a method that reduces or eliminates the requirement for complete resonance assignments in NMR analysis.

Main Methods:

• NMR spectra are treated as continuous grayscale images and compared with simulated spectra from candidate protein structures.
• A physics-based polarization transfer model predicts cross-peak intensities, and empirical T2 relaxation rates and scalar couplings model line shapes.
• Simulated spectra are quantitatively scored against experimental data using normalized cross correlation, yielding fitness scores.

Main Results:

• NMRFAM-BPHON effectively discriminates between structural models, particularly for 13C-detected magic angle spinning solid-state NMR spectra.
• The method's performance can be improved by incorporating available experimental chemical shifts.
• The software includes a user-friendly graphical user interface for ChimeraX.

Conclusions:

• NMRFAM-BPHON offers a powerful and accessible alternative to traditional NMR-based protein structure determination methods.
• This approach significantly reduces the manual effort required for analyzing NMR data.
• The software democratizes advanced NMR analysis for a wider range of researchers.