Protein fold recognition through application of residual dipolar coupling data

Area of Science:

• Biophysics
• Structural Biology
• Nuclear Magnetic Resonance (NMR) Spectroscopy

Background:

• Residual dipolar couplings (RDCs) are a powerful NMR technique for protein structure determination.
• Current methods often require a close structural starting model for effective RDC-based structure calculation.
• Identifying suitable starting models, especially for proteins with low sequence similarity, remains a challenge.

Purpose of the Study:

• To develop a computational program, RDC-PROSPECT, for identifying structural homologs or analogs of a target protein using RDC data.
• To enable the use of identified structural analogs as starting models for RDC-based protein structure calculations.
• To create a tool applicable to proteins beyond the scope of current protein threading techniques.

Main Methods:

• RDC-PROSPECT utilizes 15N-1H RDC data and predicted secondary structure information for structural homolog identification.
• The program searches the Protein Data Bank (PDB) for the best structural match to the target protein's RDC data.
• Algorithmic design focused on efficient principal alignment frame (PAF) search for speed.

Main Results:

• RDC-PROSPECT successfully identified structural folds for approximately 80% of tested proteins (33 proteins from BMRB and literature).
• Achieved an average alignment accuracy of 97.9% residues within a 4-residue shift.
• Demonstrated performance independent of sequence similarity, outperforming current protein threading methods.

Conclusions:

• RDC-PROSPECT is an effective tool for identifying structural analogs using RDC data, even with low sequence similarity.
• The program significantly improves upon previous methods in accuracy and speed for RDC-based structure calculation.
• Its efficiency makes it suitable for large-scale applications in structural biology.