Rapid protein global fold determination using ultrasparse sampling, high-dynamic range artifact suppression, and...

Area of Science:

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

Background:

• Global fold determination is crucial for understanding large protein topology and reducing assignment ambiguity in NMR studies.
• Traditional NMR methods require extensive data collection and processing, limiting the study of large protein structures.

Purpose of the Study:

• To develop and validate a novel NMR approach combining ultrasparse sampling and a new data processing algorithm (SCRUB) for efficient global fold determination of large proteins.
• To demonstrate the capability of this method to collect high-resolution Nuclear Overhauser Effect (NOE) data and perform automated structure calculations rapidly.

Main Methods:

• Utilized a 4-D time-shared NOESY experiment with ultrasparse sampling on selectively labeled protein samples.
• Developed and applied the SCRUB algorithm, an iterative artifact removal method enhancing signal-to-noise and quantitative accuracy.
• Performed automated resonance assignment and global fold calculations using the CYANA software.

Main Results:

• Achieved high-resolution NOE data collection in four days, significantly reducing sampling requirements (1.2% of Nyquist).
• The SCRUB algorithm demonstrated superior artifact suppression (>250x better than CLEAN) and quantitative signal reproduction.
• Accurate and well-converged global fold ensembles were obtained for 23 kDa and 29 kDa proteins using automated assignment from the processed 4-D NOESY data.

Conclusions:

• The combined approach of ultrasparse sampling, SCRUB processing, and automated assignment provides an efficient and accurate method for large protein global fold determination.
• This technique significantly reduces data acquisition and processing time compared to existing sparse 4-D NMR methods.
• Enables rapid structural insights into large protein targets, overcoming previous limitations in NMR-based structural biology.