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Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy

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ASAP: An automatic sequential assignment program for congested multidimensional solid state NMR spectra.

¹Department of Physics, University of Central Florida, Orlando 32816, USA.

Journal of Magnetic Resonance (San Diego, Calif. : 1997)

|March 24, 2024

Summary

This summary is machine-generated.

We developed an automatic sequential assignment program (ASAP) to simplify complex solid-state NMR (ssNMR) spectra. This tool aids in assigning signals for large proteins lacking crystalline order, accelerating research.

Keywords:

Multidimensional spectroscopy NMR Sequential assignment

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Area of Science:

Biophysics
Structural Biology
Nuclear Magnetic Resonance Spectroscopy

Background:

Solid-state NMR (ssNMR) spectroscopy is crucial for determining the structure of proteins that cannot be crystallized.
Assigning signals in congested ssNMR spectra is a significant bottleneck in structural analysis.
Incomplete side-chain resonance detection and spectral overlap complicate accurate ssNMR signal assignments.

Purpose of the Study:

To develop an automated program for sequential signal assignment in ssNMR spectra.
To address the challenges posed by spectral congestion and incomplete resonance data in ssNMR.
To accelerate the structural analysis of large, non-crystalline proteins using ssNMR.

Main Methods:

Developed an automatic sequential assignment program (ASAP).
ASAP integrates the auto-residue type assignment strategy (ARTIST) with Monte Carlo simulated annealing (MCSA).
Input includes residue type assignments (RTAs) from NCACX spectra, NCOCX peak lists, and protein sequences.

Main Results:

ASAP demonstrates robust performance in signal assignment for ssNMR spectra.
The program effectively overcomes challenges from spectral congestion and incomplete side-chain resonances.
ASAP significantly accelerates the assignment of signals for large proteins (>200 residues).

Conclusions:

ASAP provides a powerful solution for automating signal assignments in challenging ssNMR spectra.
This method enhances the efficiency of structural determination for large proteins that are difficult to crystallize.
The developed program facilitates broader application of ssNMR in structural biology.