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Updated: Nov 1, 2025

Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy
Published on: September 17, 2017
CheSPI: chemical shift secondary structure population inference.
Jakob Toudahl Nielsen1, Frans A A Mulder2
1Interdisciplinary Nanoscience Center (iNANO) and Department of Chemistry, Aarhus University, Gustav Wieds Vej 14, 8000, Aarhus C, Denmark. jtn@inano.au.dk.
CheSPI accurately measures protein structure and disorder using NMR chemical shifts. This method detects residual structure and classifies proteins, aiding in structural analysis.
Area of Science:
- Biochemistry
- Structural Biology
- Biophysics
Background:
- NMR chemical shifts (CSs) provide insights into local protein structure.
- Recent advances allow inference of structure from deviations in random coil CS (RCCS).
Purpose of the Study:
- To present CheSPI, a method for unbiased and sensitive measurement of local protein structure and disorder.
- To demonstrate CheSPI's ability to predict residual structure and classify proteins.
Main Methods:
- Utilizes NMR chemical shifts (CSs) and random coil CS (RCCS) predictions.
- Employs an efficient method for aggregate measures of local structure and disorder.
Main Results:
- CheSPI successfully predicts residual structure in proteins.
- It robustly delineates four structural classes for intrinsically disordered proteins.
- For structured proteins, CheSPI predicts up to eight classes, aligning with DSSP classification.
Conclusions:
- CheSPI offers a sensitive tool for analyzing protein structure and disorder.
- The method provides comprehensive numeric and graphical output for visualization.
- CheSPI is freely available as a web tool and a standalone Python program.
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