CHESPA/CHESCA-SPARKY: Automated NMR data analysis plugins for SPARKY to map protein allostery

Hongzhao Shao, Stephen Boulton, Cristina Olivieri, Hebatallah Mohamed, Madoka Akimoto, Manu Veliparambil Subrahmanian, Gianluigi Veglia, John L. Markley, Giuseppe Melacini, Woonghee Lee

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


MOTIVATION: Correlated Nuclear Magnetic Resonance (NMR) chemical shift changes identified through the CHEmical Shift Projection Analysis (CHESPA) and CHEmical Shift Covariance Analysis (CHESCA) reveal pathways of allosteric transitions in biological macromolecules. To address the need for an automated platform that implements CHESPA and CHESCA and integrates them with other NMR analysis software packages, we introduce here integrated plugins for NMRFAM-SPARKY that implement the seamless detection and visualization of allosteric networks.

AVAILABILITY AND IMPLEMENTATION: CHESCA-SPARKY and CHESPA-SPARKY are available in the latest version of NMRFAM-SPARKY from the National Magnetic Resonance Facility at Madison (, the NMRbox Project ( and to subscribers to the SBGrid ( The assigned spectra involved in this study and tutorial videos using this dataset are available at

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Online.

Original languageEnglish (US)
Pages (from-to)1176-1177
Number of pages2
Issue number8
StatePublished - Apr 15 2021

Bibliographical note

Publisher Copyright:
© 2021 Oxford University Press. All rights reserved.


  • Data Analysis
  • Magnetic Resonance Spectroscopy
  • Nuclear Magnetic Resonance, Biomolecular
  • Proteins
  • Software

PubMed: MeSH publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Journal Article
  • Research Support, N.I.H., Extramural


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