Automated Determination of Nuclear Magnetic Resonance Chemical Shift Perturbations in Ligand Screening Experiments: The PICASSO Web Server

Vincenzo Laveglia, Andrea Giachetti, Linda Cerofolini, Kevin Haubrich, Marco Fragai, Alessio Ciulli, Antonio Rosato (Lead / Corresponding author)

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
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Abstract

Nuclear magnetic resonance (NMR) is an effective, commonly used experimental approach to screen small organic molecules against a protein target. A very popular method consists of monitoring the changes of the NMR chemical shifts of the protein nuclei upon addition of the small molecule to the free protein. Multidimensional NMR experiments allow the interacting residues to be mapped along the protein sequence. A significant amount of human effort goes into manually tracking the chemical shift variations, especially when many signals exhibit chemical shift changes and when many ligands are tested. Some computational approaches to automate the procedure are available, but none of them as a web server. Furthermore, some methods require the adoption of a fairly specific experimental setup, such as recording a series of spectra at increasing small molecule:protein ratios. In this work, we developed a tool requesting a minimal amount of experimental data from the user, implemented it as an open-source program, and made it available as a web application. Our tool compares two spectra, one of the free protein and one of the small molecule:protein mixture, based on the corresponding peak lists. The performance of the tool in terms of correct identification of the protein-binding regions has been evaluated on different protein targets, using experimental data from interaction studies already available in the literature. For a total of 16 systems, our tool achieved between 79% and 100% correct assignments, properly identifying the protein regions involved in the interaction.
Original languageEnglish
Pages (from-to)5726-5733
Number of pages8
JournalJournal of Chemical Information and Modeling
Volume61
Issue number12
Early online date29 Nov 2021
DOIs
Publication statusPublished - 27 Dec 2021

Keywords

  • Algorithms
  • Adducts
  • Peptides and proteins
  • Ligands
  • Nuclear magnetic resonance spectroscopy

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