Disentangling the Complexity in Protein Complexes Using Complementary Isotope-Labeling and Multiple-Receiver NMR Spectroscopy

Intrinsically disordered proteins are abundant in eukaryotic systems, but they remain largely elusive pharmacological targets. NMR spectroscopy proved to be a suitable method to study these proteins and their interaction with one another or with drug candidates. Although NMR can give atomistic information about these interplays, molecular complexity due to severe spectral overlap, limited sample stability, and quantity remain an issue and hamper widespread applications. Here, we propose an approach to simultaneously map protein-protein binding sites onto two interacting partners by employing a complementary isotope-labeling strategy and a multiple receiver NMR detection scheme. With one partner being <sup>15</sup>N,<sup>2</sup>H labeled and the interacting one being <sup>13</sup>C,<sup>1</sup>H-labeled, we exploited proton and carbon detection to obtain clean and easily readable information. The method is illustrated with an application to the 50 kDa ternary protein complex formed between the prominent oncogenic transcription factor complex Myc/MAX and the tumor suppressor BRCA1.

Authors

Institutions

Publication Details

Journal
Journal of the American Chemical Society
Published
2024-10-07
DOI
https://doi.org/10.1021/jacs.4c09176
Citations
5
Primary Topic
Metabolomics and Mass Spectrometry Studies
Type
article
Field-Weighted Citation Impact
1.17

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Disentangling the Complexity in Protein Complexes Using Complementary Isotope-Labeling and Multiple-Receiver NMR Spectroscopy

Roberta Pierattelli, Karin Ledolter, Isabella C. Felli, Marco Schiavina et al.
5 citations
Journal of the American Chemical Society
Metabolomics and Mass Spectrometry Studies
1.17
article

Disentangling the Complexity in Protein Complexes Using Complementary Isotope-Labeling and Multiple-Receiver NMR Spectroscopy

Roberta Pierattelli, Karin Ledolter, Isabella C. Felli, Marco Schiavina, Robert Konrat, Maria Anna Rodella, Sonja Knödlstorfer
article en
5 citations

Abstract

Intrinsically disordered proteins are abundant in eukaryotic systems, but they remain largely elusive pharmacological targets. NMR spectroscopy proved to be a suitable method to study these proteins and their interaction with one another or with drug candidates. Although NMR can give atomistic information about these interplays, molecular complexity due to severe spectral overlap, limited sample stability, and quantity remain an issue and hamper widespread applications. Here, we propose an approach to simultaneously map protein-protein binding sites onto two interacting partners by employing a complementary isotope-labeling strategy and a multiple receiver NMR detection scheme. With one partner being <sup>15</sup>N,<sup>2</sup>H labeled and the interacting one being <sup>13</sup>C,<sup>1</sup>H-labeled, we exploited proton and carbon detection to obtain clean and easily readable information. The method is illustrated with an application to the 50 kDa ternary protein complex formed between the prominent oncogenic transcription factor complex Myc/MAX and the tumor suppressor BRCA1.

Journal of the American Chemical SocietyVol. 146(41)
University of Vienna (AT), Max Perutz Labs (AT), Vienna Biocenter (AT), Interuniversity Consortium for Magnetic Resonance (IT), University of Florence (IT)
European Commission, Austrian Science Fund, Horizon 2020 Framework Programme, Directorate-General for Communication
Openalex Percentile: Top 22%
Metabolomics and Mass Spectrometry Studies
1.17
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.