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Conformational Characterization and Classification of Intrinsically Disordered Proteins by Native Mass Spectrometry and Charge‐State Distribution Analysis
Author(s) -
Santambrogio Carlo,
Natalello Antonino,
Brocca Stefania,
Ponzini Erika,
Grandori Rita
Publication year - 2019
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201800060
Subject(s) - sasa , intrinsically disordered proteins , chemistry , conformational ensembles , native state , characterization (materials science) , mass spectrometry , computational chemistry , crystallography , nanotechnology , molecular dynamics , materials science , biology , chromatography , biochemistry , paleontology
Intrinsically disordered proteins (IDPs) are systematically under‐represented in structural proteomics studies. Their structural characterization implies description of the dynamic conformational ensembles populated by these polymers in solution, posing major challenges to biophysical methods. “Native” MS (native‐MS) has emerged as a central tool in this field, conjugating the unique MS analytical power with structurally meaningful descriptors, like solvent‐accessible surface area (SASA) and collisional cross section (CCS). This review summarizes recently published papers comparing native‐MS and solution methods, with a focus on charge‐state‐distribution (CSD) analysis for IDP conformational analysis. The results point to substantial agreement, supporting structural interpretation of native‐MS spectra of IDPs. The discussion is integrated with data from our group on “synthetic” IDPs, obtained by reduction and alkylation of natively folded proteins, whose fold is stabilized by disulfide bridges. Finally, an MS‐based compaction index ( CI ) is introduced, evaluating SASA with reference to globular and fully disorder proteins. Such a parameter can be calculated for single conformers or the whole conformational ensemble, offering a continuous index for IDP comparison and classification.