Linking Gas-Phase and Solution-Phase Protein Unfolding via Mobile Proton Simulations
Native mass spectrometry coupled to ion mobility (IM-MS) combined with collisional activation (CA) of ions in the gas phase (in vacuo) is an important method for the study of protein unfolding. It has advantages over classical biophysical and structural techniques as it can be used to analyze small...
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| Published in: | Analytical chemistry (Washington) Vol. 94; no. 46; pp. 16113 - 16121 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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American Chemical Society
22-11-2022
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| Abstract | Native mass spectrometry coupled to ion mobility (IM-MS) combined with collisional activation (CA) of ions in the gas phase (in vacuo) is an important method for the study of protein unfolding. It has advantages over classical biophysical and structural techniques as it can be used to analyze small volumes of low-concentration heterogeneous mixtures while maintaining solution-like behavior and does not require labeling with fluorescent or other probes. It is unclear, however, whether the unfolding observed during collision activation experiments mirrors solution-phase unfolding. To bridge the gap between in vacuo and in-solution behavior, we use unbiased molecular dynamics (MD) to create in silico models of in vacuo unfolding of a well-studied protein, the N-terminal domain of ribosomal L9 (NTL9) protein. We utilize a mobile proton algorithm (MPA) to create 100 thermally unfolded and coulombically unfolded in silico models for observed charge states of NTL9. The unfolding behavior in silico replicates the behavior in-solution and is in line with the in vacuo observations; however, the theoretical collision cross section (CCS) of the in silico models was lower compared to that of the in vacuo data, which may reflect reduced sampling. |
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| AbstractList | Native mass spectrometry coupled to ion mobility (IM-MS) combined with collisional activation (CA) of ions in the gas phase (in vacuo) is an important method for the study of protein unfolding. It has advantages over classical biophysical and structural techniques as it can be used to analyze small volumes of low-concentration heterogeneous mixtures while maintaining solution-like behavior and does not require labeling with fluorescent or other probes. It is unclear, however, whether the unfolding observed during collision activation experiments mirrors solution-phase unfolding. To bridge the gap between in vacuo and in-solution behavior, we use unbiased molecular dynamics (MD) to create in silico models of in vacuo unfolding of a well-studied protein, the N-terminal domain of ribosomal L9 (NTL9) protein. We utilize a mobile proton algorithm (MPA) to create 100 thermally unfolded and coulombically unfolded in silico models for observed charge states of NTL9. The unfolding behavior in silico replicates the behavior in-solution and is in line with the in vacuo observations; however, the theoretical collision cross section (CCS) of the in silico models was lower compared to that of the in vacuo data, which may reflect reduced sampling.Native mass spectrometry coupled to ion mobility (IM-MS) combined with collisional activation (CA) of ions in the gas phase (in vacuo) is an important method for the study of protein unfolding. It has advantages over classical biophysical and structural techniques as it can be used to analyze small volumes of low-concentration heterogeneous mixtures while maintaining solution-like behavior and does not require labeling with fluorescent or other probes. It is unclear, however, whether the unfolding observed during collision activation experiments mirrors solution-phase unfolding. To bridge the gap between in vacuo and in-solution behavior, we use unbiased molecular dynamics (MD) to create in silico models of in vacuo unfolding of a well-studied protein, the N-terminal domain of ribosomal L9 (NTL9) protein. We utilize a mobile proton algorithm (MPA) to create 100 thermally unfolded and coulombically unfolded in silico models for observed charge states of NTL9. The unfolding behavior in silico replicates the behavior in-solution and is in line with the in vacuo observations; however, the theoretical collision cross section (CCS) of the in silico models was lower compared to that of the in vacuo data, which may reflect reduced sampling. Native mass spectrometry coupled to ion mobility (IM-MS) combined with collisional activation (CA) of ions in the gas phase ( ) is an important method for the study of protein unfolding. It has advantages over classical biophysical and structural techniques as it can be used to analyze small volumes of low-concentration heterogeneous mixtures while maintaining solution-like behavior and does not require labeling with fluorescent or other probes. It is unclear, however, whether the unfolding observed during collision activation experiments mirrors solution-phase unfolding. To bridge the gap between and behavior, we use unbiased molecular dynamics (MD) to create models of unfolding of a well-studied protein, the N-terminal domain of ribosomal L9 (NTL9) protein. We utilize a mobile proton algorithm (MPA) to create 100 thermally unfolded and coulombically unfolded models for observed charge states of NTL9. The unfolding behavior replicates the behavior in-solution and is in line with the observations; however, the theoretical collision cross section (CCS) of the models was lower compared to that of the data, which may reflect reduced sampling. Native mass spectrometry coupled to ion mobility (IM-MS) combined with collisional activation (CA) of ions in the gas phase (in vacuo) is an important method for the study of protein unfolding. It has advantages over classical biophysical and structural techniques as it can be used to analyze small volumes of low-concentration heterogeneous mixtures while maintaining solution-like behavior and does not require labeling with fluorescent or other probes. It is unclear, however, whether the unfolding observed during collision activation experiments mirrors solution-phase unfolding. To bridge the gap between in vacuo and in-solution behavior, we use unbiased molecular dynamics (MD) to create in silico models of in vacuo unfolding of a well-studied protein, the N-terminal domain of ribosomal L9 (NTL9) protein. We utilize a mobile proton algorithm (MPA) to create 100 thermally unfolded and coulombically unfolded in silico models for observed charge states of NTL9. The unfolding behavior in silico replicates the behavior in-solution and is in line with the in vacuo observations; however, the theoretical collision cross section (CCS) of the in silico models was lower compared to that of the in vacuo data, which may reflect reduced sampling. Native mass spectrometry coupled to ion mobility (IM-MS) combined with collisional activation (CA) of ions in the gas phase ( in vacuo ) is an important method for the study of protein unfolding. It has advantages over classical biophysical and structural techniques as it can be used to analyze small volumes of low-concentration heterogeneous mixtures while maintaining solution-like behavior and does not require labeling with fluorescent or other probes. It is unclear, however, whether the unfolding observed during collision activation experiments mirrors solution-phase unfolding. To bridge the gap between in vacuo and in-solution behavior, we use unbiased molecular dynamics (MD) to create in silico models of in vacuo unfolding of a well-studied protein, the N-terminal domain of ribosomal L9 (NTL9) protein. We utilize a mobile proton algorithm (MPA) to create 100 thermally unfolded and coulombically unfolded in silico models for observed charge states of NTL9. The unfolding behavior in silico replicates the behavior in-solution and is in line with the in vacuo observations; however, the theoretical collision cross section (CCS) of the in silico models was lower compared to that of the in vacuo data, which may reflect reduced sampling. |
| Author | Cragnolini, Tristan Thalassinos, Konstantinos Eldrid, Charles Ben-Younis, Aisha Zou, Junjie Raleigh, Daniel P. |
| AuthorAffiliation | Department of Chemistry University of London School of Biological Sciences University of Southampton Institute of Structural and Molecular Biology, Birkbeck College Institute of Structural and Molecular Biology, Division of Bioscience |
| AuthorAffiliation_xml | – name: University of Southampton – name: Institute of Structural and Molecular Biology, Birkbeck College – name: University of London – name: Department of Chemistry – name: School of Biological Sciences – name: Institute of Structural and Molecular Biology, Division of Bioscience |
| Author_xml | – sequence: 1 givenname: Charles orcidid: 0000-0001-5306-3644 surname: Eldrid fullname: Eldrid, Charles organization: Institute of Structural and Molecular Biology, Division of Bioscience – sequence: 2 givenname: Tristan surname: Cragnolini fullname: Cragnolini, Tristan organization: University of London – sequence: 3 givenname: Aisha surname: Ben-Younis fullname: Ben-Younis, Aisha organization: Institute of Structural and Molecular Biology, Division of Bioscience – sequence: 4 givenname: Junjie surname: Zou fullname: Zou, Junjie organization: Department of Chemistry – sequence: 5 givenname: Daniel P. orcidid: 0000-0003-3248-7493 surname: Raleigh fullname: Raleigh, Daniel P. organization: Department of Chemistry – sequence: 6 givenname: Konstantinos orcidid: 0000-0001-5072-8428 surname: Thalassinos fullname: Thalassinos, Konstantinos email: k.thalassinos@ucl.ac.uk organization: University of London |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36350278$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Algorithms Chemistry Fluorescence Fluorescent indicators Ionic mobility Ions - chemistry Mass spectrometry Mass spectroscopy Molecular dynamics Molecular Dynamics Simulation Protein Conformation Protein folding Protein Unfolding Proteins Proteins - chemistry Protons Vapor phases |
| Title | Linking Gas-Phase and Solution-Phase Protein Unfolding via Mobile Proton Simulations |
| URI | http://dx.doi.org/10.1021/acs.analchem.2c03352 https://www.ncbi.nlm.nih.gov/pubmed/36350278 https://www.proquest.com/docview/2743521876 https://www.proquest.com/docview/2734613092 https://pubmed.ncbi.nlm.nih.gov/PMC9685592 |
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