Back

Buffer-dependent conformational dynamics of α-synuclein revealed by nanopipette electrospray ionisation ion mobility mass spectrometry

Byrd, E. J.; Norgate, E. L.; Crossley, J. A.; Chau, C. C.; Schiffrin, B.; Kulak, A.; Radford, S.; Actis, P.; Calabrese, A. N.; Sobott, F.

2025-08-23 biophysics
10.1101/2025.08.19.671163 bioRxiv
Show abstract

Electrolyte conditions in vivo and in vitro are known to influence protein structure and function. Intrinsically disordered proteins (IDPs) are particularly sensitive to their solution conditions such as ionic strength and molecular crowding, and their dynamic structural ensembles rapidly respond to the cellular environment. While structural mass spectrometry (MS) techniques are uniquely able to capture aspects of this structural diversity, technical limitations have largely precluded the use of native MS approaches to interrogate the conformational rearrangements of IDPs in response to high concentrations of non-volatile salts. Here, we overcome this challenge by employing sub 100-nm nanopipette electrospray emitters for more gentle and salt-tolerant analysis to study the conformational distribution of -Synuclein (S) using native MS and ion mobility-MS in varied solution conditions, including in phosphate buffered saline. We show using native MS that it is possible to capture salt and buffer induced changes in the S conformational ensemble when using traditional biochemical buffers, which reflect structural changes from in silico predictions and in-solution measurements. This work demonstrates the power of nanopipette emitters for the study of IDPs, and establishes native MS as a method that can be routinely used to determine how solution conditions tune the conformational landscape of IDPs. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/671163v2_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@35ff69org.highwire.dtl.DTLVardef@113821aorg.highwire.dtl.DTLVardef@1c27b90org.highwire.dtl.DTLVardef@13e4db1_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Chemical Science
73 papers in training set
Top 0.1%
18.5%
2
Angewandte Chemie International Edition
93 papers in training set
Top 0.1%
12.7%
3
Journal of the American Chemical Society
217 papers in training set
Top 0.3%
9.8%
4
Analytical Chemistry
218 papers in training set
Top 0.7%
5.2%
5
Communications Chemistry
48 papers in training set
Top 0.1%
4.4%
50% of probability mass above
6
Analytical and Bioanalytical Chemistry
18 papers in training set
Top 0.1%
3.2%
7
Nature Communications
5641 papers in training set
Top 35%
3.2%
8
JACS Au
43 papers in training set
Top 0.2%
3.2%
9
Protein Science
246 papers in training set
Top 2%
2.4%
10
Journal of Proteome Research
234 papers in training set
Top 1%
1.7%
11
Angewandte Chemie
15 papers in training set
Top 0.1%
1.7%
12
Biophysical Journal
631 papers in training set
Top 3%
1.7%
13
ACS Central Science
71 papers in training set
Top 0.9%
1.3%
14
Analytical Biochemistry
26 papers in training set
Top 0.3%
1.3%
15
PLOS Computational Biology
1863 papers in training set
Top 16%
1.3%
16
The Journal of Physical Chemistry Letters
63 papers in training set
Top 0.6%
1.1%
17
Chemical Communications
25 papers in training set
Top 0.4%
1.1%
18
PLOS ONE
5266 papers in training set
Top 55%
1.1%
19
Proceedings of the National Academy of Sciences
2444 papers in training set
Top 35%
1.1%
20
PROTEOMICS
43 papers in training set
Top 0.6%
1.1%
21
eLife
5828 papers in training set
Top 60%
1.0%
22
ACS Chemical Biology
167 papers in training set
Top 2%
1.0%
23
Journal of Molecular Biology
232 papers in training set
Top 3%
1.0%
24
ChemBioChem
55 papers in training set
Top 1.0%
1.0%
25
ACS Chemical Neuroscience
67 papers in training set
Top 1%
1.0%
26
Biomolecules
100 papers in training set
Top 3%
0.8%
27
Journal of the American Society for Mass Spectrometry
37 papers in training set
Top 0.5%
0.8%
28
Methods
34 papers in training set
Top 0.9%
0.6%
29
Physical Chemistry Chemical Physics
36 papers in training set
Top 0.7%
0.6%
30
Computational and Structural Biotechnology Journal
242 papers in training set
Top 8%
0.6%