Back

Accurate determination of the preferred aggregation number of a micelle-encapsulated membrane protein dimer

Harris, J.; Pantelopulos, G. A.; Straub, J. E.

2025-10-04 biophysics
10.1101/2025.10.02.679921 bioRxiv
Show abstract

The preferred aggregation number of dodeclyphoshocholine (DPC) micelles [Formula] encapsulating dimeric and higher order protein assemblies is difficult to determine via experimental techniques due to uncertainty in dimer geometry and heterogeneity in the conformational ensemble. Dimerization of the Amyloid Precursor Protein transmembrane domain (C99) is a particular step of importance in the production of amyloid-{beta} protein and the amyloid cascade. Molecular dynamics simulations of the C99 dimer and other transmembrane proteins have been performed to compliment micelle-phase protein structure studies. It has often been assumed that the value of [Formula] is the same as that of the pure, empty micelle. Here, we provide a convenient method for testing that assumption, while also accounting for the finite-size effects inherent in computer simulations of micelle self-assembly. Employing large, unbiased, coarsegrained molecular dynamics simulations of DPC and C99 dimer self-assembly, we determined the radius of gyration to be 21.6 {+/-} 2.0 [A] for the micelle-encapsulated dimer, and 16.0 {+/-} 1.0 [A] for the pure DPC micelle. Using these radii of gyration, we performed all-atom simulations of DPC-encapsulated C99 dimers with preferred aggregation numbers of 100 and 54 DPC to test the effect of using an expected versus a naive estimate of aggregation number on the structure of the transmembrane protein dimer. Through atomistic simulations, we determined that the transmembrane dimeric structure displays different characteristics depending on the aggregation number of the micelle, in addition to increased water penetration and micelle defects when the aggregation number is too small.

Published in Biophysical Journal (predicted rank #7) · training set

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

1
The Journal of Physical Chemistry B
167 papers in training set
Top 0.1%
38.4%
2
Journal of Chemical Theory and Computation
140 papers in training set
Top 0.2%
8.6%
3
The Journal of Chemical Physics
56 papers in training set
Top 0.1%
7.6%
50% of probability mass above
4
Journal of Chemical Information and Modeling
238 papers in training set
Top 1%
4.7%
5
Langmuir
36 papers in training set
Top 0.1%
4.7%
6
The Journal of Physical Chemistry Letters
63 papers in training set
Top 0.1%
4.3%
Biophysical Journal · published here
631 papers in training set
Top 2%
4.2%
8
Physical Chemistry Chemical Physics
36 papers in training set
Top 0.1%
3.1%
9
Biomacromolecules
29 papers in training set
Top 0.2%
2.3%
10
Biochemistry
148 papers in training set
Top 1%
2.1%
11
Soft Matter
60 papers in training set
Top 0.5%
1.6%
12
Journal of Colloid and Interface Science
12 papers in training set
Top 0.2%
1.1%
13
JACS Au
43 papers in training set
Top 0.7%
1.0%
14
Nanoscale
42 papers in training set
Top 0.6%
1.0%
15
Chemical Science
73 papers in training set
Top 2%
1.0%
16
Journal of Computational Chemistry
13 papers in training set
Top 0.3%
0.8%
17
ACS Omega
105 papers in training set
Top 4%
0.8%
18
Frontiers in Molecular Biosciences
102 papers in training set
Top 3%
0.6%
19
Scientific Reports
3612 papers in training set
Top 80%
0.6%
20
Computational and Structural Biotechnology Journal
242 papers in training set
Top 8%
0.6%