Back

High-Throughput Characterization of Trends in Transmembrane Helix Partitioning into Membrane Domains

Thelen, J.; Koenig, M.; Vuorte, M.; Liimatainen, J.; Javanainen, M.; Lolicato, F.

2026-05-18 biophysics
10.64898/2026.05.14.725159 bioRxiv
Show abstract

The plasma membrane is a laterally heterogeneous environment in which lipid organization plays a central role in regulating protein function. In model systems, this heterogeneity is often described in terms of coexisting liquid-ordered (Lo) and liquid-disordered (Ld) phases, commonly associated with the lipid raft concept. Despite extensive experimental and computational efforts, the molecular determinants governing protein partitioning between these domains remain poorly understood, largely due to the limited number of systems studied. Here, we address this challenge using a high-throughput computational approach, systematically analyzing the partitioning behavior of almost 5,000 helical transmembrane peptides in phase-separating lipid membranes. Across all simulations, we find that none of the peptides exhibit a clear preference for the Lo phase, while the vast majority partition into the Ld phase. This observation is consistent with experimental results in simplified membrane systems and suggests that commonly used ternary lipid mixtures may not fully capture the physicochemical environment governing protein sorting in biological membranes. In addition, we identify a subset of peptides that preferentially localize at the Lo/Ld interface. These interfacial peptides display distinct sequence characteristics, indicating that boundary localization is governed by specific combinations of residue composition and spatial arrangement rather than a single dominant feature. Overall, our results reveal that transmembrane helix partitioning in model membranes is dominated by a preference for disordered environments, with interfacial localization emerging as a distinct and potentially functional behavior.

Matching journals

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

1
Biophysical Journal
545 papers in training set
Top 0.1%
22.3%
2
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 3%
14.2%
3
Biochimica et Biophysica Acta (BBA) - Biomembranes
30 papers in training set
Top 0.1%
6.3%
4
The Journal of Physical Chemistry B
158 papers in training set
Top 0.3%
6.3%
5
Scientific Reports
3102 papers in training set
Top 35%
3.6%
50% of probability mass above
6
Protein Science
221 papers in training set
Top 0.4%
3.6%
7
Nature Communications
4913 papers in training set
Top 44%
2.9%
8
PLOS Computational Biology
1633 papers in training set
Top 11%
2.9%
9
eLife
5422 papers in training set
Top 33%
2.4%
10
Journal of the American Chemical Society
199 papers in training set
Top 3%
2.1%
11
Journal of Chemical Information and Modeling
207 papers in training set
Top 2%
1.9%
12
Langmuir
31 papers in training set
Top 0.2%
1.8%
13
The Journal of Physical Chemistry Letters
58 papers in training set
Top 0.8%
1.7%
14
Journal of Chemical Theory and Computation
126 papers in training set
Top 0.5%
1.6%
15
Frontiers in Molecular Biosciences
100 papers in training set
Top 2%
1.6%
16
Physical Biology
43 papers in training set
Top 1%
1.3%
17
Structure
175 papers in training set
Top 2%
1.3%
18
PLOS ONE
4510 papers in training set
Top 60%
1.2%
19
Journal of Molecular Biology
217 papers in training set
Top 3%
0.9%
20
Journal of The Royal Society Interface
189 papers in training set
Top 4%
0.9%
21
Chemical Science
71 papers in training set
Top 2%
0.9%
22
Frontiers in Cell and Developmental Biology
218 papers in training set
Top 7%
0.9%
23
Communications Biology
886 papers in training set
Top 17%
0.9%
24
Biochemistry
130 papers in training set
Top 1%
0.9%
25
Journal of Biological Chemistry
641 papers in training set
Top 3%
0.9%
26
Nano Letters
63 papers in training set
Top 3%
0.7%
27
Advanced Science
249 papers in training set
Top 22%
0.6%