Back

Force-regulated catch bonds and fusion peptide exposure drive coronavirus entry

Li, H.; Li, Z.; Gao, H.

2026-05-22 biophysics
10.64898/2026.05.21.727024 bioRxiv
Show abstract

Coronaviruses invade human cells within dynamic mechanical environments through endocytosis and membrane fusion, both mediated by the class I fusion protein spike. In SARS-CoV and SARS-CoV-2, the spike engages the human ACE2 receptor through a catch bond--an interaction whose lifetime increases under tensile force. Concurrently, mechanical pulling facilitates disruption of the S1/S2 subunits of spike, a critical step for membrane fusion. To elucidate how mechanical cues coordinate these processes, we developed a unified elastic-stochastic model that integrates theoretical analysis and computational simulations to trace viral entry. Our results identify the force-regulated catch bond between spike and ACE2 as a key determinant of successful invasion. This catch bond not only enhances receptor-mediated endocytosis but also increases the probability of S1/S2 disengagement, thereby promoting membrane fusion. Importantly, under conditions of strong catch bonding, the force-accelerated separation of S1 and S2 fine-tunes the balance between entry pathways. These findings uncover a potential mechanobiological mechanism that mediates viral cell entry by coupling receptor binding strength with spike disassembly under force. By characterizing these mechanical regulations, this work facilitates the assessment of emerging viral threats and inspires the design of drug delivery systems that leverage catch-bond kinetics for enhanced targeting.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 2%
18.1%
2
Nature Communications
4913 papers in training set
Top 22%
8.9%
3
Nature Physics
39 papers in training set
Top 0.3%
6.2%
4
Science
429 papers in training set
Top 6%
6.1%
5
eLife
5422 papers in training set
Top 15%
6.1%
6
Science Advances
1098 papers in training set
Top 3%
4.7%
50% of probability mass above
7
Advanced Science
249 papers in training set
Top 4%
4.2%
8
ACS Nano
99 papers in training set
Top 1%
3.8%
9
Cell Reports
1338 papers in training set
Top 15%
3.6%
10
Cell Systems
167 papers in training set
Top 5%
2.7%
11
Neuron
282 papers in training set
Top 5%
2.3%
12
Nucleic Acids Research
1128 papers in training set
Top 9%
2.0%
13
Cell
370 papers in training set
Top 10%
2.0%
14
Biophysical Journal
545 papers in training set
Top 3%
1.8%
15
Journal of the American Chemical Society
199 papers in training set
Top 3%
1.6%
16
PRX Life
34 papers in training set
Top 0.4%
1.6%
17
Nature
575 papers in training set
Top 12%
1.4%
18
Nano Letters
63 papers in training set
Top 2%
1.3%
19
Developmental Cell
168 papers in training set
Top 10%
1.3%
20
Nature Materials
21 papers in training set
Top 0.8%
0.9%
21
PLOS Computational Biology
1633 papers in training set
Top 21%
0.9%
22
ACS Central Science
66 papers in training set
Top 2%
0.9%
23
PNAS Nexus
147 papers in training set
Top 1%
0.9%
24
Communications Physics
12 papers in training set
Top 0.5%
0.8%
25
Nature Chemical Biology
104 papers in training set
Top 4%
0.8%
26
Molecular Cell
308 papers in training set
Top 10%
0.8%
27
Nature Structural & Molecular Biology
218 papers in training set
Top 5%
0.8%
28
Nature Nanotechnology
30 papers in training set
Top 1%
0.8%
29
Protein & Cell
25 papers in training set
Top 3%
0.7%
30
Cell Discovery
54 papers in training set
Top 5%
0.7%