Back

RNA2PS: A sequence-specific coarse-grained RNA model linking structure, thermodynamics and phase separation

Tejedor, A. R.; Luengo-Marquez, J.; Iscar, J. O.; Garcia, J. R.; Ocana, A.; Collepardo-Guevara, R.; Gonzalez, P. L.; Espinosa, J. R.

2026-04-26 biophysics
10.64898/2026.04.24.720717 bioRxiv
Show abstract

RNA plays a central role in the formation and regulation of biomolecular condensates, yet a quantitative understanding of how RNA sequence, structure, and thermodynamics jointly determine phase behaviour, particularly in repeat expansion RNAs, remains incomplete. Here, we introduce RNA2PS, a sequence-specific RNA coarse-grained model for phase-separation that predicts RNA structure from sequence, achieving quantitative agreement for both single-stranded conformations and duplex helical geometry relative to crystallographic PDB structures. RNA2PS represents each nucleotide by two beads that separate the phosphate-ribose backbone from the base. This representation decouples electrostatic interactions from directional base pairing, while explicitly incorporating strand polarity (5' [->] 3') and local sequence context at the trimer level. Canonical and wobble base pairing are modelled through a multi-body potential with sequence-dependent coordination. Importantly, RNA2PS captures sequence-dependent duplex stability at the nearest-neighbour level and reproduces experimental melting temperatures across a diverse set of sequences. RNA2PS shows that phase separation of trinucleotide repeat RNAs is governed by transient inter-strand duplexes that form reversible cross-links. Competition between intra- and intermolecular base pairing regulates the density of labile RNA-RNA interactions, giving rise to strong sequence- and length-dependent differences in condensation that reproduce cellular RNA foci formation. Overall, RNA2PS provides a near-quantitative predictive framework that links sequence-encoded hybridization thermodynamics to mesoscale condensation of pure RNA sequences.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

1
Nucleic Acids Research
1128 papers in training set
Top 0.1%
40.3%
2
Nature Communications
4913 papers in training set
Top 5%
19.1%
50% of probability mass above
3
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 16%
4.3%
4
PLOS Computational Biology
1633 papers in training set
Top 11%
3.1%
5
Biophysical Journal
545 papers in training set
Top 2%
2.9%
6
Science
429 papers in training set
Top 12%
2.1%
7
Cell Systems
167 papers in training set
Top 7%
1.7%
8
Advanced Science
249 papers in training set
Top 12%
1.5%
9
Nature Biotechnology
147 papers in training set
Top 5%
1.4%
10
eLife
5422 papers in training set
Top 49%
1.3%
11
Science Advances
1098 papers in training set
Top 25%
1.0%
12
Journal of the American Chemical Society
199 papers in training set
Top 4%
0.9%
13
Physical Review X
23 papers in training set
Top 0.4%
0.9%
14
iScience
1063 papers in training set
Top 31%
0.8%
15
ACS Nano
99 papers in training set
Top 4%
0.8%
16
PLOS ONE
4510 papers in training set
Top 67%
0.8%
17
Nature Methods
336 papers in training set
Top 6%
0.7%
18
Cell Reports
1338 papers in training set
Top 34%
0.7%
19
Nature Structural & Molecular Biology
218 papers in training set
Top 5%
0.7%
20
Communications Biology
886 papers in training set
Top 28%
0.7%
21
Structure
175 papers in training set
Top 4%
0.5%
22
Journal of The Royal Society Interface
189 papers in training set
Top 6%
0.5%
23
Scientific Reports
3102 papers in training set
Top 79%
0.5%
24
Nature
575 papers in training set
Top 18%
0.5%
25
Genome Biology
555 papers in training set
Top 9%
0.5%