bioRxivBiophysicsPreprint
RNA2PS: A sequence-specific coarse-grained RNA model linking structure, thermodynamics and phase separation
Andrés R. TejedoriD, Juan Luengo-MárqueziD, J. Oller-IscariD, Jorge Ramı́reziD, Alberto OcañaiD, Rosana Collepardo‐GuevaraiD, Pablo Llombart GonzáleziD, Jorge R. EspinosaiD
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.
- assay: long-read sequencing
- assay: structural biology
- research focus: protein structure prediction
- research focus: sequence-to-function modeling
- approach: deep learning
- approach: statistical model
- Citations
- 1
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