Sequence architecture of RNA reveals key molecular signatures driving RNA phase separation
- 1Indian Institute of Technology Kharagpur
Abstract
Biomolecular condensates are dynamic membraneless assemblies that organize various cellular processes through liquid-liquid phase separation (LLPS). RNA is an active regulator of biomolecular condensates and influence their formation through sequence-encoded features. Despite its established role in condensate formation, no computational framework exists to identify underlying signatures to predict LLPS-associated RNAs. We develop an interpretable hybrid framework to distinguish reviewed, experimentally supported LLPS-associated RNAs from background RNAs. We uncover biologically meaningful sequence properties associated with condensate participation. Each RNA is represented by 167 handcrafted descriptors capturing composition, k-mer usage, sequence complexity, repeat organization and dinucleotide autocorrelation together with 640-dimensional contextual embeddings from RNA-FM. These representations are integrated through cross-validated stacking with neural-network meta-learner. The framework achieves an AUC-ROC of 0.935, AUC-PR of 0.944 and MCC of 0.782. Feature statistics, ablation analysis and SHAP interpretation reveal a distributed sequence architecture dominated by altered local k-mer usage with additional contributions from low-complexity burden, repeat-associated organization and long-range dinucleotide pattern. Biological case studies including MALAT1 and TMPO-AS1 further connect these patterns with experimentally characterized modes of multivalent RNA-protein assembly. Together, the results show how interpretable machine learning can move beyond prediction to identify sequence features that generate mechanistic hypotheses for RNA-mediated condensation.
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BibTeX
@misc{chatterjee2026sequence,
title = {{Sequence architecture of RNA reveals key molecular signatures driving RNA phase separation}},
author = {S. Chatterjee and R. P. Bahadur},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.23.761066},
url = {https://www.biorxiv.org/content/10.64898/2026.10.23.761066v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Sequence architecture of RNA reveals key molecular signatures driving RNA phase separation
AU - S. Chatterjee
AU - R. P. Bahadur
PY - 2026
DA - 2026/10/23
PB - bioRxiv
DO - 10.64898/2026.10.23.761066
UR - https://www.biorxiv.org/content/10.64898/2026.10.23.761066v1
AB - Biomolecular condensates are dynamic membraneless assemblies that organize various cellular processes through liquid-liquid phase separation (LLPS). RNA is an active regulator of biomolecular condensates and influence their formation through sequence-encoded features. Despite its established role in condensate formation, no computational framework exists to identify underlying signatures to predict LLPS-associated RNAs. We develop an interpretable hybrid framework to distinguish reviewed, experimentally supported LLPS-associated RNAs from background RNAs. We uncover biologically meaningful sequence properties associated with condensate participation. Each RNA is represented by 167 handcrafted descriptors capturing composition, k-mer usage, sequence complexity, repeat organization and dinucleotide autocorrelation together with 640-dimensional contextual embeddings from RNA-FM. These representations are integrated through cross-validated stacking with neural-network meta-learner. The framework achieves an AUC-ROC of 0.935, AUC-PR of 0.944 and MCC of 0.782. Feature statistics, ablation analysis and SHAP interpretation reveal a distributed sequence architecture dominated by altered local k-mer usage with additional contributions from low-complexity burden, repeat-associated organization and long-range dinucleotide pattern. Biological case studies including MALAT1 and TMPO-AS1 further connect these patterns with experimentally characterized modes of multivalent RNA-protein assembly. Together, the results show how interpretable machine learning can move beyond prediction to identify sequence features that generate mechanistic hypotheses for RNA-mediated condensation.
ER -