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Firmani, S. ; Steinbauer, F.* ; Kasneci, G.* ; Marsico, A. ; Horlacher, M.

RIBEX: Predicting and explaining RNA binding across structured and intrinsically disordered regions (IDR)-rich proteins.

Bioinformatics 42:btag471 (2026)
Verlagsversion Forschungsdaten DOI
Open Access Gold
Creative Commons Lizenzvertrag
MOTIVATION: RNA-binding proteins (RBPs) regulate post-transcriptional processes, yet many remain undiscovered because RNA-binding activity often occurs outside canonical RNA-binding domains (RBDs), including within intrinsically disordered regions (IDRs) or through protein complexes. Computational methods can help identify novel RBPs, but approaches relying solely on sequence-derived features or ignoring the cellular interaction context are limited in capturing the complexity of RNA-binding behavior. To date, no framework rigorously integrates both sequence information and protein interaction context for RBP prediction. RESULTS: We introduce RIBEX, a multimodal framework that combines protein language model (pLM) embeddings with protein interactome topology to improve RBP prediction and interpretation. Specifically, we integrate sequence representations with graph-derived positional encodings (PE) from the human STRING protein-protein interaction (PPI) network. PE are computed using Personalized PageRank, reduced with principal component analysis, and fused with pooled sequence embeddings through FiLM conditioning, while Low-Rank Adaptation (LoRA) enables parameter-efficient task adaptation. Across both an annotation-based benchmark and experimental RNA Interactome Capture (RIC) dataset, PE consistently improves predictive performance, indicating that interactome topology provides complementary information beyond sequence features. LoRA adaptation of ESM2-650M further yields larger gains than simply scaling frozen backbone size. RIBEX outperforms state-of-the-art methods such as RBP-TSTL and HydRA, particularly on challenging subsets including proteins lacking canonical RBDs and those enriched in IDRs. For interpretability, we combine sequence-level computational alanine scanning with network-level positional-encoding ablation and inverse-PCA mapping, recovering known RNA-binding domains, IDR-associated contributions, and functional interactome communities linked to RBP predictions.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Molecular-basis; Recognition
e-ISSN 1367-4811
Zeitschrift Bioinformatics
Quellenangaben Band: 42, Heft: 2, Seiten: , Artikelnummer: btag471 Supplement: ,
Verlag Oxford University Press
Verlagsort Oxford
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Artificial Intelligence Cooperation Unit (HAI)
Institute of Computational Biology (ICB)
Förderungen Cluster for Nucleic Acid Sciences and Technologies-NUCLEATE
SFB TRR267
Deutsche Forschungsgemeinschaft
Technical University of Munich
Helmholtz Association under the joint research school Munich School for Data Science-MUDS