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Bridging technical innovation and computational advances in studies of RNA–protein assemblies

Nature Reviews Genetics (2026)Cite this article

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Abstract

RNA-dependent protein assemblies — including the spliceosome, ribosome and RNA-dependent membraneless organelles — have crucial roles in diverse cellular processes through RNA scaffolding and hierarchical assembly. Various empirical techniques and artificial intelligence algorithms have been developed to help understand the architecture, dynamics and functional implications of RNA–protein complexes, and their further development is underway to comprehensively integrate this information. This Review explores how combining these diverse technologies will enhance our understanding of the biological functions of RNA-dependent protein assemblies. We first explore methodological frontiers, contrasting traditional approaches with new platforms, which enable the identification and tracking of RNA–protein assembly dynamics on the same RNA molecules. We then present avenues for integrating these new experimental techniques with machine-learning methods to improve both predictive models of RNA–protein assembly and functional RNA design. We discuss how the synergy between experimental and digital biology can drive new insights into disease mechanisms and therapeutic strategies, including targeted modulation of pathogenic RNA–protein assemblies. Finally, we examine roadmaps for future research, emphasizing the potential of closed-loop systems that iteratively refine our understanding of RNA–protein assemblies through cycles of hypothesis generation, prediction, experimentation and validation.

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Fig. 1: Ribonucleoprotein-mediated regulation across the RNA life cycle.
Fig. 2: Overview of selected ribonucleoprotein assemblies in eukaryotic cells.
Fig. 3: RNA and protein features that regulate the assembly of ribonucleoproteins.
Fig. 4: An integrative systems framework for discovery of ribonucleoprotein assemblies.

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Acknowledgements

This work was supported by the Department of Veterans Affairs Office of Research and Development, USVA Merit Review grant BX001409 to P.A.K.; by NIAMS/NIH (NIAMS, National Institute of Arthritis and Musculoskeletal and Skin Diseases; NIH, National Institutes of Health) grants AR045192 and AR49737 to P.A.K.; and by Swiss National Science Foundation Postdoc Mobility Fellowship P500BP-203019 and NIAMS/NIH K99/R00 Award 1K99AR086341-01 to L.D. The authors thank M. Pilo, P. Bernstein, N. Dow and A. Dazey for administrative assistance, and the members of the Khavari lab for discussions.

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Authors and Affiliations

  1. Program in Epithelial Biology, Stanford University, Stanford, CA, USA

    Luca Ducoli, Suhas Srinivasan, Eimon Amjadi & Paul A. Khavari

  2. Program in Cancer Biology, Stanford University, Stanford, CA, USA

    Paul A. Khavari

  3. Veterans Affairs, Palo Alto Healthcare System, Palo Alto, CA, USA

    Paul A. Khavari

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  1. Luca Ducoli
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  2. Suhas Srinivasan
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  3. Eimon Amjadi
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  4. Paul A. Khavari
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Contributions

L.D. designed the review. L.D. and S.S. researched data for the article and wrote the article. L.D. and S.S. generated the figures. L.D., S.S. and E.A. compiled the main tables. All authors contributed substantially to the discussion of the content. All authors reviewed and/or edited the manuscript before submission.

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Correspondence to Luca Ducoli or Paul A. Khavari.

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Nature Reviews Genetics thanks Yunjie Zhao, who co-reviewed with Haoquan Liu; Jernej Ule; and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

AlphaFold 3: https://alphafoldserver.com/welcome

AlphaFold 3 (Code): https://github.com/google-deepmind/alphafold3

CodonBERT (Code): https://github.com/Sanofi-Public/CodonBERT

DeepCLIP: https://deepclip.compbio.sdu.dk/

DeepCLIP (Code): https://github.com/deepclip/deepclip

LinearDesign (Code): https://github.com/LinearDesignSoftware/LinearDesign

mRNA-LM (Code): https://github.com/Sanofi-Public/mRNA-LM

Partner-RBR (Code): https://github.com/Hhhzj-7/Partner-RBR

PrismNet: http://prismnetweb.zhanglab.net/

PrismNet (Code): https://github.com/kuixu/PrismNet

PSPHunter: http://psphunter.stemcellding.org/

PSPHunter (Code): https://github.com/jsun9003/PSPHunter

pyRBDome (Code): https://git.ecdf.ed.ac.uk/sgrannem/pyRBDome_Core

RBP2GO: https://rbp2go.dkfz.de/

RBPbase: https://apps.embl.de/rbpbase/

RBPNet: https://biolib.com/mhorlacher/RBPNet

RBPNet (Code): https://github.com/mhorlacher/rbpnet

RBPsuite: http://www.csbio.sjtu.edu.cn/bioinf/RBPsuite/

RBPsuite (Code): https://zenodo.org/records/14949530

RBPWorld: http://research.gzsys.org.cn/eurbpdb2/index.html

RhoFold+: https://proj.cse.cuhk.edu.hk/aihlab/rhofold/

RhoFold+ (Code): https://github.com/ml4bio/RhoFold

RiNALMo (Code): https://github.com/lbcb-sci/RiNALMo

RNAmigos2 (Code): https://github.com/cgoliver/rnamigos2

RoseTTAFold All-Atom: https://app.superbio.ai/apps/66cee172d979583be60126b5

RoseTTAFold All-Atom (Code): https://github.com/baker-laboratory/RoseTTAFold-All-Atom

RoseTTAFoldNA: https://app.superbio.ai/apps/668c0b4c5a3d67435cdebf64

RoseTTAFoldNA (Code): https://github.com/uw-ipd/RoseTTAFold2NA

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Ducoli, L., Srinivasan, S., Amjadi, E. et al. Bridging technical innovation and computational advances in studies of RNA–protein assemblies. Nat Rev Genet (2026). https://doi.org/10.1038/s41576-026-00931-9

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