2026-10-10T19:05:08Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34027998 2026-09-29T22:04:12Z category_13 portal_316 item_type_3 month_year_09_2026

Data Sheet 10_Bioinformatic prediction, qCLASH, and AlphaFold3 reveal non-overlapping layers of the miR-214-3p–mRNA interactome.pdf Jia Yao (323339) Bingqiao Huang (24502521) Chenyu Chang (18000714) Ziqi Lin (8582466) Jinyue Lei (24502519) Weifeng Zhu (419164) Mo Lisha (24502524) Tielong Xu (284802) Genetics AlphaFold3 bioinformatic prediction cross-method concordance miRISC miRNA–mRNA interaction qCLASH Introduction<p>MicroRNAs (miRNAs) regulate gene expression post-transcriptionally through sequence-specific targeting of messenger RNAs. Reliable mapping of miRNA-mRNA interactions remains challenging because available methods differ widely and systematic cross-method comparisons are lacking.</p>Method<p>Using mouse miR-214-3p as a model, we systematically compared three approaches: bioinformatic prediction (TargetScan, miRDB, miRWalk), Argonaute-dependent experimental detection (qCLASH on mouse lung tissue), and artificial intelligence-driven structural modeling (AlphaFold3). Gene lists were harmonized to Ensembl release 116 (GRCm39) Gene IDs; overlaps were evaluated against a transcriptome background using Fisher’s exact tests with Benjamini–Hochberg correction, and the three-tool consensus was assessed by 1,000,000 Monte Carlo simulations.</p>Results<p>Only 95 target genes were commonly predicted by all three bioinformatic tools, and at most 6 of the 15 Ensembl-mappable qCLASH genes were captured by any prediction algorithm. Nevertheless, all observed overlaps were significantly enriched relative to random expectation (O/E ratios 24.52–80.6; adjusted q < 0.05), and the 95-gene consensus fell far outside the simulated null distribution (P ∼ MC∼ < 10⁻⁶). AlphaFold3 produced high-confidence models (ipTM ≥0.6) for 80.95% of qCLASH-derived interactions, but a substantial fraction of bioinformatically predicted sites failed to form structurally plausible complexes.</p>Discussion<p>Bioinformatic prediction, qCLASH, and AlphaFold3 thus capture distinct and complementary layers of miRNA targeting (sequence-defined potential, context-dependent occupancy, and spatial feasibility) rather than converging on a single landscape. High AlphaFold3 confidence scores, for instance, did not guarantee biologically functional conformations; even models meeting the thresholds often displayed non-canonical architectures incompatible with silencing. In our mouse lung miR-214-3p model system, our findings support an integrative approach: method selection should align with the specific research question, and multi-layer integration is key to constructing reliable miRNA-target maps in a given biological context.</p> 2026-09-29T22:04:12Z Dataset Dataset 10.3389/fgene.2026.1924314.s010 https://figshare.com/articles/dataset/Data_Sheet_10_Bioinformatic_prediction_qCLASH_and_AlphaFold3_reveal_non-overlapping_layers_of_the_miR-214-3p_mRNA_interactome_pdf/34027998 CC BY 4.0