Bioinformatic prediction, qCLASH, and AlphaFold3 reveal non-overlapping layers of the miR-214-3p-mRNA interactome

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.

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.

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.

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.