miRNAProtPred: computational prediction of human miRNA binding based on seed complementarity and thermodynamic...

Computational prediction of microRNA-target interactions is essential for understanding post-transcriptional regulation, yet existing tools often require manual data curation, lack comprehensive miRNA databases, or provide limited guidance for experimental prioritization.

We developed miRNAProtPred, a Python package that consolidates established seed complementarity matching and ViennaRNA-based thermodynamic analysis into a streamlined workflow for predicting human miRNA binding sites on diverse target sequences. The tool integrates 2,656 curated human miRNAs from miRDB and miRBase, accepts diverse input formats (DNA, RNA, or protein sequences), and classifies predictions through a multi-criteria confidence framework incorporating seed complementarity, thermodynamic stability (minimum free energy, MFE), flanking AU content, motif identity, and match type. miRNAProtPred supports two user-selectable search modes: a default strict mode requiring exact Watson-Crick seed complementarity, and a relaxed mode that extends sensitivity to G:U wobble-supported interactions through a hierarchical exact-first fallback strategy. The tool was evaluated using experimentally validated antiviral miRNAs from SARS-CoV-2 and HIV-1, and independently benchmarked on the miRAW dataset (62,215 miRNA-target pairs).

For SARS-CoV-2, miRNAProtPred successfully identified all 16 experimentally supported inhibitory miRNAs compiled from multiple independent studies (100% recovery in strict mode), with 12 of 16 (75%) classified as high confidence (MFE ≤ -12 kcal/mol). For HIV-1, 11 of 13 (84.6%) validated miRNAs were identified through canonical seed matching, with 7 of 11 (63.6%) classified as high confidence; the remaining two miRNAs (hsa-miR-92a-3p and hsa-miR-382-5p) were recovered through the wobble-permissive relaxed mode, achieving complete recovery across both viral systems. Large-scale evaluation on the miRAW benchmark dataset confirmed the precision-oriented performance profile, with strict mode achieving 98.66% precision, 73.97% recall, an F1-score of 0.846, and a Matthews correlation coefficient of 0.748. Validated miRNAs showed thermodynamic enrichment compared to genome-wide predictions (SARS-CoV-2: -11.87 vs. -9.695 kcal/mol; HIV-1: -12.13 vs. -11.215 kcal/mol), supporting MFE-based prioritization.

miRNAProtPred provides a streamlined, pip-installable tool for predicting human miRNA binding sites on diverse target sequences, facilitating candidate prioritization for experimental validation. The package is freely available at https://github.com/somenath-combio/mirnaprotpred.