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mirMachine: A One-Stop Shop for Plant miRNA Annotation

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

Somenath Dutta¹, Manisha Pritam², Sudipta Sardar¹

¹Department of Chemical and Biomolecular Engineering, Pusan National University, Busan, Republic of Korea.

Frontiers in Genetics

|July 8, 2026

Summary

The new miRNAProtPred tool accurately predicts microRNA (miRNA) binding sites on target sequences, aiding researchers in prioritizing experimental validation for post-transcriptional regulation studies.

Keywords:

Antiviral miRNA HIV-1 SARS-CoV-2 human miRNA miRNA-based therapy minimum free energy

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Last Updated: Jul 9, 2026

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Computational prediction of microRNA-target interactions is crucial for understanding gene regulation.
Existing tools often require manual curation and lack comprehensive databases or prioritization guidance.

Purpose of the Study:

To develop an automated Python package, miRNAProtPred, for predicting human miRNA binding sites.
To streamline the process of identifying and prioritizing miRNA-target interactions for experimental validation.

Main Methods:

Consolidated seed complementarity matching and thermodynamic analysis (ViennaRNA).
Integrated 2,656 human miRNAs from miRDB and miRBase.
Implemented multi-criteria confidence framework (seed complementarity, MFE, AU content, etc.).
Supported strict (Watson-Crick) and relaxed (G:U wobble) search modes.

Main Results:

Achieved 100% recovery of experimentally validated antiviral miRNAs for SARS-CoV-2 in strict mode.
Identified 84.6% of validated HIV-1 miRNAs via canonical seed matching, with complete recovery using relaxed mode.
Demonstrated high precision (98.66%) and recall (73.97%) on the miRAW benchmark dataset.
Validated miRNAs exhibited significantly lower minimum free energy (MFE) than genome-wide predictions.

Conclusions:

miRNAProtPred offers a user-friendly, pip-installable tool for efficient miRNA-target interaction prediction.
The tool facilitates prioritization of candidate interactions for experimental validation.
Freely available at https://github.com/somenath-combio/mirnaprotpred.