miRNAProtPred: computational prediction of human miRNA binding based on seed complementarity and thermodynamic stability
Somenath Dutta, Manisha Pritam, Sudipta Sardar, Nitimoy Mondal, Sun Gu Lee · 2026
Read miRNAProtPred: computational prediction of human miRNA binding based on seed complementarity and thermodynamic stability by Somenath Dutta, Manisha Pritam, Sudipta Sardar, Nitimoy Mondal, Sun Gu Lee (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13341078.txt.
Description
Predicting where microRNAs bind their targets is the job of miRNAProtPred, a 2026 software paper by Somenath Dutta and colleagues. The Python package merges seed matching with thermodynamic stability checks from ViennaRNA into one workflow needing little manual setup.
It draws on 2,656 curated human miRNAs and accepts DNA, RNA, or protein inputs, scoring candidates by seed fit, free energy, flanking AU content, and match type. A strict mode demands exact seed pairing, while a relaxed mode allows G:U wobble. Tests recovered all 16 known antiviral miRNAs for SARS-CoV-2, with 75% rated high confidence, and benchmarking used 62,215 miRNA-target pairs from miRAW.
Catalog record
| CREATOR | Somenath Dutta, Manisha Pritam, Sudipta Sardar, Nitimoy Mondal, Sun Gu Lee |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 119050 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |