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A Polyaniline-based Sensor of Nucleic Acids

A Polyaniline-based Sensor of Nucleic Acids

Published on: November 1, 2016

PANTHER Score: Protein-Affinity for Nucleic Target-binding, Hybridization, and Energy Regression.

Parisa Aletayeb¹, Akash Deep Biswas², Stefano Rocca¹

¹Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Milano 20133, Italy.

RNA (New York, N.Y.)

|December 3, 2025

Summary

This summary is machine-generated.

We developed the PANTHER score, a machine learning model to predict protein-RNA binding free energies (ΔG). This approach overcomes data limitations, offering a reliable tool for biomolecular research and drug discovery.

Keywords:

RNA-therapeutics binding free energy (ΔG)machine learning models pairwise interaction energies predictive modeling protein-RNA interactions

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Area of Science:

Computational Biology
Biophysics
Machine Learning

Background:

Protein-RNA interactions are vital for cellular functions.
Accurate prediction of binding free energies (ΔG) is challenging due to data scarcity and interaction complexity.

Purpose of the Study:

To develop a machine learning model, the PANTHER score, for predicting protein-RNA binding free energies.
To address limitations in experimental data for protein-RNA interaction studies.

Main Methods:

A local-to-global approach was used, deriving local interaction energies from molecular dynamics simulations.
Machine learning models were trained to predict local interaction energies, integrated into the PANTHER score.
The model was evaluated on test and external stress sets, including 110 complexes with experimental ΔG.

Main Results:

Random Forest Regression achieved the highest predictive performance, yielding a Pearson correlation coefficient (r) of 0.80 and MAE of 1.79 kcal/mol on the test set.
The model demonstrated strong predictive capabilities on the stress set (r=0.64, MAE=1.63 kcal/mol).
The PANTHER score outperformed existing tools in benchmarking tests.

Conclusions:

The PANTHER score is an effective tool for predicting protein-RNA binding affinities.
Machine learning can overcome data limitations in predicting complex biomolecular interactions.
This method advances biomolecular research and drug discovery by providing accurate binding energy predictions.