Stefano Rocca

Exacerbations of respiratory viral infections significantly contribute to morbidity and healthcare burden. Among these viruses, Human Rhinoviruses (HRVs) are the most frequent causative agents of upper respiratory tract infections. To date, over 150 HRV serotypes have been identified, classified into three species: HRV-A, HRV-B, and HRV-C. No antiviral therapies are currently available against this viral family, largely due to the high serotype diversity and limited cross-protection. The major group of HRVs relies on the Intercellular Adhesion Molecule-1 (ICAM-1) receptor to infect airway epithelial cells, making ICAM-1 an attractive target for broad-spectrum therapeutic interventions. Here, we report the development of nucleic acid-based aptamers designed to disrupt ICAM-1-HRV binding and thereby prevent viral infection. Aptamers are single-stranded DNA molecules that fold into precise three-dimensional structures, enabling highly specific protein recognition. Using a Systematic Evolution of Ligands by EXponential Enrichment (SELEX) approach guided by a minimal peptide mimicking the ICAM-1 viral binding interface, a library of >10^24 random single-stranded DNA sequences was screened. Through iterative rounds of selection, we identified eight candidate 77-nt DNA aptamers, which were subsequently evaluated for their potential using in silico and in vitro assays, as well as functional assays in human epithelial cells. From this strategy, two lead aptamers were selected that effectively inhibited HRV-A16 replication in a concentration-dependent manner, as measured by viral titers (TCID50 assay) and viral RNA quantification by RT-PCR. These findings demonstrate the potential of ICAM-1-targeting aptamers as antiviral agents capable of preventing HRV entry. By targeting a host receptor and creating a protective barrier at the cell surface, this approach may offer a broadly applicable strategy against multiple HRV serotypes, paving the way for the development of novel antiviral interventions.

Although protein–RNA interactions are crucial for many biological processes, predicting their binding free energies (Δ G ) is a challenging task due to limited available experimental data and the complexity of these interactions. To address this issue, we developed a machine learning–based model designed to predict energy-based scores for protein–RNA complexes, called PANTHER Score. By applying a local-to-global approach, we proposed a methodology further subdivided into five steps: (1) We derived 87,117 pairwise local interaction energies from 331,744 MD-derived interactions across 46 curated protein–RNA complexes; (2) we trained ML models on pairwise interaction features to predict local interaction energies without performing MD simulations; (3) we integrated predicted local interaction energies using a local-to-global methodology, to compute model-specific PANTHER Score; (4) we evaluate model-specific PANTHER Score on an independent test set of seven complexes; and (5) we validated and selected the optimal model using an external stress set of 110 complexes with experimental Δ G values for implementation in the PANTHER Scoring pipeline. Among the regression models developed, Random Forest Regression exhibited the highest predictive performance as a model-specific PANTHER Score, achieveing a Pearson correlation ( r ) of 0.80 and MAE of 1.79 kcal/mol on the test set. It maintained strong predictive capabilities on the stress set ( r = 0.64, MAE = 1.63 kcal/mol). Benchmarking against existing tools on the stress test set, the PANTHER Score demonstrated superior accuracy and reliability. This study highlights the effectiveness of MD and machine learning in addressing data limitations through innovative strategies, positioning the PANTHER Score as a robust tool for predicting protein–RNA binding affinities in biomolecular research, drug discovery and mainly in RNA-therapeutics.