Pedretti, A. | Search bioRxiv

bioRxiv2026

Identification of ICAM-1-targeting DNA aptamers as a host-directed strategy to inhibit Human Rhinovirus infection

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 >1024 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 (TCID 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. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=131 SRC="FIGDIR/small/717810v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@130d8org.highwire.dtl.DTLVardef@2dd09borg.highwire.dtl.DTLVardef@1da744eorg.highwire.dtl.DTLVardef@109ea5b_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2025

Database for extended ligand-target analyses (DELTA): a new balanced resource for AI applications in drug discovery

We here present the DELTA resource, a database including balanced and annotated datasets of ligands for about 500 therapeutically relevant targets specifically collected for developing AI-based predictive models. For each target, DELTA comprises an optimized protein structure plus 200 experimentally tested ligands equally distributed between active and inactive molecules. All ligands are prepared by considering unspecified isomeric elements and combining semi-empirical calculations with MD simulations to explore their conformational space. The so-collected molecules allowed extended analyses of both ligands and targets, and the study presents some preliminary results. The performed analyses revealed that on average active ligands are larger than inactive molecules, while possessing a similar polarity. The scaffold analysis emphasized the expected and crucial role of aromatic systems, even though with some relevant differences between active and inactive molecules. Moreover, similar targets often show conserved binding sites and there is a limited but not negligible relationship between the similarity of binding sites and ligands suggesting that similar pockets tend to bind rather similar ligands. Finally, the collected biological data also allowed the analysis of the polypharmacological profile of the ligands endowed with more than one biological value. Most ligands bind two or three targets with diverse activities and almost always the bound targets belong to the same biological class. All the collected data are available for download at delta.unimi.it.

bioinformatics↗