Mapping the Chemical Space of Antiviral Peptides with Half-Space Proximal and Metadata Networks Through Interactive Data Mining

Daniela de Llano García; Marrero Ponce, Yovani; Guillermin Agüero-Chapin; Hortensia Rodríguez; Francesc J. Ferri; Edgar A. Márquez; José R. Mora; Martínez Ríos, Félix Orlando; Yunierkis Pérez-Castillo

doi:10.3390/computers14100423

Mapping the Chemical Space of Antiviral Peptides with Half-Space Proximal and Metadata Networks Through Interactive Data Mining

Journal

Computers

ISSN

2073-431X

Publisher

MDPI AG

Date Issued

2025-10-03

Author(s)

Daniela de Llano García

Guillermin Agüero-Chapin

Hortensia Rodríguez

Francesc J. Ferri

Edgar A. Márquez

José R. Mora

Yunierkis Pérez-Castillo

Type

text::journal::journal article

DOI

10.3390/computers14100423

URL

https://scripta.up.edu.mx/handle/20.500.12552/12593

Abstract

Antiviral peptides (AVPs) are promising therapeutic candidates, yet the rapid growth of sequence data and the field’s emphasis on predictors have left a gap: the lack of an integrated view linking peptide chemistry with biological context. Here, we map the AVP landscape through interactive data mining using Half-Space Proximal Networks (HSPNs) and Metadata Networks (MNs) in the StarPep toolbox. HSPNs minimize edges and avoid fixed thresholds, reducing computational cost while enabling high-resolution analysis. A threshold-free HSPN resolved eight chemically and biologically distinct communities, while MNs contextualized AVPs by source, function, and target, revealing structural–functional relationships. To capture diversity compactly, we applied centrality-guided scaffold extraction with redundancy removal (90–50% identity), producing four representative subsets suitable for modeling and similarity searches. Alignment-free motif discovery yielded 33 validated motifs, including 10 overlapping with reported AVP signatures and 23 apparently novel. Motifs displayed category-specific enrichment across antimicrobial classes, and sequences carrying multiple motifs (≥4–5) consistently showed higher predicted antiviral probabilities. Beyond computational insights, scaffolds provide representative “entry points” into AVP chemical space, while motifs serve as modular building blocks for rational design. Together, these resources provide an integrated framework that may inform AVP discovery and support scaffold- and motif-guided therapeutic design.