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    Item type:Publication,
    StarPepWeb: an integrative, graph-based resource for bioactive peptides
    (Oxford University Press, 2024)
    López, Christian
    ;
    Cárdenas, Roberto
    ;
    Aguilera-Mendoza, Longendri
    ;
    Agüero-Chapin, Guillermin
    ;
    Martínez Ríos, Félix Orlando  
    Motivation: The rapid growth of bioactive peptide sequences presents challenges for organization and analysis. Existing repositories often specialize in functions, taxonomic origins, or structural classes, but most remain isolated, use heterogeneous metadata, and lack uniform descriptors or structural models. Few integrative web services exist, offering only partial coverage or depth. As a result, reproducible and comprehensive exploration of the bioactive peptide landscape remains limited, underscoring the need for a unified, source-tracked, extensible platform. Results: We present StarPepWeb, a freely accessible web application that democratizes access to StarPepDB, one of the largest curated repositories of bioactive peptides. The platform integrates 45 120 non-redundant sequences from 40 public databases into a source-tracked graph enriched with metadata, physicochemical features, and predicted 3D structures from ESMFold. Each peptide is represented with ESM-2 embeddings and iFeature descriptors, while the interface supports metadata-aware filtering, alignment-based similarity searches with single and multiple queries, and interactive visualization. A microservice-oriented architecture ensures scalability, maintainability, and reproducible versioned downloads, including Neo4j exports. StarPepWeb thus overcomes deployment and expertise barriers of the standalone database, providing an extensible, cloud-hosted framework for integrative bioactive peptide analysis. ©The authors ©Oxford University Press.
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    Item type:Publication,
    Half-Space Proximal Networks (HSPNs): A Proxy for Multi-Query Similarity Searching Models Predicting Tumor-Homing Peptides
    (American Chemical Society (ACS), 2025)
    Romero, Maylin
    ;
    Marrero Ponce, Yovani  
    ;
    Martínez Ríos, Félix Orlando  
    ;
    Agüero-Chapin, Guillermin
    ;
    Aguilera-Mendoza, Longendri
    Tumor-homing peptides (THPs) have emerged as promising agents in cancer treatments. These short sequences can specifically target tumor cells and vasculature. Here, a nontrained machine learning (ML) method based on network science and multiquery similarity searching to predict THPs is presented. We leverage the network-based representation of THPs’ chemical space to extract valuable information by employing a novel similarity-based, yet sparse, network known as the halfspace proximal network (HSPN). The HSPN of the THPs’ giant component is composed of 12 communities that represent distinct modes of action and/or targets, as well as sequence templates (scaffolds). In the HSPN analysis, various centrality measures were employed to identify the most significant and nonredundant THPs. These central THPs were then used as queries (Qs) in group fusion similarity-based searches against an established collection of known THPs. The performance of the resulting multiquery similarity-based search models (MQSSMs) was assessed using three benchmarking datasets of THPs/non-THPs. The MQSSMs derived from the HSPNs (THP2) demonstrated superior discrimination performance compared to the classical chemical space networks (CSNs, namely THP1) when applied to the THPs/non-THPs datasets Remarkably, exceptional MCC values (>0.887) were achieved when utilizing Qs from both CSN and HSPN networks to construct MQSSMs (THP3), employing a similarity threshold of 0.6, in external datasets. Next, we conducted a statistical comparison between the performance of our top-performing MQSSM, THP3, and several THP prediction servers, including TumorHPD, THPep, SCMTHP, and NEPTUNE. Our proposed model demonstrated its superiority by surpassing the state-of-the-art supervised and trained ML methods for THP prediction with statistically significant differences. These results provide strong evidence that network-based similarity searches are highly effective and reliable for identifying THPs. ©The authors ©American Chemical Society (ACS).