Peptide hemolytic activity analysis using visual data mining of similarity-based complex networks
Peptides are promising drug development frameworks that have been hindered by intrinsic undesired properties including hemolytic activity. We aim to get a better insight into the chemical space of hemolytic peptides using a novel approach based on network science and data mining. Metadata networks (METNs) were useful to characterize and find general patterns associated with hemolytic peptides, whereas Half-Space Proximal Networks (HSPNs), represented the hemolytic peptide space. The best candidate HSPNs were used to extract various subsets of hemolytic peptides (scaffolds) considering network centrality and peptide similarity. These scaffolds have been proved to be useful in developing robust similarity-based model classifiers. Finally, using an alignment-free approach, we reported 47 putative hemolytic motifs, which can be used as toxic signatures when developing novel peptide-based drugs. We provided evidence that the number of hemolytic motifs in a sequence might be related to the likelihood of being hemolytic.
Authors
- Guillermı́n Agüero-Chapin (ORCID: https://orcid.org/0000-0002-9908-2418)
- Kevin Castillo-Mendieta (ORCID: https://orcid.org/0000-0002-0383-8285)
- César R. García‐Jacas (ORCID: https://orcid.org/0000-0002-3962-7658)
- Yovani Marrero‐Ponce (ORCID: https://orcid.org/0000-0003-2721-1142)
- Stephen J. Barigye (ORCID: https://orcid.org/0000-0003-3547-8293)
- Edgar Márquez (ORCID: https://orcid.org/0000-0002-7503-1528)
- Yunierkis Pérez‐Castillo (ORCID: https://orcid.org/0000-0002-3710-0035)
- Nelson Santiago Vispo (ORCID: https://orcid.org/0000-0002-4081-3984)
Institutions
- Consejo Nacional de Ciencia y Tecnología (GT)
- Universidad del Norte (CO)
- Universidade do Porto (PT)
- Universidad Yachay Tech (EC)
- Consejo Nacional de Ciencia y Tecnología (PY)
- Universidad de Las Américas (EC)
- Centro Nacional de Biotecnología (ES)
- Secretaría de Ciencia, Humanidades, Tecnología e Innovación (MX)
- Universidad Autónoma de Madrid (ES)
- Universidad San Francisco de Quito (EC)
- Universidad Panamericana (MX)
Publication Details
- Journal
- npj Systems Biology and Applications
- Published
- 2024-10-04
- DOI
- https://doi.org/10.1038/s41540-024-00429-2
- Citations
- 5
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 1.17
Funders
- Universidad Panamericana
- Universidad San Francisco de Quito
- Fundação para a Ciência e a Tecnologia