Peptide hemolytic activity analysis using visual data mining of similarity-based complex networks.

ABSTRACT: 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 puta

SUBMITTER: Castillo-Mendieta K 

PROVIDER: S-EPMC11452708 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature