School of Biological Sciences and Engineering, Yachay Tech University, Urcuquí, Ecuador.
CIIMAR-Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Porto, Portugal.
NPJ Syst Biol Appl. 2024 Oct 4;10(1):115. doi: 10.1038/s41540-024-00429-2.
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.
肽是很有前途的药物开发框架，但存在固有不良性质，包括溶血活性。我们旨在使用基于网络科学和数据挖掘的新方法深入了解溶血肽的化学空间。元数据网络 (METN) 可用于表征和发现与溶血肽相关的一般模式，而半空间近邻网络 (HSPN) 则代表溶血肽空间。考虑到网络中心性和肽相似性，使用最佳候选 HSPN 来提取各种溶血肽子集（支架）。这些支架已被证明可用于开发基于相似性的稳健模型分类器。最后，我们使用无对齐方法报告了 47 个可能的溶血基序，可在开发新型基于肽的药物时用作毒性特征。我们提供的证据表明，序列中的溶血基序数量可能与溶血的可能性有关。
