## 4 Materials and Methods
### Activity and Strain-Specific Predictions  
To identify promising peptides with antibacterial (ABP), antiviral (AVP), antifungal (AFP), and anticancer (ACP) activities, we screened the representative CnSA dataset derived from the union dataset of the HC and HB centralities. Predictions were made using two main tools: the standalone version of iAMPCN [84], a DL framework based on convolutional neural networks, and the AMPDiscover [85] web server, an RF-based classifier. ABPs, AVPs, and AFPs were predicted using both tools, while ACPs were exclusively predicted by iAMPCN. To enhance robustness, we incorporated consensus predictions for each activity using complementary models: (I) ABPs: Predictions were refined with the AntiBP3 [86] web interface based on SVM classifier (Gram-variable ABP model, threshold = 0.51); (II) AVPs: Predictions were cross-validated with AI4 AVP [87], a DL tool using the PC6 protein-encoding method; (III) AFPs: Cross-checked with Antifp [88] web server, an SVM-based method (AntifpMain_binary_model3, threshold = 0.5); (IV) ACPs: Additional predictions used AntiCP 2.0 [89] webserver, SVM-based (model2, threshold = 0.5), and ModIAMP v4.3.0. [74] (SVM model). ACPs shorter than 38 AA were further filtered, and we used AcPEP [90] to identify potential targets via DL methods based on convolutional neural network and multitask learning. We then generated Venn diagrams to explore the union and intersection datasets for each peptide type (ABP, AVP, AFP, and ACP). For further refinement, strain-specific activity predictions were performed for ABPs, AFPs, and AVPs using DBAASP [11]. Specifically, for ABPs, three strain-specific antibacterial prediction models were employed [91; 92]: Model I: ML predictions based on AMP sequence data; Model II: Cluster-based predictions using peptide sequence data; and Model III: ML predictions integrating peptide and bacterial genome data. These were used to rank the ABPs against some important ESKAPEE pathogens [57] but also _Bacillus subtilis_. The predicted ABPs were hierarchically ranked according to the following: Rank I--ABPs intersecting all three antibacterial activity models and all three strain-specific models; Rank II--ABPs intersecting all three activity models and two strain-specific models; Rank III--ABPs in the union of activity models but intersecting all three strain-specific models (effective against 4-5 pathogens); and Rank IV--ABPs in the union of activity models intersecting all three strain-specific models. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/molecules30030550/s1](https://www.mdpi.com/article/10.3390/molecules30030550/s1)]([https://www.mdpi.com/article/10.3390/molecules30030550/s1](https://www.mdpi.com/article/10.3390/molecules30030550/s1)), Figure S1: Completeness scores of the 27 assembled transcriptomes obtained from SRA data (NCBI), assessed with BUSCO using the Metazoa dataset; Figure S2: Venn diagrams representing the number of predicted AMPs by AMPIir, AMPIify, and Macrel from each protease-database combination; Figure S3: Venn diagrams representing the number of predicted non-haemolytic AMPs by HemoPi, Macrel, and MQSSM from each protease-database pair; Figure S4: Venn diagrams representing the number of predicted non-haemolytic and non-toxic AMPs by CAPTP, ToxinPred3, and ToxTeller from each protease-database pair; Figure S5: HSPN clustering of the 1017 AMPs obtained from the intersection of the Harmonic and Hub-Bridge datasets within the CnSA; Figure S6: Strain-specific activity predictions for ABPs derived from the most representative CnSA datasets and Venn diagrams summarizing activity predictions for five bacterial strains--_Bacillus subtilis, Escherichia coli_, Klebsiella pneumoniae, _Pseudomonas aeruginosa_, and _Staphylococcus aureus_.