## 4 Materials and Methods
### Antimicrobial and Toxicity Screening  
To identify promising AMPs within each peptidome, we screened for those with no haemolytic activity and no toxic signatures. We adopted a consensus approach, considering peptides commonly predicted by three of the following models: Antimicrobial activity: (i) the alternative/mature model from AMPir [49], best suited for sequences after post-translational processing; (ii) Macrel: (Meta)genomic AMP Classification and Retrieval [67], by running the subcommand \"macrel peptides\"; and (ii) AMPlfiy [68], a deep-learning (DL) model for AMP prediction. Each one of these tools uses different classifiers--Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Networks (DNN), respectively, maximizing the discovery of novel AMPs. Peptides with a probability of antimicrobial activity greater than 0.5 across all tools were selected. Haemolytic potential: (i) Macrel [67], as the output of the subcommand \"macrel peptides\", also contains haemolytic probability, while the \"NonHemo\" AMPs were selected\"; (ii) the hybrid model in the standalone version of HemoPi [69], which integrates motif-based and SVM-based predictions, was used to considered to identify peptides with a score of <0.5 as non-haemolytic; and (iii) a multi-query similarity searching model (MQSSM-Ii) developed by [70], which can retrieve haemolytic peptides from a representative subset of the haemolytic sequence space. Consequently, the non-identified hits were treated as non-haemolytic peptides for our analysis. Toxic signatures: (i) a hybrid model implemented in ToxinPred3 [71], which combines motif- and ML-based predictions, where peptides with a recommended score of <0.38 were considered as non-toxic; (ii) ToxTeller [72], which contains four different ML-based predictors using logistic regression (LR), SVM, RF, and a scalable end-to-end tree-boosting system (XGboost), where we selected only the peptides predicted with a probability of 0 (non-toxic) among all models; and (iii) CAPTP [73], which uses in silico mutagenesis (ISM) interpretation methods, where we selected only those AMPs predicted as \"non-toxic\". The consensus predictions among of all the outputs generated at each screening step were identified through the construction of Venn Diagrams (Supplementary Figures S2-S4). Using Seqkit tool v2.6.1 [64], we conducted an all-vs-all comparison of the 35 generated dbs at each mining step (1-peptides, 2-AMPs, and 3-Non-haemolytic/non-toxic AMPs), employing the Jaccard Index as a pairwise similarity metric [65]. Then, to explore differences between peptidomes and AMP-based peptidomes, we analysed several physicochemical properties such as the aliphatic index, Boman index, hydrophobic ratio, instability index, isoelectric point, molecular weight, sequence charge, and sequence length, which were all calculated using ModlAMP v.4.3.0. [74]. As physicochemical features represent non-parametric and dependent data, significant differences were considered if \(p<0.05\) by using the Friedman tests with truncated data, followed by Wilcoxon Signed-Rank tests with Bonferroni corrections for pairwise comparisons [75; 76]. The final nr dataset of the non-toxic and non-haemolytic AMPs was used for further analysis.