### Antimicrobial and Toxicity Screening of Virtual Peptides Reveals High AMP Diversity  
The 35 peptidomes generated from the previous in silico proteolysis were screened using a combination of three prediction tools to evaluate antimicrobial (AMPir, AMPlify, and Macrel), haemolytic (HemoPi, Macrel, and MQSSM), and toxic (CAPTP, ToxinPred3, and ToxTeller) properties, allowing for the final selection of non-haemolytic and non-toxic AMPs. This screening resulted in a total of 315 predictions. Detailed outputs from each prediction tool are provided in Dataset 4. To ensure accuracy, a consensus-based approach between three prediction tools in each mining step was employed. Venn diagrams were used to visualize the overlap between the predictions of different tools at each stage (Supplementary Figures S2-S4). This allowed for the identification of peptides that consistently met the desired criteria. After prediction, the datasets were filtered to remove redundant sequences and those that did not meet the specified criteria (e.g., haemolysis or toxicity). The final peptide datasets included 527,096 AMPs, 125,403 non-haemolytic AMPs, and 29,528 non-haemolytic and non-toxic AMPs. After reducing redundancy with CD-HIT at a 0.98 sequence identity threshold, these datasets were refined to 473,747 unique AMPs, 119,531 unique non-haemolytic AMPs, and 28,279 unique non-haemolytic and non-toxic AMPs (Table 3). The corresponding FASTA files for all the peptide libraries after antimicrobial and toxicity predictions, along with the total concatenated datasets, are available in Dataset 5 (AMPs), Dataset 6 (non-haemolytic AMPs), and Dataset 7 (non-haemolytic and non-toxic AMPs). From the highest to the lowest number, nr consensus peptides were detected at each screening step (AMP and non-haemolytic AMP, and non-haemolytic and non-toxic AMP), with db3 having the highest number (\(n=244894\); \(n=62423\); \(n=14816\)), followed by db5 (\(n=127952\); \(n=31909\); \(n=7580\)), db4 (\(n=77857\); \(n=19583\); \(n=4638\)), db6 (\(n=22,683\); \(n=5585\); \(n=1237\)), db7 (\(n=13,177\); \(n=3456\); \(n=867\)), db1 (\(n=537\); \(n=133\); \(n=36\)), and db2 (\(n=90\); \(n=24\); \(n=2\)), respectively (Table 3). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Proteolysis** & **Peptides** & **(1) AMP** & \
\begin{tabular}{c} **(2) Non-Haemolytic** \\ **AMP** \\ \end{tabular} &
\begin{tabular}{c} **(3) Non-Haemolytic** \\ **and Non-Toxic AMP** \\ \end{tabular} \\ \hline db1\_aspn & 4376 & 214 & 36 & 9 \\ db1\_chym & 2696 & 35 & 9 & 3 \\ db1\_gluc & 4447 & 298 & 82 & 20 \\ db1\_protok & 175 & 0 & 0 & 0 \\ db1\_tryp & 4318 & 49 & 8 & 4 \\ \hline Peptides db1 & 16,012 & 596 & 135 & 36 \\ nr Peptides db1 & 10,955 & 537 & 133 & 36 \\ \hline db2\_aspn & 698 & 31 & 4 & 0 \\ db2\_chym & 434 & 7 & 2 & 0 \\ db2\_gluc & 744 & 47 & 15 & 1 \\ db2\_protok & 25 & 1 & 0 & 0 \\ db2\_tryp & 670 & 11 & 3 & 1 \\ \hline Peptides db2 & 2571 & 97 & 24 & 2 \\ nr Peptides db2 & 1788 & 90 & 24 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of AMP mining results following in silico proteolysis. The table demonstrates the progression of the 35 initial datasets into robust, consensus detections of three peptide types: (**1**) AMPs, (**2**) Non-Haemolytic AMPs, and (**3**) Non-Haemolytic and Non-Toxic AMPs. This was achieved using three prediction tools at each mining step (Supplementary Figures S2-S4). The total number of peptides before and after removal of non-redundant (nr) peptides identified via CD-HIT at a 0.98 similarity threshold are represented. The peptides from AMPs and non-haemolytic and non-toxic AMPs continue to display a remarkable level of uniqueness. The maximum similarity coefficients, calculated using the Jaccard Index, were 6.37% (db5_protk/db7_protk), 5.78% (db3_protk/db5_protk), 3.73% (db3_protk/db7_protk), and 2.57% (db5_chym/db7_chym) for AMPs; and 3.00% (db3_gluc/db5_gluc), 2.81% (db5_aspn/db7_aspn), 2.60% (db3_aspn/db5_aspn), and 2.56% (db5_gluc/db7_gluc) for non-haemolytic and non-toxic AMPs (Figure 2b,c).