### In Silico Proteolysis of AMP Precursor Datasets with Distinct Proteases Yields Diverse Peptides  
After performing the in silico proteolysis using five distinct proteases--AspN, Chymotrypsin (Chym), GluC, Proteinase K (ProtK), and Trypsin (Tryp)--on the seven Cnidaria AMP precursors datasets, we generated 35 virtual peptide libraries, each corresponding to a specific protease--database combination. The resulting peptides were subsequently filtered to align with AMP characteristics by retaining peptides between 6-40 amino acids (AA) in length, removing duplicated and redundant peptides and eliminating peptides with non-standard AA (Table 2). The final peptides ranged from 11 to 40 AA in length, with an average length of 19 (Supplementary Table S4). After concatenating the peptide libraries from all protease-database combinations, we obtained a total of 12,428,038 peptides. Following redundancy reduction with cd-hit at a 0.98 sequence identity threshold, the dataset was reduced to 8,278,560 representative and nr peptides from AMP precursors. AspN and Trypsin produced the highest number of peptides in db1-db2 and db3-db7, respectively, while Protease K gathered the lowest number of peptides in all databases (Table 2). Db3 contains the highest number of nr peptides (_n_ = 4,229,977), followed by db5 (_n_ = 2,316,742), db4 (_n_ = 1,357,350), db6 (_n_ = 404,314), db7 (_n_ = 230,192), db1 (_n_ = 10,955), and db2 (_n_ = 1788) (Table 2). The corresponding FASTA files for all the 35 peptide libraries after applying in silico enzymatic digestion protocols, along with the total concatenated datasets, are provided in Dataset 3. The peptides displayed a remarkable level of uniqueness, making them ideal for further antimicrobial and toxicity predictions. The maximum similarity coefficients, calculated using the Jaccard Index, were 4.03%, 3.90%, 3.21%, and 3.18%. These values corresponded to the pairs db3_protk/db5_protk, db5_protk/db7_protk, db3_chym/db5_chym, and db5_chym/db7_chym, respectively, all within the Anthozoa group (Figure 2a). \begin{table}
\begin{tabular}{c c c c c c} \hline
**Proteolysis** & **Total** & **6–40 AA** & **Non-Duplicated** & **Non-Redundant** & **20 AA** \\
**Protocol** & **Peptides** & **Length** & **Peptides** & **Peptides** & **Alphabet** \\ \hline db1\_aspn & 15,851 & 8432 & 7980 & 4376 & 4376 \\ db1\_chym & 32,418 & 9242 & 8565 & 2696 & 2696 \\ db1\_gluc & 15,240 & 8172 & 7649 & 4447 & 4447 \\ db1\_protk & 68,118 & 3285 & 2999 & 175 & 175 \\ db1\_tryp & 19,198 & 9170 & 8442 & 4318 & 4318 \\ \hline db2\_aspn & 2748 & 1422 & 1311 & 698 & 698 \\ db2\_chym & 5075 & 1479 & 1338 & 434 & 434 \\ db2\_gluc & 2210 & 1261 & 1158 & 744 & 744 \\ db2\_protk & 10,692 & 555 & 494 & 25 & 25 \\ db2\_tryp & 3122 & 1376 & 1257 & 670 & 670 \\ \hline db3\_aspn & 73,527,51 & 4,175,173 & 3,694,840 & 2,115,242 & 1,820,209 \\ db3\_chym & 18,999,471 & 3,996,393 & 3,311,893 & 858,127 & 818,622 \\ db3\_gluc & 5,438,132 & 3,348,195 & 3,021,505 & 1,947,320 & 1,609,672 \\ db3\_protk & 33,464,859 & 1,417,423 & 1,040,671 & 59,408 & 58,164 \\ db3\_tryp & 8,467,153 & 4,350,869 & 3,846,043 & 2,082,476 & 1,849,069 \\ \hline \end{tabular}
\end{table} 
Table 2: Peptide filtering results for each proteolysis protocol. The number of putative antimicrobial proteins (AMP precursors) in each database after AMPir prediction (precursor model) is shown in brackets. Each protein library was subjected to a one-enzyme proteolysis protocol using five different proteases—AspN/aspn, Chymotrypsin/chym, GluC/gluc, Proteinase K/protk, and Trypsin/tryp. The number of peptides decreased with each filtering step, resulting in non-redundant (nr) peptides with AMP-like characteristics for each database and protease combination. Figure 2: _Cont._  
Figure 2: Peptide diversity across five proteolysis protocols in the seven Cnidaria databases during the AMP mining process, with the Jaccard similarity coefficient (%) used as a pairwise similarity metric to compare the 35 generated peptides.