## 3 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Species** & **Subphylum** & **Class (Order)** & **Candidate****ABP****Sequence** & **Rank** & **Predicted Antimicrobial Activity** \\ \hline _Alvepora_ & \multirow{2}{*}{Anth.} & HexacerMIA & YVWNPLRRPGLQVSCHSGSCKTSLAFGRLLKSKD & II & _B. subtilis_ \\ _japonica_ & & & & HexacerMIA & B. subtilis, E. coli, K. \\ _Ricordae yuma_ & \multirow{2}{*}{Anth.} & (Corallimmor- & CRSNRTQQWGGLSYIRLIGRASVVTLKQPL & III & _pneumoniae_, P. \\  & & & pharia) & & _aergiugas_ \\ _Montipora_ & \multirow{2}{*}{Anth.} & HexacerMIA & CSMRPISSSWLRFSKKIWSTSAR & III & _pneumoniae_, P. \\ _digitala_ & & & & & _aergiugas_ \\ _Phyldiscusis_ & \multirow{2}{*}{Anth.} & HexacerMIA & CWTWAVTPTFAHMGVQWRASQRVRSRLTN & III & _B. subtilis, E. coli, P. \\ _semiai_ * & & & & _aergiugas_, _S. aureus_ \\ _Polymyacs wellsi_ & \multirow{2}{*}{Anth.} & HexacerMIA & NISTKNSSASCRSLFGHCRFRTLSWLRGWGG & III & _pneumoniae_, P. \\ _Gunipora_ & \multirow{2}{*}{Anth.} & HexacerMIA & RPAISGAVTSGKQKAWGSVHKPLNCRSSLWGGG & III & _pneumoniae_, P. \\ _norfolkensis_ & & & & & _aergiugas_, _S. aureus_ \\ _Gunipora_ & \multirow{2}{*}{Anth.} & HexacerMIA & SGLRKSRMMKWPLSTGGRWSRGGLVA & III & _E. coli, K. pneumoniae_, P. \\ _norfolkensis_ & & & & & _E. coli, K. pneumoniae_, P. \\ _Galaxea_ & \multirow{2}{*}{Anth.} & HexacerMIA & YPKPKSLANWTRSSCGKLLWLTGRPHHLRAGSG & III & _E. coli, K. pneumoniae_, P. \\ _fascicularis_ & & & & & _P. aeruginosa_, S. aureus_ \\ \hline \hline  & & \multicolumn{2}{c}{Anth.} & \multicolumn{2}{c}{Andth.–Andthozoa; Med.–Medusozoa; Strains—_Bacillus subtilis, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa_ and _Staphylococcus aureus_. * Samples from tentacles.} \\ \end{tabular}
\end{table} 
Table 4: \(Cont.\)accounting for a total of nr 8,278,560 peptides. This is because trypsin generates short-length peptides with a basic Arg or Lys at the C-terminus [51]; chymotrypsin targets aromatic AAs [52]; proteinase K does not rely on specific cleavage motifs (abroad specificity) [53]; and AspN and GluC target acidic AA [36]. By applying three distinct prediction methods to evaluate antimicrobial, haemolytic, and toxicity properties--drawing inspiration from our previous study on cephalopod salivary glands [36]--we improved the accuracy and reliability of identifying AMPs that are both non-haemolytic and non-toxic to red blood cells and mammalian cells, respectively, thus removing the most undesired characteristics of AMPs [54; 55] and ensuring their suitability for future applications. Each tool utilizes different algorithms, prediction models, and features, complementing one another in identifying potential AMPs. Of the 473,747 AMPs, only 28,279 were predicted as non-haemolytic and non-toxic, revealing that not all AMPs obtained from marine organisms are safe to use. All databases were rather unique, as reflected by the Jaccard similarity coefficient. While sequence similarity is a valuable initial assessment to clarify overall similarities, it does not inherently imply functional or structural equivalence, particularly for peptides with fewer AA sequences. Nevertheless, for our purposes, it proved to be an effective and computationally efficient method for assessing similarities across large databases. A previous study on the transcriptomes of tentacle secretions of the sea anemone _Cmidopus japonicus_ also used similar algorithms to predict antimicrobial and cytotoxicity properties of AMPs within toxin-like proteins. Later, some demonstrated in vitro antimicrobial activity against _B. subtilis_ and _E. coli_[56]. Indeed, our analysis revealed that the characteristics of the final set of non-haemolytic and non-toxic AMPs closely resemble those of typical AMPs, indicating that our mining process was successful in identifying peptides with expected functional properties.