# Antimicrobial Peptide Screening from Microbial Genomes in Sludge Based on Deep Learning
## 5 Conclusions  
This study successfully employed machine learning techniques to screen potential antimicrobial peptides in sludge microbiota. By integrating deep learning and bioinformatics, a novel method for antimicrobial peptide screening was proposed, incorporating an attention mechanism model. This approach effectively identified antimicrobial peptides with potential therapeutic effects, offering a new perspective for combating multidrug-resistant bacterial infections. This study successfully overcame the limitations of traditional screening methods, significantly improving screening efficiency and accuracy.  
Experimental results, validated through molecular docking and molecular dynamics simulations, indicated that the screened peptide segments LLPRLLARRY, GVREIHGLNPG  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Major Contribution** & **Recall** & **AUC** \\ \hline Machine learning [28] & The synthesis yielded 216 peptides from a pool of 241 segments, revealing that 181 of them possessed antimicrobial activity. & 83.80\% & \(\times\) \\ \hline Deep-AmPEP30 [31] & A new method, Deep-AmPEP30, has been proposed for predicting short-chain (\(\leq\)30 amino acids) antimicrobial peptides (AMPs). This approach combines the optimal feature set reduced by PseKRAAC amino acid composition and convolutional neural networks. The genome sequences of gut commensal yeast pseudohydnea were screened, leading to the discovery of a peptide comprising 20 amino acids. & \(\times\) & 0.85 \\ \hline AMP-EBiLSTM [29] & A deep learning strategy named AMP-EBiLSTM has been proposed for the accurate prediction of antimicrobial peptides. In the realms of deep learning and ensemble learning, the authors effectively utilized binary profile features (BPF) and pseudo-amino acid composition (PSEAAC) to capture local sequences and extract amino acid information. & 92.39\% & 0.97 \\ \hline DeepLPI [30] & With an AUROC of 0. 857 on the BindingDB dataset and an AUROC of 0. 925 on the Davis dataset, the authors introduced a novel deep learning-based model for predicting protein–ligand interactions. This model is particularly suitable for drug repurposing and is primarily composed of a one-dimensional convolutional neural network (1D CNN) based on ResNet and a bidirectional long short-term memory network (biLSTM). & \(\times\) & 0.925 \\ \hline Deep learning & The application of the ALSTM model, which combines the LSTM model with an attention mechanism, successfully predicted and screened antimicrobial peptides, ensuring high accuracy. & 96.60\% & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the proposed method with other antimicrobial peptide screening methods.  
GCLHTVRLVCRR, and FRTTLAPHVLTRLLAPCW exhibited efficient antimicrobial activity and good stability. These findings provide robust support for the future development of antimicrobial drugs. This process will necessitate interdisciplinary collaboration and sustained efforts to ensure that these newly discovered antimicrobial peptides can be safely and effectively applied to human health. Therefore, while this study has made significant progress in the discovery and identification of antimicrobial peptides, the ultimate clinical application of these findings remains a long-term and complex process.  
Conceptualization, Y-XL.; methodology, X.-B.J.; software, H-J.M.; validation, C.-M.X.; formal analysis, H.-S.L.; investigation, Z.-M.L.; resources, Q.W.; data curation, X.-B.J.; writing--original draft preparation, Y.-X.L.; writing--review and editing, C.-M.X.; visualization, Y.-XL.; supervision, H.-S.L.; project administration, C.-M.X.; funding acquisition, H.-J.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China, No. 62173007.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.