# Antimicrobial Peptide Screening from Microbial Genomes in Sludge Based on Deep Learning
## 4 Discussion  
Deep learning models exhibit enormous potential in handling and learning the complex relationships within large peptide sequences and their structural properties, particularly in antimicrobial peptide prediction [25]. Through appropriate training and validation, these models can learn physicochemical features such as amino acid sequences and molecular properties, enabling a more comprehensive prediction. However, the quality and availability of data, especially high-quality annotated data, are crucial for the model's generalization ability. Insufficient or inappropriate model design with respect to data can lead to overfitting or underfitting, thereby affecting prediction accuracy [26]. Therefore, when utilizing machine learning models for antimicrobial peptide prediction, the selection of an appropriate model, features, and data preprocessing methods, along with consideration of the biological background, become crucial factors [27]. As shown in Table 5, In a recent study, [PERSON] et al. [28] utilized machine learning methods to synthesize 216 peptides from 241 segments and found that 181 of them exhibited antimicrobial activity, achieving a hit rate of 83.8%, surpassing previous efforts. Similarly, the University of Macau developed \"deep-ampep30\" [29] using deep learning to study peptides. This method, based on an optimal feature set and convolutional neural network, achieved an accuracy of 77% and an area under the ROC curve of 85%. \"AMP-EBiLSTM\" demonstrated the application of deep learning technology, specifically the enhanced bidirectional long short-term memory model, in AMP identification, achieving an accuracy of 92.39% and an AUC close to 0. 98. [PERSON]'s DeepLPI model [30] achieved an AUROC of 0.857 on the BindingDB dataset and an AUROC of 0.925 on the Davis dataset. This model is composed of a one-dimensional convolutional neural network (1D CNN) based on ResNet and a bidirectional long short-term memory network (biLSTM). Considering the experimental data results of this study, ALSTM achieved a remarkable AUC of 0.99, while LSTM reached an AUC of 0.95. The LSTM model demonstrated a high recall of 0.966, and the ALSTM model excelled in precision with a value of 0.952. In comparison, this study shows superior performance in evaluation metrics. The results of molecular docking revealed that peptide segments extracted from _Clostridiales_, _Caloramator_ sp. _E03_, and _Synergistaceae_ exhibited high affinity and low root-mean-square deviation (RMSD) values when interacting with the antimicrobial peptide receptor protein (PDB ID: 3 LFZ). Similarly, [PERSON] et al. [32] utilized AutoDock Vina to conduct molecular docking studies on compounds isolated from the plant _Ocimum cutfodonti_ against _Escherichia coli GyraseB_ (PDB ID: 6F86) to screen for antibacterial compounds. In this study, the model with the highest affinity showed a binding free energy of \(-7.4\) kcal/mol, similar to [PERSON]'s [33] findings on the antibacterial activity of nucleosides and Schiff base derivatives and their molecular docking interactions with target proteins. The analysis above indicates that antimicrobial peptides selected through machine learning techniques exhibit high binding efficiency and specificity at the molecular level. In comparison to related research, the experimental methods in this study demonstrate rationality and foresight in antimicrobial peptide screening and molecular docking analysis. The newly identified antimicrobial peptide candidates not only expand the resource library of antimicrobial peptides but also provide a new perspective and approach to addressing antibiotic resistance issues. Recent research reveals significant differences between ribosomally synthesized antimicrobial peptides (RiPPs), such as lentibiotics, and non-ribosomally synthesized peptides (NRPs). RiPPs undergo ribosomal synthesis and post-translational modifications, resulting in distinctive structures like lantipeptides, containing unusual amino acids like dehydroalanine and lanthionine residues [34].