## 4 Discussion
As summarized in Tables 2 and 3, 0.92 and 0.90 AUC values were obtained for Gram-negative and Gram-positive datasets, respectively. Additionally, in this research effort, for the first time, feature scoring and feature ranking were performed for Gram-positive and Gram-negative datasets separately, and the importance (score) of each feature in these two data sets were compared. In order to understand the underlying structure of the data, we apply PCAon Gram-negative and Gram-positive datasets separately. The PCA results in Figure 6a-d show thatwhen we visualize the AMP and non-AMP samples with PCA plots, we have noticed that there are some outlier samples (peptides) in both Gram-negative and positive datasets. In order to understand more in detail why these samples are outliers and to compile a more homogenous dataset, we have examined the physico-chemical features of the peptides. To see the distribution of each feature, we plotted histograms for the Gram-negative and the Gram-positive datasets separately (Figure 7a,b). Based on our analysis using such histograms, we define a certain range of values for each attribute for the positive class which represents the peptides having antimicrobial activity as illustrated in Table 4. While the peptides within the selected ranges are kept, other peptides are eliminated from our dataset. Once again, PCA visualization has been applied to this outlier eliminated dataset and it has been observed that the peptides can be better separated into two classes in this new dataset (Figure 8a-d). For this outlier eliminated dataset, all classification experiments have been repeated. As shown in Tables 5 and 6, we have achieved higher performance metrics when outlier removal is applied. The studies on the structure-activity relationship of AMPs emphasized that the antimicrobial activity is affected by changes in many structural and physico-chemical parameters such as net charge, hydrophobicity, and peptide chain length. Therefore, studying these properties of peptides and the similarities and differences between these features provide important insights for the development of new antimicrobial peptide prediction methods [113]. In this study, the net charge was found as the most important feature for the Gram-negative data set while it is identified as the second most important feature for the Gram-positive dataset. The net charge is an important feature that shows the affinity of cationic peptides to bind to anionic cell surface structures through electrostatic interactions. In other words, the positive charge of the cationic AMPs enables an electrostatic interaction with the negatively charged bacterial cell wall components [114]. The outer surface of the Gram-negative bacteria contains lipopolysaccharidesaccharides (LPS), while Gram-positive bacteria contain acidic polysaccharides (teichoic acids). These structures confer a net negative charge to the surface of both Gram-positive and Gram-negative bacteria. In addition, the inner membrane of Gram-negative bacteria and the single membrane of Gram-positive bacteria are composed of negatively charged phospholipids. The net positive charge is the most conserved property of AMPs, making it possible to bind to the negatively charged outer surface of the bacteria [115]. Therefore, the net charge of AMPs has an essential role in the administration of peptide\(-\)membrane interactions resulting in the disruption of the membrane integrity [5]. The consistency of the results obtained with this computational study with the previous experimental results also supports the validity of the computational models created in this study. As mentioned above, Gram-positive and Gram-negative bacteria possess different cell wall components such as teichoic acid and lipopolysaccharidesaccharides (LPSs).