## 2 Materials and Methods  
### Dataset and Data Preprocessing  
In this study, as a data resource, several AMP databases were investigated. Database of Antimicrobial Activity and Structure of Peptides (DBAASP v.2. [[http://dbaasp.org](http://dbaasp.org)]([http://dbaasp.org](http://dbaasp.org)), accessed on 10 August 2021) [47] was chosen due to the following reasons: (i) DBAASP is one of the most comprehensive AMP databases and it is widely used in literature. (ii) This database provides users with detailed information about the activity of thousands of peptides, where the antimicrobial activity has been tested experimentally or in silico against more than 4200 different organisms (bacteria, fungi, some parasites, viruses, and cancer cells). (iii) DBAASP has an application programmable interface (API). (iv) While most other databases were outdated, DBAASP is being updated frequently. Therefore, in this study, we have compiled our dataset from DBAASP. In Figure 1, we illustrate our data preprocessing steps. In terms of synthesis type, ribosomally synthesized peptides, non-ribosomally synthesized peptides, and synthetic peptides were included in our datasets (Figure 1, Step 1). In terms of peptide complexity, we focused on monomers since 90% of the peptides in databases are monomeric peptides which consist of only one polypeptide chain (Figure 1, Step 2). Most of the property calculation algorithms recognize natural amino acids. Hence, the peptides which contain non-standard amino acids, or which have N and C terminal modifications were removed from the datasets (Figure 1, Step 3 and 4). As a continuation of this work, we plan to perform de novo antimicrobial peptide design by using the dataset that we have compiled in this study. Along this line, in therapeutic applications, the prediction of non-hemolytic peptides are reported as more important than the hemolytic peptides for the elimination of the detrimental effects of AMPs on the host [48]. Hence, here we focused on non-hemolytic peptides, and the  
Figure 1: Workflow of data preprocessing. peptides having hemolytic activity against human erythrocytes were removed from the datasets (Figure 1, Step 5). AMPs exhibit their antimicrobial effects mainly through two different mechanisms. The membrane-targeting AMPs disrupt cell membrane integrity and lead to cytoplasmic leakage while the AMPs that use non-membrane targeting mechanisms mainly inhibit essential intracellular functions by interfering with DNA, RNA or proteins. AMPs shorter than 20 aa usually exert their antimicrobial effect by using non-membrane target mechanisms and they are defined as cell-penetrating antimicrobial peptides [49; 50]. However, in this study, we focused on membrane-active peptides which are generally longer than 20 aa. Among the peptides longer than 20 aa in DBAASP, most of the peptide entries are shorter than 50 aa, hence we have selected the peptides with lengths ranging from 20 to 50 aa (Figure 1, Step 6). Linear cationic antimicrobial peptides (LCAMPs) are the largest class of AMPs and they are widely found in different organisms [49]. Therefore, LCAMPs which have antimicrobial activity against Gram-negative bacteria including _Escherichia coli_, _Pseudomonas aeruginosa_, _Acinetobacter baumannii_ species, and Gram-positive bacteria including _Staphylococcus aureus_, _Listeria monocytogenes_, _Bacillus cereus_ species are selected from the DBAASP (Figure 1, Step 7 and 8). The CD-HIT [50] program was used to eliminate the sequences that have more than 80% identity (Figure 1, Step 9). The CD-HIT program is widely used in the AMP prediction problem for removing highly similar sequences [51; 52; 53; 54; 55; 56; 57; 58; 59]. In this study, the class labels of peptides are assigned according to the antimicrobial peptide activities against target organisms. In this respect, Minimum Inhibition Concentration (MIC) values are widely used to assess the in vitro levels of susceptibility or resistance of specific bacterial strains to a particular AMP [50].