## 3 Feature Encoding Methods
### Secondary Structure-Based Methods  
To investigate the secondary structure profiles of AMPs, we introduced computational methods grounded in secondary structure prediction algorithms, such as SSEC or SSEB, enabling the residue-level analysis of the \(\alpha\)-helix, \(\beta\)-sheet, and coil conformations.  
**SSEC**  
The secondary structure element content (SSEC) is a method used in bioinformatics to analyse and predict the secondary structure content of protein sequences [138]. The secondary structures are the local conformations that a protein chain can adopt, such as \(\alpha\)-helices, \(\beta\)-sheets, and coils. Understanding the secondary structure content is crucial for predicting the overall structure and function of proteins. The secondary structure types include \(\alpha\)-Helices (H), \(\beta\)-Sheets (E), and Coils (C). H corresponds to helical structures that are stabilised by hydrogen bonds between backbone atoms. E refers to extended structures formed by hydrogen bonds between adjacent strands. C represents regions that do not form regular secondary structures. SSEC calculates the proportion of each secondary structure type within the protein sequence. This is typically done via prediction algorithms that analyse the sequence and assign secondary structure types to each residue. The content is usually expressed as a percentage or fraction of the total sequence length. Various algorithms and tools are used to predict secondary structures, such as PSIPRED and DSSP. These tools use machine learning models trained on known protein structures to make predictions. The secondary structure content can provide insights into protein stability, folding, and function. For example, proteins with a high \(\alpha\)-helix content might be more stable in certain environments, whereas those with beta-sheets might be involved in interactions with other molecules.  
**SSEB**  
Secondary structure element binary (SSEB) is a method used in bioinformatics to predict and represent the secondary structure of protein sequences in a binary format [139]. This approach simplifies the prediction of secondary structures by classifying each amino acid position in the sequence as belonging to one of the following three main secondary structure types: alpha-helix, beta-sheet, or coil. Each type is represented by a binary vector, making it easier to use in machine learning models and other computational analyses. Each amino acid position in the sequence is classified into one of three secondary structure types as follows: H, E, and C. H is represented by the binary vector \([1,0,0]\). E is represented by the binary vector \([0,1,0]\). C is represents the binary vector \([0,0,1]\). SSEB predictions are typically generated via machine learning models trained on known examples of secondary structures. These models can be based on various features, such as amino acid composition, sequence complexity, and physicochemical properties. The binary representation makes SSEB suitable for high-throughput analysis, allowing for the rapid screening of large datasets to identify secondary structure patterns in protein sequences. SSEB predictions can be integrated with other bioinformatics tools and methods to provide a more comprehensive analysis of protein secondary structures. For example, it can be combined with more detailed secondary structure prediction methods or used as a preliminary step in structural and functional studies.