## 3 Feature Encoding Methods
### Physicochemical-Based Methods
This multifaceted approach allows researchers to capture both the quantitative and qualitative aspects of peptide sequences, making it a powerful tool for predicting and understanding the antimicrobial properties of peptides. **PseAAC**  
Pseudo-Amino Acid Composition (PseAAC) is an advanced feature extraction method used in bioinformatics to represent peptide sequences by incorporating both sequence-order information and the physicochemical properties of amino acids [117; 118]. This method extends the traditional amino acid composition (AAC) by adding pseudo components that capture sequence-order effects, making it particularly useful for predicting protein functions and properties. The AAC calculates the frequency or proportion of each of the 20 standard amino acids in the peptide sequence. For example, if a peptide contains ten amino acids and two of them are lysine (K), the composition of lysine would be 20%. The pseudo components capture sequence-order information, which is crucial for understanding the structural and functional properties of peptides. These components are derived from the various physicochemical properties of amino acids, such as hydrophobicity, charge, and polarity. The probability of transitioning from one amino acid to another in the sequence. Distribution of amino acids: The positional distribution of amino acids along the sequence. The correlation between amino acids at different positions in the sequence was measured. Additional properties, such as hydrophobicity, charge, and flexibility, can be included to capture more detailed sequence-order effects. The PseAAC is typically calculated via a combination of AAC and pseudo components. The formula for the PseAAC can be expressed as Equation (2),  
\[PseAAC=AAC+\lambda\times Pseudo_{Components}, \tag{2}\]  
where AAC is the amino acid composition, \(\lambda\) is a weight factor that balances the contribution of the pseudo components, and \(Pseudo_{Components}\) are the additional sequence-order features. **APAAC**  
Amphiphilic pseudo amino acid composition (APAAC) is a specialised feature extraction method used in bioinformatics to represent peptide sequences, particularly those with amphiphilic properties, such as AMPs [119; 120]. This method extends the traditional PseAAC by focusing on the amphiphilic nature of peptides, which is crucial for their interaction with biological membranes. The AAC calculates the frequency or proportion of each of the 20 standard amino acids in the peptide sequence. For example, if a peptide contains ten amino acids and two of them are lysine (K), the composition of lysine would be 20%. The pseudo components in APAAC specifically capture the amphiphilic properties of peptides, which are essential for their ability to interact with and disrupt bacterial membranes. Hydrophobicity measures the hydrophobic nature of amino acids. Charge measures the charge distribution along the peptide sequence. Polarity measures the polarity of amino acids. Secondary structure propensity measures the propensity of amino acids to form secondary structures such as alpha-helices or beta-sheets. Solvent accessibility measures the solvent accessibility of amino acids. The APAAC is calculated by combining the AAC with the amphiphilic pseudo components. The formula for the APAAC can be expressed as Equation (3). \[APAAC=AAC+\lambda\times Amphiphilic^{Pseudo}_{Components}, \tag{3}\]  
where \(Amphiphilic^{Pseudo}_{Components}\) are the additional features capturing the amphiphilic properties. **PseKRAAC**  
Pseudo K-tuple reduced amino acid composition (PseKRAAC) is a powerful bioinformatics tool designed to generate pseudo K-tuple reduced amino acid compositions from protein sequences [121]. This method simplifies protein complexity by reducing the amino acid alphabet, which helps in identifying functional conserved regions and reduces the risk of overfitting, computational burden, and information redundancy.