## 3 Feature Encoding Methods
### Physicochemical-Based Methods
Each row in the matrix corresponds to a position in the aligned sequences, and each column corresponds to one of the 20 standard amino acids. The entries in the matrix represent the frequency of each amino acid at each position in the alignment. The frequency matrix is then converted into a scoring matrix. This involves calculating the log-odds score for each amino acid at each position. The log-odds score is computed as the logarithm of the ratio of the observed frequency of an amino acid at a position to the expected frequency of that amino acid in a background model (usually the frequency of amino acids in a large database of proteins). A PSSM profile is typically represented as a matrix with dimensions L \(\times\) 20, where L is the length of the sequence and 20 corresponds to the 20 standard amino acids. Each entry P(i,a) in the matrix represents the log-odds score for amino acid a at position i in the sequence. **AAindex**  
The AAindex database is a comprehensive collection of numerical indices representing various physicochemical and biochemical properties of amino acids and pairs of amino acids [130; 131; 132]. It is widely used in bioinformatics and computational biology to analyse and predict the properties of proteins and peptides. The database consists of three main sections, including AAindex1, AAindex2, and AAindex3. The AAindex1 section contains indices representing the properties of individual amino acids. Each index is a set of 20 numerical values, one for each of the 20 standard amino acids. The AAindex2 section includes amino acid substitution matrices, which are used to represent the likelihood of one amino acid being replaced by another during evolution. The AAindex3 section contains statistical protein contact potentials, which describe the interactions between pairs of amino acids in protein structures. The indices of the AAindex can be used to predict the secondary and tertiary structures of proteins by providing insights into the physicochemical properties of amino acids. By analysing the properties of amino acids in a protein sequence, the AAindex can help predict the function of the protein. The AAindex is often used as a feature set in machine learning models to predict various properties of proteins, such as enzyme activity, binding affinity, and stability. The AAindex database can be accessed through the DBGET/LinkDB system at GenomeNet [133]. It can also be downloaded via anonymous FTP from the GenomeNet server. Additionally, there are software packages available for working with AAindex data, such as the Aaindex Python package. **BLOSUM62**  
The BLOSUM62 matrix is a widely used substitution matrix in bioinformatics for protein sequence alignment [134]. It is part of the BLOSUM (blocks substitution matrix) family, which was developed to improve the accuracy of protein sequence alignments by capturing evolutionary trends in amino acid substitutions. BLOSUM matrices are derived from conserved sequence blocks in protein families, specifically from the BLOCKS database. Clustering threshold: BLOSUM62 is constructed from alignments where no two sequences share more than 62% identity. This prevents biases from closely related sequences. Substitutions between amino acids are counted within these conserved blocks. The substitution frequencies are converted into log-odds scores, which reflect the likelihood of a substitution occurring relative to random chance. Positive scores indicate frequent substitutions, often conservative replacements that preserve biochemical properties. Negative scores reflect rare or unfavourable substitutions that could disrupt protein structure and function. BLOSUM62 balances sensitivity and specificity in detecting homologous sequences, making it effective for a wide range of evolutionary distances. Accessible solvent accessibility (ASA), also known as the solvent-accessible surface area, is a measure of the surface area of a molecule that is accessible to a solvent, typically water [135].