Developing effective protein sequence comparison methods is challenging due to the need to efficiently handle large and diverse data sets. In this work, protein sequences are numerically represented by considering their constituent amino acids’ molecular weight (physical) and hydropathy index (chemical). Sequence lengths are equalized by appending zeros, followed by employing the Fast Fourier transform (FFT) to generate spectra. Spectral data are reduced through the Inter Coefficient Difference approach, with normalized components extracted as descriptors. Euclidean distance matrices derived from these descriptors are used to construct phylogenetic trees via the UPGMA algorithm. Comparisons of ND6 and 50 Beta Globin proteins show that the hydropathy index outperforms molecular weight for sequence comparison. Phylogenetic trees for the 50 Coronavirus Spike proteins and 19 NADH proteins are also constructed using the hydropathy index. Results are evaluated using quality perception, symmetric distance, and computational time, showing that the proposed approach is more effective and satisfactory than existing methods.