The identification and categorization of homologous proteins are fundamental tasks in the field of biology, relying on tools that analyze nucleotide oramino acid sequences. However, automated detection of evolutionary patternsand additional attributes using traditional methods still presents research challenges. In this study, we propose a novel layered data representation approachthat allows us to explore evolutionary patterns and other sequence features insimilarity searching, classification, and clustering. It employs an alignment-freeprocess, and we introduce new similarity algorithms to enhance the effectiveness of this approach. These algorithms leverage techniques inspired by humanperception to capture subtle similarities within biological molecules representations. Experimental evaluations demonstrate good performance and high accuracy compared to previously proposed approaches. This layered representationshows promise in identifying similar proteins, especially with distant homologscharacteristics. Furthermore, it also suggests the development of new methods and machine learning (ML) algorithms in bioinformatics that address theprivacy and security of biological data.