DANCE: Deep Learning-Assisted Analysis of ProteiN Sequences Using Chaos Enhanced Kaleidoscopic Images

Cancer is a complex disease characterized by uncontrolled cell growth and requires an accurate classification for effective treatment. T cell receptors (TCRs), crucial proteins in the immune system, play a pivotal role in antigen recognition. Advancements in sequencing technologies have facilitated the comprehensive profiling of TCR repertoires, uncovering TCRs with potent anticancer activity and enabling TCR-based immunotherapies. Performing an effective analysis of these complex biomolecules requires representations that accurately capture both their structural and functional characteristics. T cell protein sequences pose unique challenges because of their relatively shorter lengths compared to other biomolecules. Traditional vector-based embedding methods may encounter issues such as information loss. Therefore, an image-based representation approach becomes a preferred choice for efficient embedding, allowing the preservation of essential details and enabling a comprehensive analysis of T cell protein sequences. We propose generating images from protein sequences using the concept of chaos game representation (CGR). We design images using the kaleidoscopic images approach. This Deep Learning-Assisted Analysis of ProteiN Sequences Using Chaos Enhanced Kaleidoscopic Images (called DANCE) provides a unique way to visualize protein sequences by recursively applying chaos game rules around a central seed point. The resulting kaleidoscopic images exhibit symmetrical patterns that offer a visual representation of the protein sequences. To investigate the effectiveness of this approach, we perform classification of the TCR protein sequences in terms of their respective target cancer cells, since TCRs are known for their immune response against cancer disease. The DANCE technique is used to turn the TCR sequences into pictures before classification. We employ deep learning (DL) vision models to classify the generated images to obtain insight into the relationship between the visual patterns in the generated kaleidoscopic images and the underlying protein properties. By combining CGR-based image generation with DL classification, this study opens new possibilities in protein analysis.