Cancer is a complex disease characterized by uncontrolled cell growth. T cell receptors (TCRs), crucial proteins in the immune system, play a key role in recognizing antigens, including those associated with cancer. Recent advancements in sequencing technologies have facilitated comprehensive profiling of TCR repertoires, uncovering TCRs with potent anti-cancer activity and enabling TCR-based immunotherapies. However, analyzing these intricate biomolecules necessitates efficient representations that capture their structural and functional information. T-cell protein sequences pose unique challenges due to their relatively smaller lengths compared to other biomolecules. An image-based representation approach becomes a preferred choice for efficient embeddings, allowing for the preservation of essential details and enabling comprehensive analysis of T-cell protein sequences. In this paper, we propose to generate images from the protein sequences using the idea of Chaos Game Representation (CGR) 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. we perform the classification of the T cell receptors (TCRs) protein sequences in terms of their respective target cancer cells, as TCRs are known for their immune response against cancer disease. The TCR sequences are converted into images using the DANCE method. We employ deep-learning vision models to perform the classification to obtain insights into the relationship between the visual patterns observed in the generated kaleidoscopic images and the underlying protein properties. By combining CGR-based image generation with deep learning classification, this study opens novel possibilities in the protein analysis domain.

翻译：癌症是一种以细胞不受控制生长为特征的复杂疾病。T细胞受体（TCRs）作为免疫系统中的关键蛋白质，在识别抗原（包括与癌症相关的抗原）方面发挥着核心作用。测序技术的最新进展促进了TCR受体库的全面分析，从而发现了具有强效抗癌活性的TCR，并推动了基于TCR的免疫疗法的发展。然而，分析这些复杂的生物分子需要能够捕捉其结构和功能信息的高效表征方法。与其他生物分子相比，T细胞蛋白质序列长度相对较短，这带来了独特的挑战。基于图像的表征方法因其能有效保留关键细节并支持对T细胞蛋白质序列的全面分析，成为高效嵌入表征的首选方案。本文提出利用混沌博弈表示（CGR）思想，通过万花筒图像方法从蛋白质序列生成图像。这种基于混沌增强万花筒图像的蛋白质序列深度学习辅助分析方法（称为DANCE），通过围绕中心种子点递归应用混沌博弈规则，为蛋白质序列可视化提供了一种独特途径。我们针对T细胞受体（TCRs）蛋白质序列按其靶向的特定癌细胞类型进行分类，因为TCRs以其对癌症疾病的免疫应答而闻名。首先使用DANCE方法将TCR序列转换为图像，随后采用深度学习视觉模型进行分类，以探究生成的万花筒图像中观察到的视觉模式与潜在蛋白质特性之间的关联。通过将基于CGR的图像生成与深度学习分类相结合，本研究为蛋白质分析领域开辟了新的可能性。