Beyond Sequence: Advanced Homology Detection for Cancer Protein using MS_HITAMPE

A key challenge in bioinformatics continues to be the detection of remote homologies between cancer proteins, which has important implications for the understanding of disease mechanisms and development of targeted therapies. Using MS_HITAMPE (Multi-Scale Homology Identification Technique with Alignment and Matching for Protein Evolution), this study identifies distant evolutionary relationships among cancer-related proteins that surpass traditional sequence-based methods. In this research, MS_HITAMPE was developed, which combines local and global sequence features to detect subtle evolutionary signals that conventional approaches miss. Detection of remote homologs of cancer proteins has been made more sensitive by MS_HITAMPE, which detects remote homologs with sequence similarities as low as 20%, which is a significant improvement over existing method. The results of a rigorous performance evaluation demonstrate MS_HITAMPE’s enhanced accuracy (an improvement of 15%), decreased time complexity (30% faster), and greater search similarity ratio (25%). Analysing 10,000 cancer-associated proteins from the Protein Data Bank (PDB) revealed 500 previously unknown homologous relationships, providing new insight into oncogenic protein evolution. Using MS_HITAMPE to predict the functions of uncharacterized cancer proteins, with 85% validation accuracy. In this work, researchers are given a powerful tool for analysing the complex landscape of cancer proteomics, representing a significant advance in computational biology. As a result of the discovery of hidden evolutionary connections, MS_HITAMPE can be used to uncover new hypotheses about cancer biology and potential therapeutic targets. In addition to cancer research, the method may enhance knowledge of protein evolution and function across a variety of biological fields.