Machine learning-based exploration of enzyme-substrate networks: SET8-mediated methyllysine and its changing impact within cancer proteomes

Abstract

Abstract The exploration of post-translational modifications (PTMs) within the proteome is pivotal for advancing disease and cancer therapeutics. However, identifying genuine PTM sites amid numerous candidates is challenging. Integrating machine learning (ML) models with high-throughput in vitro peptide synthesis has introduced an ML-hybrid search methodology, enhancing enzyme-substrate selection prediction. In this study we have developed a ML-hybrid search methodology to better predict enzyme-substrate selection. This model achieved a 37.4% experimentally validated precision, unveiling 885 SET8 candidate methylation sites in the human proteome—marking a 19-fold accuracy increase over traditional in vitro methods. Mass spectrometry analysis confirmed the methylation status of several sites, responding positively to SET8 overexpression in mammalian cells. This approach to substrate discovery has also shed light on the changing SET8-regulated substrate network in breast cancer, revealing a predicted gain (376) and loss (62) of substrates due to missense mutations. By unraveling enzyme selection features, this approach offers transformative potential, revolutionizing enzyme-substrate discovery across diverse PTMs while capturing crucial biochemical substrate properties.