Machine learning-driven prediction of substrates for enzymes introducing or removing protein post-translational modifications.

The exploration of post-translational modifications (PTMs) within the proteome is pivotal for advancing our understanding of disease and the function of cancer therapeutics. However, identifying genuine sites of PTMs introduced or removed by an enzyme of interest amid numerous candidates is challenging. We present a machine learning (ML)-driven search method, which combines ML with enzyme-mediated modification of complex peptide arrays to predict unexplored PTM sites for an enzyme of interest. Experimental validation confirmed that this approach correctly predicted 37-43% of proposed PTM sites, unveiling candidate sites of the methyltransferase SET8 and the deacetylases SIRT1-7. Our approach marks an important performance increase over traditional in vitro methods across separate enzyme classes. Mass spectrometry analysis confirmed the dynamic methylation status of several predicted SET8 substrates, and the deacetylation of 64 unique sites identified for SIRT2. This method has also revealed changes in SET8-regulated substrate network among breast cancer missense mutations, collectively revealing insight into differential enzyme function in disease. By disentangling the substrate features that dictate PTM-inducing enzyme specificity, this approach demonstrates potential in uncovering enzyme-substrate networks within PTM pathways.