A computational tool to infer enzyme activity using post-translational modification profiling data - Dehui Kong; Aijun Zhang; Ling Li; Zuo-Fei Yuan; Yingxue Fu; Long Wu; Ashutosh Mishra; Anthony A. High; Junmin Peng; Xusheng Wang (Communications Biology)
Abstract Enzymes play a pivotal role in orchestrating complex cellular responses to external stimuli and environmental changes through signal transduction pathways. Despite their crucial roles, measuring enzyme activities is typically indirect and performed on a smaller scale, unlike protein abundance measured by high-throughput proteomics. Moreover, it is challenging to derive the activity of enzymes from proteome-wide post-translational modification (PTM) profiling data. To address this challenge, we introduce enzyme activity inference with structural equation modeling under the JUMP umbrella (JUMPsem), a novel computational tool designed to infer enzyme activity using PTM profiling data. We demonstrate that the JUMPsem program enables estimating kinase activities using phosphoproteome data, ubiquitin E3 ligase activities from the ubiquitinome, and histone acetyltransferase (HAT) activities based on the acetylome. In addition, JUMPsem is capable of establishing novel enzyme-substrate relationships through searching motif sequences. JUMPsem outperforms widely used kinase activity tools, such as IKAP and KSEA, in terms of the number of kinases and the computational speed. The JUMPsem program is scalable and publicly available as an open-source R package and user-friendly web-based R/Shiny app. Collectively, JUMPsem provides an improved tool for inferring protein enzyme activities, potentially facilitating targeted drug development.