<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Kong D</submitter><funding>NIA NIH HHS</funding><funding>NIDA NIH HHS</funding><funding>NIDDK NIH HHS</funding><funding>NINDS NIH HHS</funding><pagination>103</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11751189</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>8(1)</volume><pubmed_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, ubiq</pubmed_abstract><journal>Communications biology</journal><pubmed_title>A computational tool to infer enzyme activity using post-translational modification profiling data.</pubmed_title><pmcid>PMC11751189</pmcid><funding_grant_id>RF1 AG064909</funding_grant_id><funding_grant_id>RF1 AG072703</funding_grant_id><funding_grant_id>R01 DA056523</funding_grant_id><funding_grant_id>RF1 AG068581</funding_grant_id><funding_grant_id>R01 DK130913</funding_grant_id><funding_grant_id>R01 NS123456</funding_grant_id><pubmed_authors>Wu L</pubmed_authors><pubmed_authors>Mishra A</pubmed_authors><pubmed_authors>High AA</pubmed_authors><pubmed_authors>Yuan ZF</pubmed_authors><pubmed_authors>Fu Y</pubmed_authors><pubmed_authors>Kong D</pubmed_authors><pubmed_authors>Wang X</pubmed_authors><pubmed_authors>Li L</pubmed_authors><pubmed_authors>Zhang A</pubmed_authors><pubmed_authors>Peng J</pubmed_authors></additional><is_claimable>false</is_claimable><name>A computational tool to infer enzyme activity using post-translational modification profiling data.</name><description>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, ubiq</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Jan</publication><modification>2025-04-22T05:26:59.383Z</modification><creation>2025-04-05T21:20:17.952Z</creation></dates><accession>S-EPMC11751189</accession><cross_references><pubmed>39838083</pubmed><doi>10.1038/s42003-025-07548-4</doi></cross_references></HashMap>