1. Academic Validation
  2. AIG1 and ADTRP are atypical integral membrane hydrolases that degrade bioactive FAHFAs

AIG1 and ADTRP are atypical integral membrane hydrolases that degrade bioactive FAHFAs

  • Nat Chem Biol. 2016 May;12(5):367-372. doi: 10.1038/nchembio.2051.
William H Parsons # 1 Matthew J Kolar # 2 Siddhesh S Kamat 1 Armand B Cognetta 3rd 1 Jonathan J Hulce 1 Enrique Saez 1 Barbara B Kahn 3 Alan Saghatelian 2 Benjamin F Cravatt 1
Affiliations

Affiliations

  • 1 The Skaggs Institute for Chemical Biology, Department of Chemical Physiology, The Scripps Research Institute, La Jolla, CA 92037.
  • 2 Salk Institute for Biological Studies, Clayton Foundation Laboratories for Peptide Biology, Helmsley Center for Genomic Medicine, La Jolla, California 92037, United States.
  • 3 Division of Endocrinology, Diabetes & Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts 02215, United States.
  • # Contributed equally.
Abstract

Enzyme classes may contain outlier members that share mechanistic, but not sequence or structural, relatedness with more common representatives. The functional annotation of such exceptional proteins can be challenging. Here, we use activity-based profiling to discover that the poorly characterized multipass transmembrane proteins AIG1 and ADTRP are atypical hydrolytic Enzymes that depend on conserved threonine and histidine residues for catalysis. Both AIG1 and ADTRP hydrolyze bioactive fatty acid esters of hydroxy fatty acids (FAHFAs) but not other major classes of lipids. We identify multiple cell-active, covalent inhibitors of AIG1 and show that these agents block FAHFA hydrolysis in mammalian cells. These results indicate that AIG1 and ADTRP are founding members of an evolutionarily conserved class of transmembrane threonine hydrolases involved in bioactive lipid metabolism. More generally, our findings demonstrate how chemical proteomics can excavate potential cases of convergent or parallel protein evolution that defy conventional sequence- and structure-based predictions.

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