bioRxivBiophysicsPreprint
Unraveling the Molecular Mechanisms of ABHD5 Membrane Targeting
Amit KumariD, Matthew A. SandersiD, Huamei ZhangiD, Li Zhou, Shahnaz ParveeniD, Miriam C. Jensen, Thomas J.D. Jorgensen, Christopher V. KellyiD, James G. GrannemaniD, Yu‐ming M. HuangiD
Abstract
ABHD5 is a master regulator of PNPLA family lipases, particularly PNPLA2 (ATGL), the rate-limiting triglyceride (TAG) hydrolase in metabolic tissues. Despite its central role in lipid metabolism, the molecular basis by which ABHD5 recognizes membranes and regulates enzymatic activity remains poorly understood. Here, we report an integrated computational-experimental study revealing how the /{beta}-hydrolase domain-containing protein 5 (ABHD5) dynamically engages lipid droplet (LD) and endoplasmic reticulum membranes to control lipolytic activation. Using multiscale molecular dynamics simulations, hydrogen-deuterium exchange mass spectrometry, and site-directed mutagenesis, we uncover a sequential dual-site membrane recognition mechanism, where the N-terminus provides initial anchoring and a lid helix within the insertion segment forms a crucial secondary contact. Membrane binding triggers a dramatic conformational switch in this lid, expanding the pseudosubstrate pocket and transforming ABHD5 into an active and membrane-localized regulator. This structural transition is coupled to membrane remodeling, inducing localized curvature and forming a triacylglycerol-enriched nanodomain beneath the ABHD5 pseudosubstrate pocket. This bidirectional interaction between ABHD5 and the membrane provides a persuasive mechanism for interfacial activation. Our findings establish new principles for how LD-binding proteins achieve functional specificity through membrane-dependent regulation, offering novel molecular targets for interventions in metabolic diseases. TeaserA key protein controls fat breakdown by changing shape when it binds membranes and reshapes local fat droplets.
- organism: human
- assay: proteomics
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
- Citations
- 4
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