Biology subjects

Leidner, F.

Publications and source records attributed to Leidner, F..

6 recordsLinked to original sources

bioRxiv2025

UBE2J2 sensitizes the ERAD ubiquitination cascade to changes in membrane lipid saturation

Protein-lipid crosstalk is fundamental to homeostasis in the endoplasmic reticulum (ER). The ER-associated degradation (ERAD) pathway, a branch of the ubiquitin-proteasome system, maintains ER membrane properties by degrading lipid metabolic enzymes. However, the ERAD components that sense membrane properties and their mechanisms remain poorly defined. Using reconstituted systems with purified ERAD factors, we show that membrane composition modulates the ubiquitination cascade at multiple levels. The membrane-anchored E2 UBE2J2 acts as a sensor for lipid packing: in loosely packed membranes, UBE2J2 becomes inactive due to membrane association that impedes ubiquitin loading, while tighter packing promotes its active conformation and interaction with E1. UBE2J2 activity directs ubiquitin transfer by the E3 ligases RNF145, MARCHF6, and RNF139, targeting both themselves and the substrate squalene monooxygenase. Additionally, RNF145 senses cholesterol, altering its oligomerization and activity. These findings reveal that ERAD integrates multiple lipid signals, with UBE2J2 relaying and extending the effect of lipid signals through its cooperation with multiple E3 ligases.

biochemistry↗

bioRxiv2025

Structure and function of otoferlin, a synaptic protein of sensory hair cells essential for hearing

Our sense of hearing relies upon speedy synaptic transmission of sound information from cochlear inner hair cells (IHCs) to spiral ganglion neurons (SGNs). To accomplish this, IHCs employ a sophisticated presynaptic machinery including the multi-C2-domain protein otoferlin which is affected by human deafness mutations. Otoferlin is essential for IHC-exocytosis but how it binds Ca2+ and the target membrane to serve synaptic vesicle (SV) tethering, docking and fusion remained unclear. Here, we obtained cryo-electron-microscopy structures of Ca2+-bound otoferlin and employed molecular dynamics simulations of membrane binding. We show that membrane binding involves C2B-C2G-domains and repositions C2F- and C2G-domains. Progressive disruption of Ca2+-binding by the C2D-domain in mice increasingly altered synaptic sound encoding and eliminated the Ca2+-cooperativity of SV-exocytosis, indicating that this Ca2+-cooperativity reflects binding of several Ca2+-ions to otoferlin. Together, our findings elucidate molecular mechanisms underlying otoferlin-mediated SV-docking and support a role of otoferlin as Ca2+-sensor of SV-fusion in IHCs.

neuroscience↗

bioRxiv2020

Deciphering complex mechanisms of resistance and loss of potency through coupled molecular dynamics and machine learning.

Drug resistance threatens many critical therapeutics through mutations in the drug target. The molecular mechanisms by which combinations of mutations, especially involving those distal from the active site, alter drug binding to confer resistance are poorly understood and thus difficult to counteract. A machine learning strategy was developed that couples parallel molecular dynamics simulations and experimental potency to identify specific conserved mechanisms underlying resistance. A series of 28 HIV-1 protease variants with 0-24 substitutions each were used as a rigorous model of this strategy. Many of the mutations were distal from the active site and the potency of variants to a drug (darunavir) varied from low pM to near {micro}M. With features extracted from the simulations, elastic network machine learning was applied to correlate physical interactions with loss of potency and succeeded to within 1 kcal/mol of experimental affinity for both the training and test sets, outperforming MM/GBSA calculations. Feature reduction resulted in a model with 4 specific features that describe interactions critical for potency for all 28 variants. These predictive features, that specifically vary with potency, occur throughout the enzyme and would not have been identified without dynamics and machine learning. This strategy thus captures the conserved dynamic mechanisms by which complex combinations of mutations confer resistance and identifies critical features that serve as bellwethers of loss of inhibitor potency. Machine learning models leveraging molecular dynamics can thus elucidate mechanisms of drug resistance that confer loss of affinity and will serve as predictive tools in future drug design.

biophysics↗

bioRxiv2019

Molecular and structural mechanism of pan-genotypic HCV NS3/4A protease inhibition by glecaprevir

Hepatitis C virus (HCV), causative agent of chronic viral hepatitis, infects 71 million people worldwide and is divided into seven genotypes and multiple subtypes with sequence identities between 68 to 82%. While older generation direct-acting antivirals (DAAs) had varying effectiveness against different genotypes, the newest NS3/4A protease inhibitors including glecaprevir (GLE) have pan-genotypic activity. The structural basis for pan-genotypic inhibition and effects of polymorphisms on inhibitor potency were not well known due to lack of crystal structures of GLE-bound NS3/4A or genotypes other than 1. In this study, we determined the crystal structures of NS3/4A from genotypes 1a, 3a, 4a and 5a in complex with GLE. Comparison with the highly similar grazoprevir (GZR) indicated the mechanism of GLEs drastic improvement in potency. We found that while GLE is highly potent against wild type NS3/4A of all genotypes, specific resistance-associated substitutions (RASs) confer orders of magnitude loss in inhibition. Our crystal structures reveal molecular mechanisms behind pan-genotypic activity of GLE, including potency loss due to RASs at D168. Our structures permit for the first time analysis of changes due to polymorphisms among genotypes, providing insights into design principles that can aid future drug development and potentially can be extended to other proteins.

biochemistry↗

bioRxiv2018

Structural Adaptation of Darunavir Analogs Against Primary Resistance Mutations in HIV-1 Protease

HIV-1 protease is one of the prime targets of agents used in antiretroviral therapy against HIV. However, under selective pressure of protease inhibitors, primary mutations at the active site weaken inhibitor binding to confer resistance. Darunavir (DRV) is the most potent HIV-1 protease inhibitor in clinic; resistance is limited, as DRV fits well within the substrate envelope. Nevertheless, resistance is observed due to hydrophobic changes at residues including I50, V82 and I84 that line the S1/S1 pocket within the active site. Through enzyme inhibition assays and a series of 12 crystal structures, we interrogated susceptibility of DRV and two potent analogs to primary S1 mutations. The analogs had modifications at the hydrophobic P1 moiety to better occupy the unexploited space in the S1 pocket where the primary mutations were located. Considerable losses of potency were observed against protease variants with I84V and I50V mutations for all three inhibitors. The crystal structures revealed an unexpected conformational change in the flap region of I50V protease bound to the analog with the largest P1 moiety, indicating interdependency between the S1 subsite and the flap region. Collective analysis of protease-inhibitor van der Waals (vdW) interactions in the crystal structures using principle component analysis indicated I84V mutation underlying the largest variation in the vdW contacts. Interestingly, the principle components were able to distinguish inhibitor identity and relative potency solely based on vdW interactions of active site residues in the crystal structures. Our results reveal the interplay between inhibitor P1 moiety and primary S1 mutations, as well as suggesting a novel method for distinguishing the interdependence of resistance through principle component analyses.

molecular biology↗

bioRxiv2018

Structural analysis of the active site and DNA binding of human cytidine deaminase APOBEC3B

APOBEC3s proteins (A3s), a family of human cytidine deaminases, protect the host cell from endogenous retro-elements and exogenous viral infections by introducing hypermutations. However, the ability to mutate genomic DNA makes A3s a potential cancer source. Of the 7 human A3s, A3B has been implicated as an endogenous cause for multiple cancers. Despite overall similarity, A3s have distinct deamination activity with A3B among the least catalytically active. Over the past few years, several structures of apo as well as DNA-bound A3 proteins have been determined. These structures revealed the molecular determinants of nucleotide specificity and the importance of the loops around the active site in DNA binding. However, for A3B, the structural basis for regulation of deamination activity and the role of active site loops in coordinating DNA had remained unknown. In this study, using a combination of advanced molecular modelling followed by experimental mutational analysis and dynamics simulations, we investigated molecular mechanism of A3B regulating activity and DNA binding. We identified a unique auto-inhibited conformation of A3B that restricts access and binding of DNA to the active site, mainly due to the extra PLV residues in loop 1. We modelled DNA binding to fully native A3B and found that Arg211 in the arginine patch of loop1 is the gatekeeper while Arg212 stabilizes the bound DNA. This model also identified the critical residues for substrate specificity, especially at the -1 position. Our results reveal the structural basis for relatively lower catalytic activity of A3B and provide opportunities for rational design of inhibitors that specifically target A3B to benefit cancer therapeutics.

biochemistry↗