Biology subjects

Azinas, S.

Publications and source records attributed to Azinas, S..

3 recordsLinked to original sources

bioRxiv2025

Structure of the central Staphylococcus aureus AAA+ protease MecA/ClpC/ClpP

Bacterial AAA+ proteases are composed of a AAA+ partner (e.g. ClpC) and an associated peptidase (e.g. ClpP). They represent ATP-fuelled and self-compartmentalized proteolytic machines that are crucial for stress resistance and virulence. ClpC requires cooperation with adaptor proteins such as MecA for activation and complex formation with ClpP. Here, we present the cryo-EM structure of the MecA/ClpC/ClpP complex from the major pathogen Staphylococcus aureus. MecA forms a dynamic crown on top of the ClpC/ClpP complex with its substrate-binding domain positioned near the ClpC pore site, likely facilitating substrate transfer. ClpC/ClpP complex formation involves ClpC P-loops and ClpP N-terminal {beta}-hairpins, which insert into the central ClpC threading channel and contact sites next to the ClpC ATPase center. ClpC and ClpP interactions are asymmetric and dictated by the activity states of ClpC ATPase subunits. ClpP binding increases ClpC ATPase and threading activities in a {beta}-hairpin dependent manner, illuminating an allosteric pathway in the cooperation of ATPase and peptidase components in bacterial AAA+ proteases.

biochemistry↗

bioRxiv2023

Unmasking AlphaFold: integration of experiments and predictions with a smarter template mechanism

Since the release of AlphaFold, researchers have actively refined its predictions and attempted to integrate it into existing pipelines for determining protein structures. These efforts have introduced a number of functionalities and results at the latest Critical Assessment of protein Structure Prediction edition (CASP15), resulting in a marked improvement in the prediction of multimeric protein structures. However, AlphaFolds capability of predicting large protein complexes is still limited and integrating experimental data in the prediction pipeline is not straightforward. In this study, we introduce AF_unmasked to overcome these limitations. Our results demonstrate that AF_unmasked can integrate experimental information to build larger or hard to predict protein assemblies with high confidence. The resulting predictions can help interpret and augment experimental data. This new approach generates near-perfect structures even when little to no evolutionary information is available and imperfect experimental structures are used as a starting point. AF_unmasked fills incomplete structures by a procedure called"structural inpainting", which may provide insights into protein dynamics. In summary, AF_unmasked provides an easy-to-use method that efficiently integrates experiments to predict large protein complexes more confidently. Codegithub.com/clami66/AF_unmasked

bioinformatics↗

bioRxiv2020

Oncometabolism Drives Autophagy Activation in Skeletal Muscle

About 20-30% of cancer-associated deaths are due to complications from cachexia which is characterized by skeletal muscle atrophy. Metabolic reprogramming in cancer cells causes body-wide metabolic and proteomic remodeling, which remain poorly understood. Here, we present evidence that the oncometabolite D-2-hydroxylgutarate (D2-HG) impairs NAD+ redox homeostasis in skeletal myotubes, causing atrophy via deacetylation of LC3-II by the nuclear deacetylase Sirt1. Overexpression of p300 or silencing of Sirt1 abrogate its interaction with LC3, and subsequently reduced levels of LC3 lipidation. Using RNA-sequencing and mass spectrometry-based metabolomics and proteomics, we demonstrate that prolonged treatment with the oncometabolite D2-HG in mice promotes cachexia in vivo and increases the abundance of proteins and metabolites, which are involved in energy substrate metabolism, chromatin acetylation and autophagy regulation. We further show that D2-HG promotes a sex-dependent adaptation in skeletal muscle using network modeling and machine learning algorithms. Our multi-omics approach exposes new metabolic vulnerabilities in response to D2-HG in skeletal muscle and provides a conceptual framework for identifying therapeutic targets in cachexia.

systems biology↗