Biology subjects

Menezes, F.

Publications and source records attributed to Menezes, F..

3 recordsLinked to original sources

bioRxiv2023

A novel PEX14/PEX5 interface links peroxisomal protein import and receptor recycling

Newly synthesized peroxisomal proteins are recognized in the cytosol by the cycling receptor PEX5 and directed to a docking complex comprising PEX14 and PEX13 at the peroxisomal membrane. After cargo translocation, the unloaded PEX5 is recycled in an ATP-dependent manner. Receptor docking involves the WxxxF-motifs in the N-terminal domain (NTD) of PEX5 that are recognized by the N-terminal domain of PEX14. Here, we combine biochemical methods and NMR spectroscopy to identify a novel binding interface between human PEX5 and PEX14. The interaction involves the PEX5 C-terminal cargo-binding TPR domain and a conserved IPSWQI peptide motif in the C-terminal intrinsically disordered region of PEX14. The three-dimensional structure of the PEX14 IPSWQI peptide bound the PEX5 TPR domain, shows the PEX14 interaction is non-overlapping with PTS1 binding to the TPR domain. Notably, PEX14 IPSWQI motif binding to a hinge region in the TPR domain shows a more open supercoil of the TPR fold that resembles the apo conformation in the absence of PTS1 peptide. Mutation of binding site residues in PEX5 or PEX14 leads to a partial protein import defect and decrease of the steady-state-concentration of PEX5. This resembles the mutant phenotype of cells affected in receptor recycling, suggesting a role in this process.

biochemistry↗

bioRxiv2023

Can Quantum Chemistry Improve the understanding of Protein-Ligand Interactions? Implications for Structure Based Drug Discovery

We introduce an Energy Decomposition Analysis suitable for understanding the nature of non-covalent binding in large chemical systems, like those of drug-protein complexes. The method is atom specific, thus allowing rationalization of the role that each atom or functional group plays for the interaction. Visual representations are constructed in the form of interaction maps, depicting the different contributions for electrostatics, polarization, dispersion (lipophilicity), etc. This marks the departure from atomistic models towards electronic interaction ones, that better correlate with experimental data. The maps provide a quick access to the driving forces behind the formation of intermolecular complexes, and the key contributors for each interaction. This allows constructing quantum mechanical models of binding. The presented method is validated against experimental binding data for the difficult to target protein-protein interface for PEX14-PEX5 and its inhibitors.

bioinformatics↗

bioRxiv2023

MISATO - Machine learning dataset for structure-based drug discovery

Large language models (LLMs) have greatly enhanced our ability to understand biology and chemistry. Yet, relatively few robust methods have been reported for structure-based drug discovery. Highly precise biomolecule-ligand interaction datasets are urgently needed in particular for LLMs, that require extensive training data. We present MISATO, the first dataset that combines quantum mechanics properties of small molecules and associated molecular dynamics simulations of about 20000 experimental protein-ligand complexes. Starting from the PDBbind dataset, semi-empirical quantum mechanics was used to systematically refine these structures. The largest collection to date of molecular dynamics traces of protein-ligand complexes in explicit water are included, accumulating to 170 s. We give ML baseline models and simple Python data loaders, and aim to foster a thriving community around MISATO (https://github.com/t7morgen/misato-dataset). An easy entry point for ML experts is provided without the need of deep domain expertise to enable the next generation of drug discovery AI models.

bioinformatics↗