Despite the odds: formation of the SARS-CoV-2 methylation complex.

Coronaviruses modify their single-stranded RNA genome with a methylated cap during replication to mimic the eukaryotic mRNAs. The capping process is initiated by several nonstructural proteins (nsp) encoded in the viral genome. The methylation is performed by two methyltransferases, nsp14 and nsp16, while nsp10 acts as a co-factor to both. Additionally, nsp14 carries an exonuclease domain which operates in the proofreading system during RNA replication of the viral genome. Both nsp14 and nsp16 were reported to independently bind nsp10, but the available structural information suggests that the concomitant interaction between these three proteins would be impossible due to steric clashes. Here, we show that nsp14, nsp10, and nsp16 can form a heterotrimer complex upon significant allosteric change. This interaction is expected to encourage the formation of mature capped viral mRNA, modulating nsp14's exonuclease activity, and protecting the viral RNA. Our findings show that nsp14 is amenable to allosteric regulation and may serve as a novel target for therapeutic approaches.

Modulation of peroxisomal import by the PEX13 SH3 domain and a proximal FxxxF binding motif.

Import of proteins into peroxisomes depends on PEX5, PEX13 and PEX14. By combining biochemical methods and structural biology, we show that the C-terminal SH3 domain of PEX13 mediates intramolecular interactions with a proximal FxxxF motif. The SH3 domain also binds WxxxF peptide motifs in the import receptor PEX5, demonstrating evolutionary conservation of such interactions from yeast to human. Strikingly, intramolecular interaction of the PEX13 FxxxF motif regulates binding of PEX5 WxxxF/Y motifs to the PEX13 SH3 domain. Crystal structures reveal how FxxxF and WxxxF/Y motifs are recognized by a non-canonical surface on the SH3 domain. The PEX13 FxxxF motif also mediates binding to PEX14. Surprisingly, the potential PxxP binding surface of the SH3 domain does not recognize PEX14 PxxP motifs, distinct from its yeast ortholog. Our data show that the dynamic network of PEX13 interactions with PEX5 and PEX14, mediated by diaromatic peptide motifs, modulates peroxisomal matrix import.

SARS-CoV-2 Mpro oligomerization as a potential target for therapy.

The main protease (Mpro) of SARS-CoV-2 is critical in the virus's replication cycle, facilitating the maturation of polyproteins into functional units. Due to its conservation across taxa, Mpro is a promising target for broad-spectrum antiviral drugs. Targeting Mpro with small molecule inhibitors, such as nirmatrelvir combined with ritonavir (Paxlovid™), which the FDA has approved for post-exposure treatment and prophylaxis, can effectively interrupt the replication process of the virus. A key aspect of Mpro's function is its ability to form a functional dimer. However, the mechanics of dimerization and its influence on proteolytic activity remain less understood. In this study, we utilized biochemical, structural, and molecular modelling approaches to explore Mpro dimerization. We evaluated critical residues, specifically Arg4 and Arg298, that are essential for dimerization. Our results show that changes in the oligomerization state of Mpro directly affect its enzymatic activity and dimerization propensity. We discovered a synergistic relationship influencing dimer formation, involving both intra- and intermolecular interactions. These findings highlight the potential for developing allosteric inhibitors targeting Mpro, offering promising new directions for therapeutic strategies.

MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery.

Large language models have greatly enhanced our ability to understand biology and chemistry, yet robust methods for structure-based drug discovery, quantum chemistry and structural biology are still sparse. Precise biomolecule-ligand interaction datasets are urgently needed for large language models. To address this, we present MISATO, a dataset that combines quantum mechanical properties of small molecules and associated molecular dynamics simulations of ~20,000 experimental protein-ligand complexes with extensive validation of experimental data. Starting from the existing experimental structures, semi-empirical quantum mechanics was used to systematically refine these structures. A large collection of molecular dynamics traces of protein-ligand complexes in explicit water is included, accumulating over 170 µs. We give examples of machine learning (ML) baseline models proving an improvement of accuracy by employing our data. An easy entry point for ML experts is provided to enable the next generation of drug discovery artificial intelligence models.

Epstein-Barr virus-driven B cell lymphoma mediated by a direct LMP1-TRAF6 complex.

Epstein-Barr virus (EBV) latent membrane protein 1 (LMP1) drives viral B cell transformation and oncogenesis. LMP1's transforming activity depends on its C-terminal activation region 2 (CTAR2), which induces NF-κB and JNK by engaging TNF receptor-associated factor 6 (TRAF6). The mechanism of TRAF6 recruitment to LMP1 and its role in LMP1 signalling remains elusive. Here we demonstrate that TRAF6 interacts directly with a viral TRAF6 binding motif within CTAR2. Functional and NMR studies supported by molecular modeling provide insight into the architecture of the LMP1-TRAF6 complex, which differs from that of CD40-TRAF6. The direct recruitment of TRAF6 to LMP1 is essential for NF-κB activation by CTAR2 and the survival of LMP1-driven lymphoma. Disruption of the LMP1-TRAF6 complex by inhibitory peptides interferes with the survival of EBV-transformed B cells. In this work, we identify LMP1-TRAF6 as a critical virus-host interface and validate this interaction as a potential therapeutic target in EBV-associated cancer.