Journal of Molecular Biology最新文献

Ubiquitination is a common post-translational modification of proteins in eukaryotic cells, and it is also a significant method of regulating protein biological function. Computational methods for predicting ubiquitination sites can serve as a cost-effective and time-saving alternative to experimental methods. Existing computational methods often build classifiers based on protein sequence information, physical and chemical properties of amino acids, evolutionary information, and structural parameters. However, structural information about most proteins cannot be found in existing databases directly. The features of proteins differ among species, and some species have small amounts of ubiquitinated proteins. Therefore, it is necessary to develop species-specific models that can be applied to datasets with small sample sizes. To solve these problems, we propose a species-specific model (SSUbi) based on a capsule network, which integrates proteins’ sequence and structural information. In this model, the feature extraction module is composed of two sub-modules that extract multi-dimensional features from sequence and structural information respectively. In the submodule, the convolution operation is used to extract encoding dimension features, and the channel attention mechanism is used to extract feature map dimension features. After integrating the multi-dimensional features from both types of information, the species-specific capsule network further converts the features into capsule vectors and classifies species-specific ubiquitination sites. The experimental results show that SSUbi can effectively improve the prediction performance of species with small sample sizes and outperform other models.

Globally, the continuous spread and evolution of SARS-CoV-2, along with its variants, profoundly impact human well-being, health, security, and the growth of socio-economic. In the field of development of drugs against COVID-19, the main protease (M^pro) is a critical target as it plays a core role in the lifecycle of SARS-CoV-2. Bofutrelvir acts as a potent inhibitor of SARS-CoV-2 M^pro, demonstrating high efficacy and broad-spectrum antiviral activity. Compared to therapies that require pharmacokinetic boosters, such as ritonavir, the monotherapy approach of Bofutrelvir reduces the risk of potential drug interactions, making it suitable for a wider patient population. However, further studies on the potency and mechanism of inhibition of Bofutrelvir against the M^pro of COVID-19 and its variants, together with other coronaviruses, are needed to prepare for the possibility of a possible re-emerging threat from an analogous virus in the future. Here, we reveal the effective inhibition of Bofutrelvir against the M^pro of SARS-CoV-2, SARS-CoV, and HCoV-229E through FRET and crystallographic analysis. Furthermore, the inhibitory mechanisms of Bofutrelvir against two SARS-CoV-2 M^pro mutants (G15S and K90R) were also elucidated through FRET and crystallographic studies. Through detailed analysis and comparison of these crystal structures, we identified crucial structural determinants of inhibition and elucidated the binding mode of Bofutrelvir to M^pros from different coronaviruses. These findings are hopeful to accelerate the development of safer and more potent inhibitors against the M^pro of coronavirus, and to provide important references for the prevention and treatment of similar viruses that may emerge in the future.

ABC transporters are ancient and ubiquitous nutrient transport systems in bacteria and play a central role in defining lifestyles. Periplasmic solute-binding proteins (SBPs) are components that deliver ligands to their translocation machinery. SBPs have diversified to bind a wide range of ligands with high specificity and affinity. However, accurate assignment of cognate ligands remains a challenging problem in SBPs. Urea metabolism plays an important role in the nitrogen cycle; anthropogenic sources account for more than half of global nitrogen fertilizer. We report identification of urea-binding proteins within a large SBP sequence family that encodes diverse functions. By combining genetic linkage between SBPs, ABC transporter components, enzymes or transcription factors, we accurately identified cognate ligands, as we verified experimentally by biophysical characterization of ligand binding and crystallographic determination of the urea complex of a thermostable urea-binding homolog. Using three-dimensional structure information, these functional assignments were extrapolated to other members in the sequence family lacking genetic linkage information, which revealed that only a fraction bind urea. Using the same combined approaches, we also inferred that other family members bind various short-chain amides, aliphatic amino acids (leucine, isoleucine, valine), γ-aminobutyrate, and as yet unknown ligands. Comparative structural analysis revealed structural adaptations that encode diversification in these SBPs. Systematic assignment of ligands to SBP sequence families is key to understanding bacterial lifestyles, and also provides a rich source of biosensors for clinical and environmental analysis, such as the thermostable urea-binding protein identified here.