PB-GPT: An innovative GPT-based model for protein backbone generation.

With advanced computational methods, it is now feasible to modify or design proteins for specific functions, a process with significant implications for disease treatment and other medical applications. Protein structures and functions are intrinsically linked to their backbones, making the design of these backbones a pivotal aspect of protein engineering. In this study, we focus on the task of unconditionally generating protein backbones. By means of codebook quantization and compression dictionaries, we convert protein backbone structures into a distinctive coded language and propose a GPT-based protein backbone generation model, PB-GPT. To validate the generalization performance of the model, we trained and evaluated the model on both public datasets and small protein datasets. The results demonstrate that our model has the capability to unconditionally generate elaborate, highly realistic protein backbones with structural patterns resembling those of natural proteins, thus showcasing the significant potential of large language models in protein structure design.

SurfPro-NN: A 3D point cloud neural network for the scoring of protein-protein docking models based on surfaces features and protein language models.

Protein-protein interactions (PPI) play a crucial role in numerous key biological processes, and the structure of protein complexes provides valuable clues for in-depth exploration of molecular-level biological processes. Protein-protein docking technology is widely used to simulate the spatial structure of proteins. However, there are still challenges in selecting candidate decoys that closely resemble the native structure from protein-protein docking simulations. In this study, we introduce a docking evaluation method based on three-dimensional point cloud neural networks named SurfPro-NN, which represents protein structures as point clouds and learns interaction information from protein interfaces by applying a point cloud neural network. With the continuous advancement of deep learning in the field of biology, a series of knowledge-rich pre-trained models have emerged. We incorporate protein surface representation models and language models into our approach, greatly enhancing feature representation capabilities and achieving superior performance in protein docking model scoring tasks. Through comprehensive testing on public datasets, we find that our method outperforms state-of-the-art deep learning approaches in protein-protein docking model scoring. Not only does it significantly improve performance, but it also greatly accelerates training speed. This study demonstrates the potential of our approach in addressing protein interaction assessment problems, providing strong support for future research and applications in the field of biology.

Resveratrol alleviates Ang II-induced vascular smooth muscle cell senescence by upregulating E2F1/SOD2 axis.

Background: Vascular smooth muscle cells (VSMCs) senescence is a crucial factor relevant to accelerate cardiovascular diseases. Resveratrol (RES) has been reported that could obstruct vascular senescence. However, the detailed molecular mechanisms of RES in VSMCs senescence are still indistinct and deserve further investigations. Methods and Results: In this study, VSMCs were treated with 100 nM angiotensin II (Ang II) for 3 days and then followed with a range of different concentrations of RES (0.5, 5, 15, 25, 35, 50 µM), and 25 µM of RES was chose for following experiments. We found that the E2F1 and SOD2 expressions were reduced in Ang II-induced VSMCs. RES treatment impeded Ang II-induced oxidative stress and mitochondrial dysfunction through elevating E2F1 and SOD2 expression, thereby alleviating VSMCs senescence. Additionally, E2F1 knockdown reversed the protective effects of RES on VSMCs senescence caused by Ang II administration. Ch-IP assay and dual luciferase reporter gene assay validated that E2F1 could bind to the promoter region of SOD2. Furthermore, E2F1 or SOD2 overexpression blocked Ang II-induced on VSMCs senescence. Conclusion: In conclusion, RES mitigated Ang II-induced VSMCs senescence by suppressing oxidative stress and mitochondrial dysfunction through activating E2F1/SOD2 axis. Our study disclosed that RES might be a potential drug and the axis of its regulatory mechanism might be therapeutic targets for postponing vascular senescence.