In eukaryotic cells, transcription by RNA polymerase II occurs in the context of chromatin, requiring the transcription machinery to navigate through nucleosomes as it traverses gene bodies. Recent advances in structural biology have provided unprecedented insights into the mechanisms underlying transcription elongation. This review presents a structural perspective on transcription through chromatin, focusing on the latest findings from high-resolution structures of transcribing RNA polymerase II-nucleosome complexes. I discuss how RNA polymerase II, in concert with elongation factors such as SPT4/5, SPT6, ELOF1, and the PAF1 complex, engages with and transcribes through nucleosomes. The review examines the stepwise unwrapping of nucleosomal DNA as polymerase advances, the roles of elongation factors in facilitating this process, and the mechanisms of nucleosome retention and transfer during transcription. This structural perspective provides a foundation for understanding the intricate interplay between the transcription machinery and chromatin, offering insights into how cells balance the need for genetic accessibility with the maintenance of genome stability and epigenetic regulation.
Understanding drug-protein interactions is crucial for elucidating drug mechanisms and optimizing drug development. However, existing methods have limitations in representing the three-dimensional structure of targets and capturing the complex relationships between drugs and targets. This study proposes a new method, DTA-GTOmega, for predicting drug-target binding affinity. DTA-GTOmega utilizes OmegaFold to predict protein three-dimensional structure and construct target graphs, while processing drug SMILES sequences with RDKit to generate drug graphs. By employing multi-layer graph transformer modules and co-attention modules, this method effectively integrates atomic-level features of drugs and residue-level features of targets, accurately modeling the complex interactions between drugs and targets, thereby significantly improving the accuracy of binding affinity predictions. Our method outperforms existing techniques on benchmark datasets such as KIBA, Davis, and BindingDB_Kd under cold-start setting. Moreover, DTA-GTOmega demonstrates competitive performance in real-world DTI scenarios involving DrugBank data and drug-target interactions related to cardiovascular and nervous system-related diseases, highlighting its robust generalization capabilities. Additionally, the introduced DTI evaluation metrics further validate DTA-GTOmega's potential in handling imbalanced data.