Structural mechanism of GTPase-powered ribosome-tRNA movement

GTPases are regulators of cell signaling acting as molecular switches. The translational GTPase EF-G stands out, as it uses GTP hydrolysis to generate force and promote the movement of the ribosome along the mRNA. The key unresolved question is how GTP hydrolysis drives molecular movement. Here, we visualize the GTPase-powered step of ongoing translocation by time-resolved cryo-EM. EF-G in the active GDP–Pi form stabilizes the rotated conformation of ribosomal subunits and induces twisting of the sarcin-ricin loop of the 23 S rRNA. Refolding of the GTPase switch regions upon Pi release initiates a large-scale rigid-body rotation of EF-G pivoting around the sarcin-ricin loop that facilitates back rotation of the ribosomal subunits and forward swiveling of the head domain of the small subunit, ultimately driving tRNA forward movement. The findings demonstrate how a GTPase orchestrates spontaneous thermal fluctuations of a large RNA-protein complex into force-generating molecular movement

Conformational rearrangements upon start codon recognition in human 48S translation initiation complex

Selection of the translation start codon is a key step during protein synthesis in human cells. We obtained cryo-EM structures of human 48S initiation complexes and characterized the intermediates of codon recognition by kinetic methods using eIF1A as a reporter. Both approaches capture two distinct ribosome populations formed on an mRNA with a cognate AUG codon in the presence of eIF1, eIF1A, eIF2–GTP–Met-tRNAiMet and eIF3. The ‘open’ 40S subunit conformation differs from the human 48S scanning complex and represents an intermediate preceding the codon recognition step. The ‘closed’ form is similar to reported structures of complexes from yeast and mammals formed upon codon recognition, except for the orientation of eIF1A, which is unique in our structure. Kinetic experiments show how various initiation factors mediate the population distribution of open and closed conformations until 60S subunit docking. Our results provide insights into the timing and structure of human translation initiation intermediates and suggest the differences in the mechanisms of start codon selection between mammals and yeast

Reproducing RECIST lesion selection via machine learning: Insights into intra and inter-radiologist variation

Background: The Response Evaluation Criteria in Solid Tumors (RECIST) aims to provide a standardized approach to assess treatment response in solid tumors. However, discrepancies in the selection of measurable and target lesions among radiologists using these criteria pose a significant limitation to their reproducibility and accuracy. This study aimed to understand the factors contributing to this variability. Methods: Machine learning models were used to replicate, in parallel, the selection process of measurable and target lesions by two radiologists in a cohort of 40 patients from an internal pan-cancer dataset. The models were trained on lesion characteristics such as size, shape, texture, rank, and proximity to other lesions. Ablation experiments were conducted to evaluate the impact of lesion diameter, volume, and rank on the selection process. Results: The models successfully reproduced the selection of measurable lesions, relying primarily on size-related features. Similarly, the models reproduced target lesion selection, relying mostly on lesion rank. Beyond these features, the importance placed by different radiologists on different visual characteristics can vary, specifically when choosing target lesions. Worth noting that substantial variability was still observed between radiologists in both measurable and target lesion selection. Conclusions: Despite the successful replication of lesion selection, our results still revealed significant inter-radiologist disagreement. This underscores the necessity for more precise guidelines to standardize lesion selection processes and minimize reliance on individual interpretation and experience as a means to bridge existing ambiguities