bims-tricox 2025-03-23 papers

Issue of 2025–03–23
two papers selected by
Yash Verma, University of Zurich

Nat Commun. 2025 Mar 20. 16(1): 2751

Structurally heterogeneous ribosomes cooperate in protein synthesis in bacterial cells.

Karla Helena-Bueno, Sophie Kopetschke, Sebastian Filbeck, Lewis I Chan, Sonia Birsan, Arnaud Baslé, Maisie Hudson, Stefan Pfeffer, Chris H Hill, Sergey V Melnikov.

Ribosome heterogeneity is a paradigm in biology, pertaining to the existence of structurally distinct populations of ribosomes within a single organism or cell. This concept suggests that structurally distinct pools of ribosomes have different functional properties and may be used to translate specific mRNAs. However, it is unknown to what extent structural heterogeneity reflects genuine functional specialization rather than stochastic variations in ribosome assembly. Here, we address this question by combining cryo-electron microscopy and tomography to observe individual structurally heterogeneous ribosomes in bacterial cells. We show that 70% of ribosomes in Psychrobacter urativorans contain a second copy of the ribosomal protein bS20 at a previously unknown binding site on the large ribosomal subunit. We then determine that this second bS20 copy appears to be functionally neutral. This demonstrates that ribosome heterogeneity does not necessarily lead to functional specialization, even when it involves significant variations such as the presence or absence of a ribosomal protein. Instead, we show that heterogeneous ribosomes can cooperate in general protein synthesis rather than specialize in translating discrete populations of mRNA.

DOI: https://doi.org/10.1038/s41467-025-57955-8

Sci Rep. 2025 Mar 20. 15(1): 9587

Generative AI lacks the human creativity to achieve scientific discovery from scratch.

Amy Wenxuan Ding, Shibo Li.

Scientists are interested in whether generative artificial intelligence (GenAI) can make scientific discoveries similar to those of humans. However, the results are mixed. Here, we examine whether, how and what scientific discovery GenAI can make in terms of the origin of hypotheses and experimental design through the interpretation of results. With the help of a computer-supported molecular genetic laboratory, GenAI assumes the role of a scientist tasked with investigating a Nobel-worthy scientific discovery in the molecular genetics field. We find that current GenAI can make only incremental discoveries but cannot achieve fundamental discoveries from scratch as humans can. Regarding the origin of the hypothesis, it is unable to generate truly original hypotheses and is incapable of having an epiphany to detect anomalies in experimental results. Therefore, current GenAI is good only at discovery tasks involving either a known representation of the domain knowledge or access to the human scientists' knowledge space. Furthermore, it has the illusion of making a completely successful discovery with overconfidence. We discuss approaches to address the limitations of current GenAI and its ethical concerns and biases in scientific discovery. This research provides insight into the role of GenAI in scientific discovery and general scientific innovation.

Keywords: ChatGPT; Generative artificial intelligence; Large Language models; Scientific discovery

DOI: https://doi.org/10.1038/s41598-025-93794-9