Gene regulation in oocytes relies heavily on poly(A) tail-length changes. Here, the authors develop PAL-AI, a neural network model that predicts tail-length changes, identifies regulatory motifs, and ...

Intracellular pathogens hijack host gene expression to subvert cellular processes. Here, the authors show that Legionella pneumophila’s PieF effector inhibits the human CCR4-NOT deadenylation machiner...

m6A is a potent inducer of ribosome stalling and collisions that trigger YTHDF-mediated mRNA degradation, a process that highlights the ribosome as a critical sensor linking translation dynamics to m6...

Alors qu’à l’entrée en CP, filles et garçons ont le même niveau en maths, un écart important apparaît en seulement 4 mois et s'accroît ensuite. Cette nouvelle étude menée sur près de 3  millions d’élè...

Advances in machine learning for structural biology have dramatically enhanced our capacity to predict protein–protein interactions (PPIs). Here, we review recent developments in the computational prediction of PPIs, particularly focusing on innovations that enable interaction predictions that have no precedence in nature, termed de novo. We discuss novel machine learning algorithms for PPI prediction, including approaches based on co-folding and atomic graphs. We further highlight methods that learn from molecular surfaces, which can predict PPIs not found in nature including interactions induced by small molecules. Finally, we explore the emerging biotechnological applications enabled by these predictive capabilities, including the prediction of antibody–antigen complexes and molecular glue-induced PPIs, and discuss their potential to empower drug discovery and protein engineering.

Rao et al. show that a polar dinoflagellate steals and maintains functional plastids, mitochondria, and nuclei from the microalga Phaeocystis. Plastids enlarge with enhanced photosynthesis, and mitoch...

Abstract. Functional RNA molecules are crucial for biological processes from gene regulation to protein synthesis, and analyzing functional motifs and elem

Zhang et al. identify a regulatory mechanism for how cells adapt to nutrient scarcity through widespread −1 ribosomal frameshifts, culminating in accelerated mRNA decay. This process, dependent on cod...

The mathematics of mass screening is crucial for understanding results from imperfect detection methods in large datasets. Although this framework is well-established it is not commonly applied in plant biology. Here, we view the identification of messenger RNAs that travel over long distances in plants (mobile mRNAs) through the lens of mass screening statistics. RNA-Seq analyses have identified thousands of mobile mRNAs. Consideration of the detection accuracy and prevalence, however, cast doubt on these numbers. The presented methodology is relevant to all areas of research where detection tests with less than 100% accuracy are applied to find rare events in large datasets.