The propagation of a viral infection from a host parent to its progeny is known as vertical transmission, or seed transmission in plants. It allows viral infections to rapidly spread locally via polle...

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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...

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...