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Biology subjects

Skrinjar, P.

Publications and source records attributed to Skrinjar, P..

2 recordsLinked to original sources

Muscle Fiber- and Cell Type-Specificity of Training Adaptation in Male Mice

Skeletal muscle possesses extraordinary plasticity of structure, metabolism, and function in response to repeated contractile activity. As a syncytium embedded within a complex microenvironment, muscle relies on the coordination of distinct myonuclear populations and diverse mononucleated cell types. Here, we present a high-resolution single-nucleus RNA-sequencing atlas of 550000 skeletal muscle nuclei, capturing the longitudinal transcriptional responses of 17 distinct myonuclear and 21 mononuclear cell populations at multiple time points after one bout of exhaustive exercise in trained and sedentary mice. The transcriptional programs of these populations are further shaped by training status into divergent adaptive trajectories. A subset of oxidative myonuclei enters a delayed regenerative state post-exercise, reflecting a bifurcated response to a disproportionate metabolic load on fibers during endurance exercise. Prior training accelerates homeostatic recovery and shields oxidative nuclei from exacerbated damage signatures. In parallel, mononucleated cells emerge as the primary mediators of intercellular communication during recovery. Together, this dataset establishes that training adaptation emerges through a coordinated interplay of intrinsic adaptive programs of multicellular remodeling, and provides a foundational resource for mechanistic insights into muscle plasticity.

physiology↗

Have protein-ligand co-folding methods moved beyond memorisation?

Deep learning has driven major breakthroughs in protein structure prediction, however the next critical advance is accurately predicting how proteins interact with small molecule ligands, to enable real-world applications such as drug discovery. Recent cofolding methods aim to address this challenge, but evaluating their performance has been inconclusive due to the lack of relevant bench-marking datasets. Here we present a comprehensive evaluation of four leading all-atom cofolding methods using our newly introduced benchmark dataset Runs N Poses, which comprises 2,600 high-resolution protein-ligand systems released after the training cutoff used by these methods. We demonstrate that current cofolding approaches largely memorise ligand poses from their training data, hindering their use for de novo drug design. With this assessment and benchmark dataset, we aim to accelerate progress in the field by allowing for a more realistic assessment of the current state-of-the-art deep learning methods for predicting protein-ligand interactions.

bioinformatics↗