Biology subjects

Skrinjar, P.

Publications and source records attributed to Skrinjar, P..

4 recordsLinked to original sources

bioRxiv2026

PAIP1 couples mRNA export to cytoplasmic mRNP remodeling and poly(A) homeostasis

Messenger ribonucleoproteins (mRNPs) acquire distinct protein compositions as they move from the nucleus to the cytoplasm, yet how this transition is coordinated and how inefficient remodeling affects downstream mRNA metabolism remains poorly understood. Here, we identify PAIP1 as a metazoan cofactor of the mRNA export ATPase DDX19. By binding a conserved N-terminal motif in DDX19, PAIP1 is recruited to the nuclear pore, where it promotes exchange of the nuclear poly(A)-binding protein PABPN1 for cytoplasmic PABPC1, thereby coupling mRNA export to cytoplasmic mRNP maturation. PAIP1 depletion alters cytoplasmic mRNP composition, mRNA stability and poly(A)-tail homeostasis. In Drosophila embryos, where poly(A)-tail regulation of maternal mRNAs directs early development, maternal PAIP1 depletion shortens poly(A)-tails, delays zygotic genome activation, and causes severe developmental defects. Our findings identify an export-coupled mRNP maturation pathway linking PABP exchange to downstream mRNA metabolism.

molecular biology↗

bioRxiv2026

Stoic: Fast and accurate protein stoichiometry prediction

MotivationProtein complexes are central to cellular function, but experimental determination of their structures remains challenging. Structure prediction methods require prior knowledge of stoichiometry - the number of copies of each protein entity within a complex. Current approaches rely on computationally expensive brute-force methods that run structure prediction on multiple stoichiometry combinations, often with limited accuracy. ResultsWe introduce Stoic, a method that uses protein language model embeddings to predict protein complex stoichiometry. Our approach learns to identify interface residues that participate in protein-protein interactions, rather than relying on global sequence features. By integrating these interface-aware embeddings into a graph neural network, Stoic achieves fast and accurate stoichiometry prediction for both homomeric and heteromeric targets. AvailabilitySource code for inference and training along with web versions are available in the repository at https://github.com/PickyBinders/stoic. Contactjanani.durairaj@unibas.ch

bioinformatics↗

bioRxiv2025

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↗

bioRxiv2025

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↗