Langlen Chanu, A. | bioRxiv Science

bioRxiv2025

PAF15 stabilizes PCNA on DNA and slows down its sliding dynamics

The PCNA-associated factor 15 (PAF15) protein is essential for human genome stability by regulating DNA replication and repair through its interaction with proliferating cell nuclear antigen (PCNA). Despite its significance, the mechanistic details of the PAF15-PCNA interaction remain insufficiently understood. Here, we reveal how PAF15 influences the sliding dynamics and function of PCNA. Using single-molecule diffusion analysis on a DNA skybridge platform, we show that PAF15 binding stabilizes PCNA in its DNA-bound state, reduces its diffusion rate along the duplex, restricts accessibility to PCNA PIP-box binding sites, and regulates PCNA loading and unloading on DNA via replication factor C (RFC). Our results are consistent with its coordinating role in both high-fidelity replication and lesion bypass. These findings establish PAF15 as a key regulator of PCNA function, serving as a mobile platform for DNA-editing enzymes and thereby influencing genome maintenance.

biophysics↗

bioRxiv2025

Deep learning-based classification of complex intracellular calcium concentration patterns

Intracellular calcium ion (Ca2+) exhibits diverse dynamical behaviors, including complex oscillations such as bursting and chaos. The distinction of diverse dynamical patterns in time series data holds significant biochemical and biophysical implications, as these patterns are intimately related to physiological cellular states associated with health and disease. In this work, we introduce a deep learning framework based on a large kernel convolutional neural network (LKCNN) for the classification of dynamical states of intracellular Ca2+ dynamics. We use a chemical Langevin equation to generate synthetic data for training the LKCNN, simulating intracellular Ca2+ concentration patterns mimicking real experimental traces. We show that the LKCNN framework reliably classifies diverse intracellular Ca2+ dynamical regimes, achieving near 90% accuracy across all dynamical states. To this end, we propose an optimized kernel size for the LKCNN classifier, which captures both short-lived fluctuations and long-range correlations. While steady states, bursting, and simple oscillations are robustly distinguished with near-perfect accuracy, the performance slightly degrades for chaotic and multiple periodicity states under realistic levels of intrinsic fluctuations, reflecting genuine overlap in their temporal signatures. We validate our classifier with experimental Ca2+ concentration data, showing strong agreement with manual labeling and confirming generalizability beyond synthetic datasets. These results establish LKCNNs as flexible and scalable tools for systematic classification of cellular dynamics, with broad potential applications to other oscillatory biological processes.

biophysics↗