PIS: A Physics-Informed System for Accurate State Partitioning of $Aβ_{42}$ Protein Trajectories
This work addresses a domain-specific challenge for biological researchers studying Alzheimer's disease by providing an incremental improvement in state partitioning with enhanced interpretability.
The paper tackles the problem of accurately partitioning metastable states in Aβ42 protein trajectories for Alzheimer's disease research by introducing PIS, a physics-informed system that integrates physical priors, achieving superior performance on the Aβ42 dataset.
Understanding the conformational evolution of $β$-amyloid ($Aβ$), particularly the $Aβ_{42}$ isoform, is fundamental to elucidating the pathogenic mechanisms underlying Alzheimer's disease. However, existing end-to-end deep learning models often struggle to capture subtle state transitions in protein trajectories due to a lack of explicit physical constraints. In this work, we introduce PIS, a Physics-Informed System designed for robust metastable state partitioning. By integrating pre-computed physical priors, such as the radius of gyration and solvent-accessible surface area, into the extraction of topological features, our model achieves superior performance on the $Aβ_{42}$ dataset. Furthermore, PIS provides an interactive platform that features dynamic monitoring of physical characteristics and multi-dimensional result validation. This system offers biological researchers a powerful set of analytical tools with physically grounded interpretability. A demonstration video of PIS is available on https://youtu.be/AJHGzUtRCg0.