PIS: A Physics-Informed System for Accurate State Partitioning of $Aβ_{42}$ Protein Trajectories

TL;DR

PIS, a Physics-Informed System designed for robust metastable state partitioning, is introduced, a Physics-Informed System designed for robust metastable state partitioning that achieves superior performance on the A\beta_{42}$ dataset.

Abstract

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.

Figures (4)

• Figure 1: The architecture of PIS.
• Figure 2: The overall framework of the PIS model.
• Figure 3: User interface for multi-dimensional result validation and kinetic analysis.
• Figure 4: Interface for the dynamic monitoring of conformational evolution and physical metrics.