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

TL;DR

PIS is a physics-informed system that improves state partitioning of $Aeta_{42}$ protein trajectories by integrating physical priors, leading to better interpretability and performance in understanding Alzheimer's-related conformational changes.

Contribution

This work introduces PIS, a novel physics-informed framework that incorporates physical priors into protein trajectory analysis for enhanced metastable state detection.

Findings

Superior performance on $Aeta_{42}$ dataset

Enhanced interpretability with physical constraints

Interactive platform for dynamic analysis

Abstract

Understanding the conformational evolution of $\beta$-amyloid ($A\beta$), particularly the $A\beta_{42}$ 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 $A\beta_{42}$ 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.