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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Published on: April 8, 2020

HAGGNN: Hydration-Aware Geometric Graph Neural Network for Protein Thermal Stability Prediction.

Yingying Jiang¹, Lingzhi Liu¹, Yanrui Ding¹

¹School of Science, Jiangnan University, Wuxi, China.

|March 18, 2026

Summary

This summary is machine-generated.

Water actively influences protein stability, but current models overlook its role. Our new Hydration-Aware Geometric Graph Neural Network (HAGGNN) integrates hydration thermodynamics for improved protein thermal stability prediction.

Keywords:

equivariant graph neural network hydration environment invariant graph neural network protein thermostability prediction

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Area of Science:

Computational biology
Biophysics
Structural biology

Background:

Water is crucial for protein thermodynamics, influencing structure, function, and stability.
Existing deep learning models for protein thermal stability prediction often neglect the hierarchical hydration environment and its interplay with protein structure.

Purpose of the Study:

To develop a novel deep learning model that explicitly integrates the hydration environment into protein thermal stability prediction.
To address the limitations of current models by incorporating hydration thermodynamics alongside geometric and topological features.

Main Methods:

Introduction of the Hydration-Aware Geometric Graph Neural Network (HAGGNN).
Development of a unified Hydration-Geometry Co-Modeling framework combining invariant and equivariant Graph Neural Networks (GNNs).
Jointly capturing geometric dependencies and hydration effects to model protein-water interactions.

Main Results:

HAGGNN achieved superior predictive performance on a large-scale protein dataset compared to models without hydration thermodynamics.
Ablation studies confirmed the significant contribution of each module within the HAGGNN framework.
Demonstrated enhanced understanding of protein-water interactions and their impact on thermal stability.

Conclusions:

HAGGNN offers a new computational paradigm for protein thermal stability prediction by integrating geometric learning with hydration environment.
The model provides mechanistic insights into how hydration thermodynamics affects protein stability.
Highlights the importance of considering water's active role in computational protein design and analysis.