Commutative Algebra Learning for Protein Flexibility Analysis

Protein flexibility, commonly quantified by B-factors, is closely related to protein structure and function.

However, accurate B-factor prediction remains challenging due to the multiscale nature of protein structures and the complexity of atomic interactions.

In this work, we propose a commutative algebra-based learning framework, termed CAL, for protein B-factor prediction.

Unlike many biomolecular prediction tasks that rely primarily on global structural representations, B-factor prediction requires an accurate characterization of the local geometric environments surrounding individual atoms.

To address this challenge, CAL employs commutative algebra theory to construct localized algebraic descriptors at multiple spatial scales.

On a benchmark dataset of 364 proteins, CAL improves prediction accuracy by 34.5\% over the classical Gaussian network model (GNM).

Extensive experiments demonstrate that CAL achieves robust and consistent performance across diverse datasets and is competitive with existing state-of-the-art methods.

Furthermore, by integrating CAL with machine learning, we develop a blind prediction model capable of cross-protein B-factor prediction.

Overall, CAL provides an effective, efficient, and mathematically principled framework for protein flexibility prediction and offers a powerful approach for analyzing and predicting localized structural properties in complex biomolecular systems.