Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction

TL;DR

This paper introduces a new framework to improve the robustness of protein property prediction models when using predicted structures, addressing the degradation caused by structure embedding bias.

Contribution

It identifies the structure embedding bias as a key issue and proposes the SAO framework to mitigate this bias, enhancing prediction accuracy for both predicted and experimental structures.

Findings

The proposed SAO framework effectively reduces embedding bias.

SAO improves prediction accuracy across multiple models.

Benchmark datasets and code are released for community use.

Abstract

Protein structure-based property prediction has emerged as a promising approach for various biological tasks, such as protein function prediction and sub-cellular location estimation. The existing methods highly rely on experimental protein structure data and fail in scenarios where these data are unavailable. Predicted protein structures from AI tools (e.g., AlphaFold2) were utilized as alternatives. However, we observed that current practices, which simply employ accurately predicted structures during inference, suffer from notable degradation in prediction accuracy. While similar phenomena have been extensively studied in general fields (e.g., Computer Vision) as model robustness, their impact on protein property prediction remains unexplored. In this paper, we first investigate the reason behind the performance decrease when utilizing predicted structures, attributing it to the structure embedding bias from the perspective of structure representation learning. To study this problem, we identify a Protein 3D Graph Structure Learning Problem for Robust Protein Property Prediction (PGSL-RP3), collect benchmark datasets, and present a protein Structure embedding Alignment Optimization framework (SAO) to mitigate the problem of structure embedding bias between the predicted and experimental protein structures. Extensive experiments have shown that our framework is model-agnostic and effective in improving the property prediction of both predicted structures and experimental structures. The benchmark datasets and codes will be released to benefit the community.