  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.
