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.

通过研究，我们发现当利用 AI 工具（如 AlphaFold2）预测的蛋白质结构时，现有的方法在预测准确性上存在明显的下降，我们将该现象归因于结构表示学习的结构嵌入偏差。为了解决这个问题，我们提出了一种蛋白质结构嵌入对齐优化框架（SAO），并确定了一个稳健蛋白质性质预测的蛋白质三维图结构学习问题（PGSL-RP3），通过大量实验证明了我们的框架在改进预测结构和实验结构的性质预测方面既适用于各种模型，又有效果。

蛋白质三维图形结构学习用于稳健基于结构的蛋白质性质预测

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

Machine Learning-guided solutions for protein learning tasks have made
significant headway in recent years. However, success in scientific discovery
tasks is limited by the accessibility of well-defined and labeled in-domain
data. To tackle the low-data constraint, recent adaptions of deep learning
models pretrained on millions of protein sequences have shown promise; however,
the construction of such domain-specific large-scale model is computationally
expensive. Here, we propose Representation Learning via Dictionary Learning
(R2DL), an end-to-end representation learning framework in which we reprogram
deep models for alternate-domain tasks that can perform well on protein
property prediction with significantly fewer training samples. R2DL reprograms
a pretrained English language model to learn the embeddings of protein
sequences, by learning a sparse linear mapping between English and protein
sequence vocabulary embeddings. Our model can attain better accuracy and
significantly improve the data efficiency by up to $10^5$ times over the
baselines set by pretrained and standard supervised methods. To this end, we
reprogram an off-the-shelf pre-trained English language transformer and
benchmark it on a set of protein physicochemical prediction tasks (secondary
structure, stability, homology, stability) as well as on a biomedically
relevant set of protein function prediction tasks (antimicrobial, toxicity,
antibody affinity).

本文提出了一种通过 Representation Learning via Dictionary Learning（R2DL）框架，在少于一定训练样本的情况下对蛋白质序列进行嵌入，可以更好地进行蛋白质属性预测，并且可以比基于预先训练和标准监督方法的基线提高 $10^5$ 倍以上。