探索预训练嵌入在机器引导蛋白质设计中的极限：以预测AAV载体活力的案例研究
Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability
AI总结：
本研究评估了多种ProtBERT和ESM2嵌入变体在蛋白质序列表示中的表现，发现氨基酸级嵌入在监督任务中优于序列级嵌入，但需通过任务特定标签微调才能达到最佳性能。
摘要：
有效的蛋白质序列表示被视为机器学习基于蛋白质设计的基石。然而，蛋白质生物工程对序列表示提出了独特挑战，因为实验数据集通常包含少量突变，这些突变要么稀疏分布在整个序列中，要么集中在局部区域。这限制了序列级表示提取功能信号的能力。此外，尽管综合比较研究至关重要，但此类研究仍十分有限。在本研究中，我们系统评估了多种ProtBERT和ESM2嵌入变体作为序列表示，以腺相关病毒衣壳作为案例研究和生物工程的典型例子，通过在大蛋白质中高度局部化的序列变异来实现功能优化。我们的结果表明，在微调之前，氨基酸级嵌入在监督预测任务中优于序列级嵌入，而后者在无监督设置中更有效。然而，只有在嵌入通过任务特定标签微调时，才能达到最佳性能，序列级嵌入表现出最佳性能。此外，我们的发现表明，产生显著序列表示变化所需的序列变异程度超过了生物工程研究通常探索的范围，显示了在稀疏或高度局部化突变数据集中微调的必要性。
英文摘要：
Effective representations of protein sequences are widely recognized as a cornerstone of machine learning-based protein design. Yet, protein bioengineering poses unique challenges for sequence representation, as experimental datasets typically feature few mutations, which are either sparsely distributed across the entire sequence or densely concentrated within localized regions. This limits the ability of sequence-level representations to extract functionally meaningful signals. In addition, comprehensive comparative studies remain scarce, despite their crucial role in clarifying which representations best encode relevant information and ultimately support superior predictive performance. In this study, we systematically evaluate multiple ProtBERT and ESM2 embedding variants as sequence representations, using the adeno-associated virus capsid as a case study and prototypical example of bioengineering, where functional optimization is targeted through highly localized sequence variation within an otherwise large protein. Our results reveal that, prior to fine-tuning, amino acid-level embeddings outperform sequence-level representations in supervised predictive tasks, whereas the latter tend to be more effective in unsupervised settings. However, optimal performance is only achieved when embeddings are fine-tuned with task-specific labels, with sequence-level representations providing the best performance. Moreover, our findings indicate that the extent of sequence variation required to produce notable shifts in sequence representations exceeds what is typically explored in bioengineering studies, showing the need for fine-tuning in datasets characterized by sparse or highly localized mutations.
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