![[2602.14828] Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.14828] Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability
Summary
This study evaluates the effectiveness of pre-trained embeddings in machine-guided protein design, focusing on predicting AAV vector viability. It highlights the importance of fine-tuning embeddings for optimal predictive performance in bioengineering tasks.
Why It Matters
Understanding the limitations and capabilities of pre-trained embeddings is crucial for advancing machine learning applications in protein design. This research provides insights into how sequence representations can be optimized, which is vital for developing more effective bioengineering strategies.
Key Takeaways
- Amino acid-level embeddings outperform sequence-level representations in supervised tasks.
- Sequence-level representations are more effective in unsupervised settings.
- Fine-tuning embeddings with task-specific labels is essential for optimal performance.
- The extent of sequence variation needed for effective representation exceeds typical bioengineering studies.
- Comparative studies on embedding effectiveness are crucial for improving predictive performance.
Quantitative Biology > Quantitative Methods arXiv:2602.14828 (q-bio) [Submitted on 16 Feb 2026] Title:Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability Authors:Ana F. Rodrigues, Lucas Ferraz, Laura Balbi, Pedro Giesteira Cotovio, Catia Pesquita View a PDF of the paper titled Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability, by Ana F. Rodrigues and 4 other authors View PDF Abstract: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 target...
