BERTology Meets Biology: Interpreting Attention in Protein Language Models (2006.15222v3)

Abstract: Transformer architectures have proven to learn useful representations for protein classification and generation tasks. However, these representations present challenges in interpretability. In this work, we demonstrate a set of methods for analyzing protein Transformer models through the lens of attention. We show that attention: (1) captures the folding structure of proteins, connecting amino acids that are far apart in the underlying sequence, but spatially close in the three-dimensional structure, (2) targets binding sites, a key functional component of proteins, and (3) focuses on progressively more complex biophysical properties with increasing layer depth. We find this behavior to be consistent across three Transformer architectures (BERT, ALBERT, XLNet) and two distinct protein datasets. We also present a three-dimensional visualization of the interaction between attention and protein structure. Code for visualization and analysis is available at https://github.com/salesforce/provis.

• The paper demonstrates that attention mechanisms in Transformers capture the spatial structure of proteins by aligning with contact maps.
• It reveals that deeper layers specifically target binding sites, encoding complex biophysical properties and hierarchical protein features.
• The analysis shows amino acid-level coherence with biochemical metrics like the BLOSUM matrix, enhancing model interpretability for protein functions.

Analysis of Attention in Protein LLMs

The paper "BERTology Meets Biology: Interpreting Attention in Protein LLMs" presents an exploration into the interpretability of attention mechanisms within Transformer architectures applied to protein sequences. Transformer models, notably BERT and its variants, have excelled in learning robust representations for both natural language processing and protein modeling. However, the latent structures captured by these models often pose interpretability challenges. This work employs methods to elucidate the contributions of attention mechanisms in understanding protein properties and their functional ramifications.

Key Contributions

1. Structural and Functional Insights: One principal insight is that attention mechanisms can successfully capture the spatial structure of proteins. The paper demonstrates that attention weights in deeper layers of the model align strongly with protein contact maps, accurately connecting amino acids that are spatially proximal despite their sequential distance. This emergent behavior is consistent across multiple Transformer architectures—BERT, ALBERT, and XLNet—highlighting a robust property of these models.
2. Binding Sites and Biophysical Properties: Attention layers intricately target binding sites, revealing key interaction points within proteins that dictate their functionality. This targeting occurs increasingly in the deeper layers, suggesting a hierarchical abstraction process where higher-level features and complex biophysical properties are encoded progressively. The paper employs both attention visualization techniques and probing classifiers to substantiate these findings.
3. Amino Acid Analysis: The paper extends its analysis to finer granularity by examining attention interactions at the amino acid level. Intriguingly, attention distributions across amino acids correlate with the BLOSUM substitution matrix, indicating cognizance of biochemical similarities. This suggests that the attention heads are not memorizing individual amino acids but perhaps detecting underlying structural and functional patterns encoded within these relationships.
4. Visualization Tools: To facilitate intuitive understanding and scientific discovery, the authors provide a visualization framework that displays attention within the three-dimensional structure of proteins. Such tools are crucial in bridging learned representations with biological insights, potentially leading to novel discoveries in protein engineering and drug development.

Implications and Future Scope

The findings presented indicate that Transformer models, initially designed for NLP tasks, possess significant potential in bioinformatics, especially in areas requiring the elucidation of protein structure-function relationships. The ability of models to distinguish between intricacies of protein folding, binding affinity regions, and amino acid properties represents a leap forward in computational biology, comparable to advances seen with traditional molecular dynamics or sequence alignment techniques.

Practically, these insights can be exploited to enhance performance in proteomics tasks like structure prediction, mutation effect analysis, and protein design. However, exploiting this latent potential requires bridging the domain gap between computational learning and biological expertise. The visualizations proposed are a step toward this integration, yet there is scope for more interactive and explanatory tools that can cater to varied scientific needs.

Theoretically, this paper opens avenues for exploring how similar approaches could be adapted to other biological sequence types or even broader datasets containing structured hierarchies. Moreover, the impact of different architectural choices and transformer variants remains partially explored, presenting a rich area for further exploration.

In conclusion, the convergence of BERTology techniques with protein modeling delineates a frontier in leveraging AI for biological research. Understanding and improving these models' interpretability is crucial, not only for achieving better performance but also for ensuring the reliability and transparency necessary for scientific applications. As machine learning continues to pervade biological sciences, such interdisciplinary research will be instrumental in harnessing the full potential of these powerful computational tools.