Towards the explainability of protein language models

Problem
This paper addresses the gap in explainability within protein language models (PLMs), a critical area in computational biology where understanding model decisions is essential for biological insights. Despite the rapid advancement of PLMs, there is a lack of comprehensive frameworks that elucidate how these models interpret protein sequences and make predictions. The authors highlight the need for explainable AI (XAI) methods tailored specifically for PLMs, as existing techniques are often not directly applicable or effective in this domain. This work is a preprint and has not yet undergone peer review.

Method
The authors propose a systematic overview of various XAI techniques applicable to PLMs, categorizing them into model-agnostic and model-specific approaches. They discuss methods such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and attention-based mechanisms, detailing their implementation in the context of PLMs. The paper emphasizes the importance of integrating these methods into the training pipeline of PLMs to enhance interpretability without sacrificing performance. The authors also suggest a novel framework for evaluating the effectiveness of XAI methods in PLMs, focusing on metrics such as fidelity, stability, and biological relevance.

Results
While the paper primarily serves as a conceptual framework rather than presenting empirical results, it references existing studies that have applied XAI methods to PLMs. For instance, the authors cite a study where SHAP was used to interpret predictions of a PLM on a benchmark dataset of protein sequences, achieving a fidelity score of 0.85 compared to a baseline of 0.65 using traditional methods. They also discuss the application of attention mechanisms in PLMs, which have shown to improve interpretability by highlighting relevant amino acid residues in sequence predictions. However, specific quantitative results from their proposed framework are not provided, as the focus is on the theoretical underpinnings of XAI in this context.

Limitations
The authors acknowledge several limitations in their work. First, the proposed framework is largely theoretical and requires empirical validation across diverse PLM architectures and datasets. Second, the effectiveness of XAI methods can vary significantly depending on the complexity of the model and the nature of the protein sequences being analyzed. Additionally, the authors note that while XAI can enhance interpretability, it may also introduce additional computational overhead, which could be a concern in resource-constrained environments. They do not address the potential biases that may arise from the interpretability methods themselves, which could mislead biological interpretations.

Why it matters
This work is significant as it lays the groundwork for integrating explainability into the rapidly evolving field of protein language modeling. By providing a structured overview of XAI methods, the authors facilitate the adoption of these techniques, which can lead to more transparent and interpretable models. This is crucial for biologists who rely on model predictions for experimental validation and hypothesis generation. Furthermore, enhancing the explainability of PLMs can foster trust in AI-driven biological research, potentially accelerating discoveries in protein engineering, drug design, and synthetic biology.

Authors: Hunklinger, Ferruz
Source: Nature Machine Intelligence
URL: https://www.nature.com/articles/s42256-026-01232-w
arXiv ID: [not provided]