Interpretable feature extraction and dimensionality reduction in ESM2 for protein localization prediction.

Area of Science:

• Computational Biology
• Bioinformatics
• Machine Learning in Biology

Background:

• Large language models (LLMs) are increasingly applied to biological predictions, particularly for amino acid sequence feature representation.
• Previous research has focused on LLM architecture and fine-tuning, with less attention paid to the nature of extracted features.
• Subcellular localization prediction is a key downstream task where LLM features show promise.

Purpose of the Study:

• To investigate diverse feature extraction strategies from the ESM2 model for biological sequence analysis.
• To understand the relationship between extracted features and specific subcellular localization.
• To evaluate the impact of feature inputs on prediction performance and interpretability.

Main Methods:

• Proposed and evaluated different ESM2 representation extraction strategies, considering sequence character type and position.
• Employed dimensionality reduction, predictive analysis, and interpretability techniques to analyze feature associations.
• Assessed prediction performance and interpretability robustness using Random Forest and Deep Neural Networks with varied feature inputs.

Main Results:

• Identified associations between specific feature types and subcellular localizations, such as N-terminal preference for Mitochondrion and Golgi apparatus.
• Demonstrated that phosphorylation site-based features can reflect phosphorylation properties.
• Showcased varied prediction performance and interpretability based on different feature inputs.

Conclusions:

• Novel insights into maximizing the utility of LLMs for biological domain knowledge extraction.
• Enhanced understanding of the mechanisms underlying LLM-based biological predictions.
• Provided accessible code and APIs for feature extraction and further research.