Exploring Protein Language Models for Synthetic Biology

The interplay between artificial intelligence and synthetic biology has revolutionized our understanding of protein design and function prediction. This blog delves into the insights of Etienne Goffient, PhD, senior researcher at the Technology Innovation Institute, Abu Dhabi, known for his expertise in bioinformatics and protein language models (PLMs).

In the workshop recap, he sheds light on the transformative potential of protein language models in predicting protein functions and enabling groundbreaking applications.

Editor’s note: This is a summary of a session from ODSC Europe 2024 on LLM evaluation. To learn directly from the experts in real time, be sure to check out ODSC East 2025 this May!

Understanding Proteins

Proteins, often called the building blocks of life, are pivotal to biological functions. These molecules, composed of amino acid sequences, derive their unique 3D structures and functions from these sequences. Tasks in protein research, like predicting 3D structures (e.g., AlphaFold’s achievements​) and solving the inverse folding problem, illustrate the complexity of linking sequence, structure, and function.

Protein Language Models (PLMs) vs. Large Language Models (LLMs)

Both PLMs and LLMs rely on generative models for predictions but differ fundamentally:

• Bidirectional Reading: Protein sequences can be analyzed in both directions, crucial for tasks like antibody design.
• Conditioning on Properties: PLMs incorporate additional information (e.g., structure, solubility) for enhanced accuracy.

Key Properties of Protein Language Models

Attention Mechanisms: Critical for estimating proximity in 3D space, these attention matrices approximate amino acid contact maps. While not always accurate, they provide valuable structural insights.

Caveats: These estimates serve as proxies and require careful interpretation.

Applications of Protein Language Models

PLMs drive innovation across various domains:

Protein Design:

• Antibodies: Optimizing variable regions for therapeutic applications.
• Enzymes: Refining active sites for industrial efficiency.
• Vaccines: Designing epitopes for targeted immune responses.

Industrial Uses: Applications span from biocatalysis to novel biomaterials development.

The Protein Language Model Ecosystem

The evolution of PLMs mirrors advancements in natural language processing. Key contributions include:

• Open-source Models: ESM2 and ProGen showcase the accessibility of high-quality PLMs​.
• Datasets:
• UniProt: Central to protein research with hundreds of millions of sequences.
• BFD and Meta-AI: Expanding the horizon with billions of sequences.

Benchmarks for Protein Language Models

PLMs are evaluated using diverse benchmarks:

• Structure Prediction: Contact maps and folding analysis.
• Property Estimation: Solubility and temperature optima.
• Function Prediction: Classification of protein functionalities.

Challenges: The lack of a unified benchmarking platform highlights a significant gap.

Protein Function Prediction

Protein function prediction, vital in industrial contexts, is complex due to dependencies and class imbalances. Effective approaches include:

• Fine-tuning: Adapting pre-trained embeddings with classifiers.
• Retrieval-Augmented Classification: Leveraging reference databases to enhance accuracy.
• Combined Methods: A synergy of fine-tuning and retrieval yields optimal results.

Code Demonstration

In the video, Etienne Goffient, PhD, provided in-depth, hands-on examples utilizing the capabilities of Pre-trained Language Models (PLMs), harnessing the power of platforms like Google Colaboratory. These interactive demonstrations served as a springboard for a wide range of explorations:

• T5 Model Exploration: Participants were guided through the intricacies of the T5 model, gaining a deeper understanding of its architecture, functionalities, and potential applications.
• Sequence Tokenization: The critical process of tokenization was demystified, showcasing how text sequences are broken down into smaller units for effective processing by the model.
• Embedding Retrieval and Augmented Classification: Attendees learned advanced techniques for retrieving embeddings (numerical representations of words or phrases) and leveraging them to enhance classification tasks, potentially leading to more accurate and nuanced results.
• Fine-Tuning on Custom Datasets: Etienne Goffient, PhD, demonstrated how to adapt pre-trained models to specific domains and tasks by fine-tuning them on custom datasets. This empowers users to tailor PLMs to their unique requirements and achieve superior performance on specialized applications.

Key Takeaways

As shown by Etienne Goffient, PhD,’s workshop, protein language models are indispensable tools for exploring protein structure and function. Open-source initiatives and datasets democratize access, accelerating innovation across synthetic biology, medicine, and industry.

Conclusion

This tutorial underscored the transformative role of protein language models in synthetic biology. By exploring the shared resources, readers are invited to engage actively in advancing this frontier. For further insights, connect with the speaker on GitHub and delve into related topics like DNA language models.