Fine-Tuning Protein Language Models Unlocks the Potential of Underrepresented Viral Proteomes

Area of Science:

• Computational biology
• Bioinformatics
• Virology

Background:

• Protein language models (pLMs) generate powerful protein embeddings for various applications.
• Current pLMs show bias against underrepresented species, especially viral proteins, due to limited training data.
• This underrepresentation hinders accurate modeling of viral protein diversity and function.

Purpose of the Study:

• To enhance the performance of pLMs on viral protein sequences.
• To address the bias in pLMs against underrepresented viral proteomes.
• To improve downstream applications in virology and biotechnology.

Main Methods:

• Fine-tuning pre-trained pLMs using diverse learning frameworks.
• Employing parameter-efficient fine-tuning strategies for viral protein sequences.
• Benchmarking the quality of learned protein embeddings.

Main Results:

• Fine-tuning significantly improves the quality of protein representations for viral proteins.
• Enhanced pLMs show improved performance on various downstream computational biology tasks.
• The approach effectively mitigates biases against underrepresented viral proteomes.

Conclusions:

• Fine-tuning pLMs on viral sequences is crucial for accurate viral protein modeling.
• This work provides tools and methods to advance viral biology research.
• The improved models support efforts in disease control and biotechnological innovation.