Understanding Proteins and Their Importance
Proteins are vital for many biological processes, including metabolism and immune responses. Their structure and function depend on the sequence of amino acids. Computational protein science aims to understand this relationship and create proteins with specific properties.
Advancements in AI for Protein Science
Traditional AI models have made progress in protein modeling, such as predicting structures and designing proteins. However, they struggle with understanding the complex rules of protein sequences. Recently, Protein Language Models (pLMs) have emerged, using advanced AI techniques to enhance our understanding of proteins, predict their functions, and design new ones.
Research Contributions
Researchers from various universities have developed pLMs that effectively capture protein knowledge and solve complex problems related to protein sequences, structures, and functions. These models are categorized into three types: sequence-based, structure- and function-enhanced, and multimodal models. They have applications in:
- Protein structure prediction
- Function prediction
- Protein design
Impact on Key Areas
pLMs are making significant contributions in:
- Antibody Design: pLMs can create antibody sequences that bind to specific targets, providing a more efficient and cost-effective alternative to traditional methods.
- Enzyme Engineering: These models optimize enzymes for better stability and new functions, improving their efficiency.
- Drug Discovery: pLMs predict how drugs interact with proteins, speeding up the identification of potential drug candidates.
Conclusion and Future Directions
This study highlights the transformative role of LLMs in protein science, discussing both foundational concepts and recent advancements. It emphasizes the potential of pLMs in practical applications like antibody design, enzyme engineering, and drug discovery. The review also points out future opportunities in this rapidly evolving field.
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