[Advances in machine learning for predicting protein functions].

Area of Science:

• Biochemistry and Molecular Biology
• Bioinformatics
• Computational Biology

Background:

• Proteins are vital for cellular functions, making their functional understanding critical for medicine and drug discovery.
• Enzyme applications in green synthesis are promising but hindered by high costs and diverse functionalities.
• Experimental protein function determination is laborious and time-consuming, lagging behind rapid sequencing advancements.

Approach:

• This review explores protein function annotation methods, detailing the evolution and application of machine learning (ML).
• It highlights ML's role in addressing the challenge of annotating the growing volume of protein sequence data.
• The focus is on ML-driven approaches for predicting enzyme functions and advancing protein research.

Key Points:

• The increasing gap between sequenced proteins and annotated functions necessitates efficient prediction methods.
• Machine learning provides a data-driven approach to overcome the limitations of experimental characterization.
• AI-assisted tools are crucial for accelerating the discovery of protein and enzyme functions.

Conclusions:

• Machine learning presents a powerful and efficient solution for protein function prediction.
• AI integration promises to revolutionize protein research, particularly in enzyme function discovery.
• Future research should focus on developing advanced AI models for comprehensive protein function annotation.