Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Protein annotation is vital for understanding microbial diversity, metabolism, and evolution.
• Hypothetical proteins (HPs) represent a significant knowledge gap in genomics, especially with massive 'omics' data.
• Next-generation sequencing generates vast amounts of data, increasing the challenge of characterizing all proteins.

Approach:

• This review examines the principles of protein annotation, including classical and computational methods.
• It details machine learning and deep learning algorithms applicable to protein function prediction.
• Recent research examples demonstrate the application of AI in genome annotation.

Key Points:

• AI offers powerful tools to address the challenge of annotating hypothetical proteins.
• Machine and deep learning can analyze large biological datasets to infer protein functions.
• Understanding AI algorithms is essential for biologists and computer scientists in this field.

Conclusions:

• Leveraging AI is key to unlocking the potential of hypothetical proteins and advancing biological research.
• Comprehensive genome annotation using AI can reveal hidden metabolic potentials and evolutionary insights.
• This work aims to bridge the gap between AI development and biological application in protein annotation.