Machine Learning Techniques to Infer Protein Structure and Function from Sequences: A Comprehensive Review.

Area of Science:

• Biochemistry
• Computational Biology
• Bioinformatics

Background:

• Understanding protein structure and function is crucial for biological insights and drug development.
• The increasing volume of protein sequence data necessitates advanced computational methods.
• Machine learning (ML) offers powerful tools for predicting protein characteristics directly from sequences.

Purpose of the Study:

• To provide a comprehensive review of ML applications in protein structure and function inference.
• To discuss various ML approaches, including deep learning architectures.
• To highlight current challenges and recent progress in the field.

Main Methods:

• Review of machine learning techniques, focusing on convolutional neural networks (CNNs) and natural language processing (NLP).
• Application of ML for predicting protein secondary and tertiary structures, residue-residue contacts, protein function, and subcellular localization.
• Exploration of advancements in deep learning architectures for protein analysis.

Main Results:

• ML techniques, particularly deep learning, show significant promise in predicting diverse protein attributes.
• Successful application of CNNs and NLP in inferring complex protein structural and functional information.
• Identification of key challenges, including data quality and model interpretability.

Conclusions:

• Machine learning is essential for advancing the understanding of protein structure and function.
• ML has the potential to revolutionize drug discovery and personalized medicine.
• Continued development of deep learning models will further enhance predictive capabilities.