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Related Concept Videos

Protein Organization01:24

Protein Organization

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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
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Protein Organization01:13

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Protein Organization01:13

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Protein and Protein Structure02:15

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Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme...
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A Protocol for Computer-Based Protein Structure and Function Prediction
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Protein structure prediction powered by artificial intelligence: from biochemical foundations to practical

Tianxiang Yin1, Yunxuan Chen2, Yuhang Wang3

  • 1Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.

Frontiers in Molecular Biosciences
|March 25, 2026
PubMed
Summary

Protein structure prediction is revolutionized by AI, overcoming limitations of experimental and traditional computational methods. Advanced deep learning models now offer near-experimental accuracy and increased speed for diverse applications.

Keywords:
AlphaFoldESMFoldRoseTTAFoldartificial intelligenceprotein language modelsprotein structure prediction

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Area of Science:

  • Structural biology
  • Computational biology
  • Biochemistry

Background:

  • Protein three-dimensional structure dictates biological function, making its determination crucial.
  • Experimental methods (X-ray crystallography, NMR, cryo-EM) face limitations in throughput and cost.
  • Traditional computational methods struggle with novel proteins and complex folding.

Purpose of the Study:

  • To review biochemical principles of protein folding.
  • To summarize recent AI-driven advances in protein structure prediction.
  • To discuss applications and future directions in the field.

Main Methods:

  • Deep learning and large-scale protein language models.
  • Integration of evolutionary information and geometric constraints.
  • End-to-end neural architectures (e.g., AlphaFold3, RoseTTAFold) and single-sequence approaches (e.g., ESMFold).

Main Results:

  • AI models achieve near-experimental accuracy in protein structure prediction.
  • Single-sequence models provide significant speed and scalability improvements.
  • AI advancements are transforming drug discovery, enzyme engineering, and disease research.

Conclusions:

  • AI has overcome major hurdles in protein structure prediction.
  • Continued advancements promise broader applications and deeper biological insights.
  • Addressing current challenges will further refine AI's role in structural biology.