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Related Concept Videos

Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...
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Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
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Protein Organization01:24

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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.
Protein and Protein Structures02:15

Protein and Protein Structures

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 can...

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Updated: May 17, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

From static structures to dynamic landscapes: Generative artificial intelligence for protein conformational dynamics.

Jie Huang1, Yaowei Jin2, Qian Shi2

  • 1Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.

Current Opinion in Structural Biology
|May 15, 2026
PubMed
Summary
This summary is machine-generated.

Generative AI is advancing protein structure prediction beyond static folds to dynamic conformational ensembles. While promising for drug discovery, challenges in physical grounding and kinetic realism need addressing for reliable biological insights.

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Last Updated: May 17, 2026

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05:08

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Artificial Intelligence

Background:

  • Protein function is driven by dynamic conformational ensembles, posing challenges for static structure prediction.
  • Generative artificial intelligence (AI) has improved native-fold prediction but struggles with conformational landscapes and binding-induced transitions, limiting mechanistic biology and drug discovery.

Purpose of the Study:

  • To review recent advancements (2023-2025) in generative AI for predicting dynamic protein conformations.
  • To examine the limitations of current generative AI models in capturing protein dynamics.
  • To discuss the future potential of generative AI in dynamic structural biology and drug design.

Main Methods:

  • Review of emerging generative AI models for protein conformational ensemble generation.
  • Analysis of methods for ligand-responsive receptor sampling and pathway inference.
  • Evaluation of physical grounding, kinetic realism, data balance, and calibration in AI models.

Main Results:

  • Generative AI models are increasingly capable of learning dynamic conformational distributions directly.
  • Applications include ensemble generation, ligand-responsive sampling, and pathway inference.
  • Current models face limitations in predictive fidelity due to incomplete physical and kinetic realism.

Conclusions:

  • Generative AI shows significant promise for advancing dynamic structural biology and mechanism-guided drug design.
  • Addressing limitations in physical grounding and kinetic realism is crucial for reliable application.
  • Future developments may establish generative AI as a robust framework for understanding protein dynamics.