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Related Concept Videos

Protein Folding01:25

Protein Folding

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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.
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...
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Molecular Chaperones and Protein Folding03:00

Molecular Chaperones and Protein Folding

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The native conformation of a protein is formed by interactions between the side chains of its constituent amino acids. When the amino acids cannot form these interactions, the protein cannot fold by itself and needs chaperones. Notably, chaperones do not relay any additional information required for the folding of polypeptides; the native conformation of a protein is determined solely by its amino acid sequence. Chaperones catalyze protein folding without being a part of the folded protein.
The...
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Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
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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 Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

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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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Protein and Protein Structure02:15

Protein and Protein Structure

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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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Updated: Sep 15, 2025

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

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Beyond static structures: protein dynamic conformations modeling in the post-AlphaFold era.

Xinyue Cui1, Lingyu Ge1, Xia Chen1

  • 1College of Information Engineering, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou 310023, Zhejiang, China.

Briefings in Bioinformatics
|July 15, 2025
PubMed
Summary
This summary is machine-generated.

Deep learning, like AlphaFold, excels at predicting static protein structures. However, understanding protein function requires modeling dynamic conformational changes, a key challenge in AI-driven structural biology.

Keywords:
deep learningdiffusion modeldynamic conformationsensemblemolecular dynamics (MD)protein structure prediction

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Area of Science:

  • Structural biology
  • Computational biology
  • Biophysics

Background:

  • Deep learning, exemplified by AlphaFold, has transformed static protein structure prediction.
  • Protein function is intrinsically linked to dynamic conformational transitions, not just static structures.
  • Understanding these dynamics is vital for elucidating protein mechanisms and regulation.

Purpose of the Study:

  • To review fundamental concepts of protein dynamic conformations.
  • To survey computational methods for modeling protein dynamics post-AlphaFold.
  • To identify challenges and future directions in AI-driven protein conformation studies.

Main Methods:

  • Literature review of protein dynamics concepts.
  • Survey of computational modeling techniques for protein conformational changes.
  • Analysis of challenges and future research avenues in the field.

Main Results:

  • Deep learning advances protein structure prediction but multi-state modeling remains a challenge.
  • Key challenges include data limitations, methodological constraints, and evaluation metrics for dynamics.
  • Potential strategies and future research directions are proposed to advance the field.

Conclusions:

  • Transitioning from static to dynamic protein representations is crucial for understanding function.
  • Addressing current challenges is essential for advancing AI-driven structural biology.
  • Further research into protein dynamics will deepen insights into biological mechanisms.