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Related Concept Videos

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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.
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Protein Folding

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...
Protein Folding01:22

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Overview
Protein Folding01:22

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Molecular Chaperones and Protein Folding03:00

Molecular Chaperones and Protein Folding

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...
Molecular Chaperones and Protein Folding03:00

Molecular Chaperones and Protein Folding

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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Single-Molecule FRET Imaging for Observing the Conformational Dynamics of Dynamin-Like GTPase Atlastin
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Challenging cases for AlphaFold: two multidomain proteins with zinc-binding-, phosphorylation- or dimerization-driven

Yinshan Yang1, Hélène Déméné2

  • 1Centre de Biochimie Structurale (CBS), Univ Montpellier, CNRS, INSERM, Montpellier, France.

European Biophysics Journal : EBJ
|June 29, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence tools like AlphaFold show promise in protein structure prediction. However, this study demonstrates current limitations in accurately predicting complex, flexible multidomain proteins simultaneously.

Keywords:
AlphaFoldArtificial intelligenceCase studyPredictionProtein structure

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Area of Science:

  • Biochemistry
  • Structural Biology
  • Artificial Intelligence

Background:

  • Deep learning, particularly AlphaFold, has revolutionized protein structure prediction from amino acid sequences.
  • Despite advances, challenges remain in predicting proteins with complex conformational dynamics.

Purpose of the Study:

  • To evaluate the performance of AlphaFold versions on two challenging multidomain proteins: LicT and P1.
  • To identify limitations of current AI models in predicting proteins with inter-domain flexibility and dynamic behavior.

Main Methods:

  • Screening AlphaFold versions against LicT (Bacillus subtilis) and P1 (Rice Yellow Mottle Virus).
  • Analyzing prediction accuracy for proteins exhibiting phosphorylation-dependent or metal-binding-dependent conformational changes.

Main Results:

  • AlphaFold versions struggled to simultaneously predict all domains of the selected multidomain proteins.
  • Inter-domain flexibility, dimerization, and allosteric regulation pose significant challenges for current prediction algorithms.

Conclusions:

  • Current artificial intelligence models, including AlphaFold, face limitations in accurately predicting the complete structures of highly dynamic and flexible multidomain proteins.
  • Further advancements are needed to capture the complex conformational landscape of such proteins.