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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.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Conservation of Protein Domains02:26

Conservation of Protein Domains

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 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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Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.

Talant Ruzmetov1, Ta I Hung1,2, Saisri Padmaja Jonnalagedda3

  • 1Department of Chemistry, University of California, Riverside, CA92521.

Research Square
|July 9, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces Internal Coordinate Net (ICoN), a deep learning model for protein conformational analysis. ICoN efficiently samples protein dynamics and identifies novel conformations, aiding in understanding intrinsically disordered proteins (IDPs) and disease mechanisms.

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Area of Science:

  • Biophysics
  • Computational Biology
  • Structural Biology

Background:

  • Protein conformational ensembles are crucial for biological function, particularly for intrinsically disordered proteins (IDPs).
  • Understanding and computationally sampling these ensembles, especially for dynamic proteins like amyloid-β42 (Aβ42), presents significant challenges.
  • Existing methods struggle to comprehensively explore the vast conformational landscape of proteins.

Purpose of the Study:

  • To develop an unsupervised deep learning model, Internal Coordinate Net (ICoN), for learning and sampling protein conformational dynamics.
  • To rapidly identify novel synthetic protein conformations with complex structural arrangements.
  • To apply ICoN to the amyloid-β42 monomer to comprehensively sample its conformational landscape and rationalize experimental findings.

Main Methods:

  • Developed an unsupervised deep learning model, Internal Coordinate Net (ICoN), trained on molecular dynamics (MD) simulation data.
  • Utilized latent space interpolation within ICoN to generate novel synthetic conformations.
  • Applied the ICoN model to the amyloid-β42 monomer to explore its conformational landscape.

Main Results:

  • ICoN successfully learned physical principles of protein conformational changes from MD data.
  • The model efficiently generated novel synthetic conformations with sophisticated backbone and sidechain arrangements.
  • Comprehensive sampling of the Aβ42 monomer's conformational landscape revealed functionally relevant clusters and rationalized experimental data.
  • Identified novel conformations with atomistic details and distinct sidechain rearrangements, validated by EPR and amino acid substitution studies.

Conclusions:

  • Internal Coordinate Net (ICoN) provides a powerful and transferable deep learning approach for sampling protein conformational ensembles.
  • The method enhances the understanding of protein dynamics, IDPs, and disease-related protein aggregations.
  • Deep learning can effectively leverage learned atomistic motions for advanced protein conformation sampling and discovery.