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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 Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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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.
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Different notations are used to represent the three-dimensional structure of molecules on two-dimensional surfaces. One of the most commonly used representations is the dash-wedge formula. The dashed wedges, solid wedges, and the plane lines indicate the groups situated behind the plane, coming out of the plane, and in the plane, respectively.
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Updated: Oct 9, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures.

Vy T Duong1, Elizabeth M Diessner1, Gianmarc Grazioli2

  • 1Department of Chemistry, University of California, Irvine, CA 92697, USA.

Biomolecules
|December 24, 2021
PubMed
Summary

We developed "neural upscaling" to reconstruct atomistic protein details from simplified network models. This method recovers crucial structural information, especially for intrinsically disordered proteins, enhancing molecular dynamics simulations.

Keywords:
coarse-grained modelsintrinsically disordered proteinsmachine learningmolecular dynamicsprotein structure networks

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Area of Science:

  • Biophysics
  • Computational Biology
  • Machine Learning

Background:

  • Coarse-graining simplifies complex biomolecular systems for efficient simulations.
  • Topological coarse-graining, using protein structure networks (PSNs), offers significant computational savings but loses atomistic detail.
  • Reconstructing atomistic detail from coarse-grained models is a key challenge.

Purpose of the Study:

  • To introduce a novel
  • neural upscaling
  • method for inferring atomic coordinates from protein structure networks (PSNs).
  • To evaluate the effectiveness of neural upscaling in recovering atomistic structural information, particularly for intrinsically disordered proteins.
  • To bridge the gap between computationally efficient coarse-grained models and the need for atomistic detail in molecular dynamics.

Main Methods:

  • Developed a machine learning approach combined with physically-guided refinement to infer atomic coordinates from PSNs.
  • Utilized the constraints and configuration likelihoods inherent in PSNs to guide the upscaling process.
  • Applied the method to a 1 μs atomistic molecular dynamics trajectory of Aβ1-40.

Main Results:

  • Neural upscaling successfully recapitulated detailed structural information from PSNs.
  • The method was particularly effective in recovering transient secondary structure features in intrinsically disordered proteins.
  • Demonstrated the ability to impute atomistic coordinates from network representations.

Conclusions:

  • Scalable network-based models can be combined with neural upscaling to achieve atomistic detail in protein structure and dynamics.
  • Neural upscaling offers a promising strategy for enhancing the utility of coarse-grained models in biophysical simulations.
  • This approach facilitates the study of complex biological systems where both computational efficiency and atomistic resolution are required.