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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.
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Protein Networks02:26

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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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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Protein and Protein Structure02:15

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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.
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A Protocol for Computer-Based Protein Structure and Function Prediction
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Published on: November 3, 2011

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Validation of protein structure models using network similarity score.

Sambit Ghosh1,2, Vasundhara Gadiyaram1,2, Saraswathi Vishveshwara1

  • 1Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India.

Proteins
|June 10, 2017
PubMed
Summary

A new network similarity score (NSS) method rigorously compares protein structures by analyzing backbone and side-chain connectivity. This approach quantifies subtle differences, offering a robust tool for protein structure validation and molecular dynamics analysis.

Keywords:
CASPclusteringfluctuationsmodel validationmolecular dynamic simulationnetwork similarityprotein structure networksside chain networksstructure comparisonweighted networks

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Area of Science:

  • Structural Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Accurate protein structure validation is crucial for structure prediction, molecular dynamics (MD) analysis, and identifying subtle structural changes.
  • Current validation benchmarks like GDT-TS, TM-score, and RMSD lack global connectivity analysis for both backbone and side-chain structures.
  • A need exists for methods that assess differences in protein structure connectivity at a global level.

Purpose of the Study:

  • To adapt and validate a graph spectral-based method, the network similarity score (NSS), for comparing protein structures at backbone and side-chain noncovalent connectivity levels.
  • To address the gap in methods that provide global connectivity information for protein structure comparison.
  • To introduce a network-based approach for analyzing side-chain interaction fluctuations in ensembles of structures.

Main Methods:

  • Adopted the network similarity score (NSS), a graph spectral-based method, to compare protein structures.
  • Applied NSS to analyze protein structures from X-ray crystallography, computational modeling (including CASP models), and molecular dynamics simulations.
  • Developed a network-based method to analyze side-chain interaction fluctuations (edge-weights) in structural ensembles.

Main Results:

  • The NSS method was validated across diverse protein structure datasets, demonstrating its ability to compare structures at both backbone and side-chain connectivity levels.
  • The method successfully quantified subtle differences in connectivity compared to reference protein structures.
  • Systematic identification of local and global regions contributing to NSS differences was achieved through score components, a unique feature of this spectral method.

Conclusions:

  • The network similarity score (NSS) provides a robust and unique spectral-based scoring scheme for comprehensive protein structure comparison.
  • NSS can effectively quantify subtle connectivity differences, serving as a strong foundation for structural validation and analysis.
  • The introduced network-based analysis of side-chain interactions is a valuable tool for interpreting molecular dynamics simulation trajectories.