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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
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Understanding structural variability in proteins using protein structural networks.

Vasam Manjveekar Prabantu1, Vasundhara Gadiyaram1,2, Saraswathi Vishveshwara1

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

Current Research in Structural Biology
|May 19, 2022
PubMed
Summary
This summary is machine-generated.

Protein structure networks (PSNs) reveal side-chain interactions, offering a novel way to compare protein conformations and understand their functional diversity. This method highlights variability in protein structures beyond the backbone.

Keywords:
Edge-weight varianceProtein classificationProtein structural networksProtein structure comparisonStructural variability

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Area of Science:

  • Structural Biology
  • Computational Biology
  • Biophysics

Background:

  • Proteins function by adopting specific conformations from a range of possibilities.
  • Comparing protein conformations is crucial for understanding their dynamic nature.
  • Existing methods like root mean square deviation (RMSD) primarily focus on backbone structures, neglecting detailed side-chain interactions.

Purpose of the Study:

  • To develop a comprehensive framework for comparing protein conformations, explicitly including side-chain interactions.
  • To utilize protein structure network (PSN) formalism and graph spectral methods for detailed structural comparison.
  • To classify protein structural domains based on side-chain network topology and identify sources of conformational variability.

Main Methods:

  • Construction of protein structure networks (PSNs) by explicitly treating non-covalent side-chain interactions.
  • Comparison of PSNs from multiple crystal conformers using graph spectral methods for local and global analysis.
  • Pair-wise analysis of PSN topologies to assess dissimilarities and conformational diversity.
  • Ensemble analysis of edge-weight variance to quantify contributions to variability from specific interactions.

Main Results:

  • Proteins generally maintain backbone structure and interactions but exhibit variability in side-chain connectivity or overall structure.
  • Analysis revealed differential contributions of specific interactions to overall structure variability.
  • Identification of highly variable interactions and their impact on protein conformational diversity through case studies.
  • Sub-network variability based on solvent accessibility and secondary structure was investigated.

Conclusions:

  • PSN analysis provides a robust method for comparing protein conformations, capturing nuances missed by backbone-centric approaches.
  • The study establishes a framework for classifying protein structural domains based on side-chain network properties.
  • This approach offers a pathway to better correlate protein structure with function by understanding conformational dynamics.