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Related Experiment Videos

Extracting hydrogen-bond signature patterns from protein structure data.

Tejasvini Prasad1, Tamilselvi Subramanian, Sridhar Hariharaputran

  • 1Bioinformatics Centre, Indian Institute of Science, Bangalore 560-012, India.

Applied Bioinformatics
|February 8, 2005
PubMed
Summary

Comparing protein intramolecular interaction networks reveals function beyond sequence or structure. This novel approach uses hydrogen-bond fingerprints to identify unexpected similarities in protein families.

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Area of Science:

  • Biochemistry
  • Structural Biology
  • Bioinformatics

Background:

  • Protein classification is crucial for biological research, often relying on sequence or structural similarities.
  • However, functionally similar proteins may lack discernible sequence/structure resemblance, and vice versa.
  • Existing comparison methods are insufficient for detecting all functional relationships.

Purpose of the Study:

  • To propose a new method for comparing protein molecules based on their intramolecular interaction networks.
  • To investigate if the 3D arrangement of key residues, reflected in interaction networks, is key to protein function.
  • To develop a novel metric for measuring protein structural similarities.

Main Methods:

  • Computed signature patterns (fingerprints) of intramolecular interaction networks in known protein families.

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  • Utilized an approach to identify structural equivalences and consensus hydrogen bonds.
  • Applied these fingerprints to search the Protein Data Bank (PDB).

    • Main Results:

    • Identified unique interaction network fingerprints for different protein structural families.
    • Discovered new, unexpected similarities between proteins by searching the PDB with these fingerprints.
    • Demonstrated the potential of hydrogen-bond fingerprints as a metric for structural comparison.

    Conclusions:

    • The 3D disposition of key residues, captured by interaction networks, is a significant determinant of protein function.
    • Hydrogen-bond fingerprints offer a powerful new way to detect protein similarities and relationships.
    • This approach expands the toolkit for protein classification and functional inference.