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Predicting fold novelty based on ProtoNet hierarchical classification.

Ilona Kifer1, Ori Sasson, Michal Linial

  • 1Department of Biological Chemistry, Institute of Life Sciences Jerusalem 91904, Israel.

Bioinformatics (Oxford, England)
|November 13, 2004
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel computational method to identify proteins likely belonging to new structural superfamilies. Proteins distant in the ProtoNet classification are prioritized as potential novel targets.

Area of Science:

  • Structural biology
  • Bioinformatics
  • Computational genomics

Background:

  • Structural genomics aims to map protein structures, facing the challenge of identifying novel superfamilies.
  • Prioritizing proteins for structure determination is crucial for comprehensive protein space representation.

Purpose of the Study:

  • To develop a computational method for predicting proteins likely belonging to new structural superfamilies.
  • To improve the identification and prioritization of novel protein targets for structural genomics initiatives.

Main Methods:

  • Utilized a variant of ProtoNet, an automatic hierarchical classification of SwissProt protein sequences.
  • Assigned a likelihood score to each protein indicating its potential to belong to a new superfamily.
  • Validated the method against the Structural Classification of Proteins (SCOP) database.

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Main Results:

  • Proteins located further from solved structures within the ProtoNet hierarchy showed a higher likelihood of being new superfamilies.
  • The ProtoNet-based method demonstrated superior performance in detecting new targets compared to the previous ProtoMap classification.
  • The developed method outperformed PSI-BLAST in identifying potential new superfamilies.

Conclusions:

  • The ProtoNet classification effectively aids in identifying proteins likely representing new structural superfamilies.
  • This method offers a more efficient approach to prioritizing targets for structural genomics, surpassing existing tools.
  • The findings contribute to advancing the goal of representing the entire protein structural space.