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CASP3 comparative modeling evaluation.

T Alwyn Jones1, G J Kleywegt

  • 1Department of Cell and Molecular Biology, Uppsala University, Sweden. alwyn@xray.bmc.uu.se

Proteins
|October 20, 1999
PubMed
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This study evaluates the CASP3 comparative modelling competition, assessing fold accuracy, insertion/deletion bridging, and side-chain addition. Automation of evaluation aspects was also explored.

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein modelling

Background:

  • The Critical Assessment of protein Structure Prediction (CASP) competition benchmarks protein structure prediction methods.
  • Comparative modeling, a key prediction technique, relies on known structures to model new ones.

Purpose of the Study:

  • To evaluate the performance and accuracy of methods submitted to the CASP3 comparative modelling competition.
  • To analyze specific aspects of model quality, including overall fold accuracy, handling of insertions/deletions, and side-chain placement.
  • To explore the feasibility of automating parts of the model evaluation process.

Main Methods:

  • Analysis of submitted models against experimental structures from the CASP3 competition.
  • Assessment metrics focused on global fold accuracy, local structural alignment, and side-chain rotamer accuracy.

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  • Development and application of automated procedures for evaluating specific modelling aspects.

    • Main Results:

    • Detailed findings on the accuracy of comparative models in CASP3 across different evaluation criteria.
    • Identification of strengths and weaknesses in comparative modelling approaches prevalent during CASP3.
    • Demonstration of progress in automating the assessment of protein models.

    Conclusions:

    • The CASP3 competition provided valuable insights into the state of comparative modelling.
    • Specific areas like handling sequence insertions and deletions require further methodological development.
    • Automation holds promise for improving the efficiency and objectivity of protein model evaluation.