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Protein structure prediction and analysis using the Robetta server.

David E Kim1, Dylan Chivian, David Baker

  • 1Structural Genomics of Pathogenic Protozoa, Department of Biochemistry, University of Washington, Seattle WA 98195, USA.

Nucleic Acids Research
|June 25, 2004
PubMed
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The Robetta server offers automated protein structure prediction and analysis tools. It uses comparative modeling or de novo methods, including fragment insertion, to generate models and predict mutation effects.

Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Science

Background:

  • Protein structure prediction is crucial for understanding protein function.
  • Automated tools are needed to handle the large volume of sequence data.
  • Accurate protein models are essential for drug discovery and protein engineering.

Purpose of the Study:

  • To present the capabilities of the Robetta server for protein structure prediction and analysis.
  • To describe the methods employed by Robetta for generating structural models.
  • To highlight new features for predicting mutation effects on protein interactions.

Main Methods:

  • Automated parsing of protein sequences into domains.
  • Comparative modeling using templates identified by BLAST, PSI-BLAST, FFAS03, or 3D-Jury.

Related Experiment Videos

  • De novo structure prediction using the Rosetta fragment insertion method.
  • Integration of experimental Nuclear Magnetic Resonance (NMR) constraints for RosettaNMR.
  • Computational interface alanine scanning for predicting mutation effects.

    • Main Results:

    • Robetta successfully generates structural models for proteins using both comparative and de novo approaches.
    • The server can utilize existing structural templates or predict structures from scratch.
    • It provides tools for analyzing the impact of mutations on protein-protein interactions.

    Conclusions:

    • The Robetta server is a valuable resource for automated protein structure prediction and analysis.
    • It offers versatile methods adaptable to various sequence and data inputs.
    • Future updates will include protein design and docking capabilities, further enhancing its utility.