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Protein structure modeling with MODELLER.

Narayanan Eswar1, David Eramian, Ben Webb

  • 1Department of Biopharmaceutical Sciences and California Institute for Quantitative Biomedical Research, University of California at San Francisco, San Francisco, CA, USA.

Methods in Molecular Biology (Clifton, N.J.)
|June 11, 2008
PubMed
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Computational protein modeling bridges the sequence-structure gap, providing useful protein models for over half of known sequences. This computational approach aids in understanding protein functions when experimental data is limited.

Area of Science:

  • Biochemistry
  • Structural Biology
  • Bioinformatics

Background:

  • Genome sequencing has rapidly expanded the number of known protein sequences.
  • Experimental protein structure determination methods are limited, characterizing only a small fraction of these sequences.
  • A significant gap exists between the number of known protein sequences and their experimentally determined structures.

Purpose of the Study:

  • To illustrate the application of computational protein structure modeling using MODELLER.
  • To demonstrate the construction of comparative protein models for proteins with unknown structures.
  • To highlight the potential of computational methods to address the sequence-structure gap.

Main Methods:

  • Utilized MODELLER software for comparative protein structure modeling.

Related Experiment Videos

  • Developed and automated protocols for model construction.
  • Focused on comparative modeling for proteins lacking experimental structures.

    • Main Results:

    • Successfully constructed a comparative model for a protein with an unknown structure.
    • Automated protocols yielded models of useful accuracy.
    • Achieved useful accuracy for domains in over 50% of known protein sequences.

    Conclusions:

    • Computational protein structure modeling, exemplified by MODELLER, can effectively bridge the sequence-structure gap.
    • Automated modeling protocols provide accurate protein models, increasing the structural information available for a large proportion of known protein sequences.
    • These computational techniques are crucial for advancing structural biology and understanding protein function in the post-genomic era.