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A Protocol for Computer-Based Protein Structure and Function Prediction
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Protein structure modeling with MODELLER.

Benjamin Webb1, Andrej Sali

  • 1Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA.

Methods in Molecular Biology (Clifton, N.J.)
|February 28, 2014
PubMed
Summary
This summary is machine-generated.

Computational protein structure modeling addresses the gap between vast genome data and limited experimental structures. MODELLER software can generate accurate protein models, aiding in the characterization of over half of known protein sequences.

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Area of Science:

  • Structural bioinformatics
  • Computational biology
  • Genomics

Background:

  • Genome sequencing rapidly expands protein sequence data.
  • Experimental protein structure determination is experimentally intensive, yielding limited atomic-resolution structures.
  • A significant gap exists between known protein sequences and their experimentally determined structures.

Purpose of the Study:

  • To demonstrate the utility of computational protein structure modeling.
  • To illustrate the application of MODELLER for comparative protein modeling.
  • To assess the accuracy and applicability of automated modeling protocols.

Main Methods:

  • Utilizing MODELLER software for comparative protein structure modeling.
  • Developing and automating protocols for protein model construction.
  • Evaluating the accuracy of generated protein models.

Main Results:

  • Comparative models were successfully constructed for proteins with unknown structures.
  • Automated protocols yielded models of useful accuracy for a substantial portion of known protein sequences.
  • Over half of all known protein sequences could be modeled with useful accuracy.

Conclusions:

  • Computational protein structure modeling, exemplified by MODELLER, effectively bridges the sequence-structure gap.
  • Automated modeling protocols offer a scalable solution for characterizing large numbers of protein sequences.
  • This approach significantly enhances the potential for understanding protein function from sequence data.