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Multidomain Assembler (MDA) Generates Models of Large Multidomain Proteins.

Samuel Hertig1, Thomas D Goddard1, Graham T Johnson2

  • 1Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California; Resource for Biocomputing, Visualization, and Informatics, University of California, San Francisco, San Francisco, California.

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|May 9, 2015
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Summary

We created MULTIDOMAIN ASSEMBLER (MDA) to improve protein structure prediction for large, multi-domain proteins. MDA automates template selection and model generation, addressing challenges in complex protein modeling.

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein modeling

Background:

  • Homology modeling is crucial for predicting protein structures.
  • Modeling proteins with multiple domains presents unique challenges, including spatial arrangement and template selection.
  • Existing methods struggle with proteins containing numerous domains or a vast number of potential templates.

Purpose of the Study:

  • To develop an automated tool for homology modeling of multi-domain proteins.
  • To address challenges in template searching, selection, and spatial arrangement for complex protein structures.
  • To integrate this tool into the UCSF CHIMERA molecular graphics package.

Main Methods:

  • Developed MULTIDOMAIN ASSEMBLER (MDA) software.
  • Automated template searching, visualization, and selection processes.
  • Implemented multidomain model generation within UCSF CHIMERA.

Main Results:

  • MDA effectively handles proteins with a large number of domains (e.g., fibronectin).
  • The software addresses template coverage issues for regions with varying homology.
  • Successful demonstration of applications and discussion of MDA's capabilities.

Conclusions:

  • MULTIDOMAIN ASSEMBLER (MDA) is a valuable tool for homology modeling of complex, multi-domain proteins.
  • MDA automates and improves critical steps in the protein structure prediction pipeline.
  • The software offers solutions for challenges related to template selection and spatial arrangement in large proteins.