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Comparative protein structure modeling by combining multiple templates and optimizing sequence-to-structure

Narcis Fernandez-Fuentes1, Brajesh K Rai, Carlos J Madrid-Aliste

  • 1Department of Biochemistry and Seaver Center for Bioinformatics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.

Bioinformatics (Oxford, England)
|September 8, 2007
PubMed
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A new method, Multiple Mapping Method with Multiple Templates (M4T), improves protein structure modeling by efficiently combining multiple templates and optimizing target-template alignments for better accuracy.

Area of Science:

  • Computational biology
  • Structural bioinformatics

Background:

  • Comparative protein structure modeling faces challenges in combining multiple templates and generating accurate target-template alignments.
  • Efficiently integrating diverse structural information is crucial for advancing protein modeling.

Purpose of the Study:

  • To introduce a novel method, Multiple Mapping Method with Multiple Templates (M4T), for improved comparative protein structure modeling.
  • To address the bottlenecks of template selection, combination, and alignment generation.

Main Methods:

  • M4T employs an iterative clustering approach for selecting and combining multiple template structures (MT), considering template uniqueness, sequence similarity, and resolution.
  • The Multiple Mapping Method (MMM) optimizes sequence-to-structure alignments by integrating alternative alignment regions based on structural context.

Related Experiment Videos

  • The M4T-generated alignment serves as input for a comparative modeling module.

    • Main Results:

    • The MT module effectively selects and combines multiple templates based on multiple criteria.
    • MMM provides an optimal alignment by considering the structural environment of templates.
    • M4T demonstrated favorable performance compared to state-of-the-art methods on benchmark datasets (CASP6 and independent test set).

    Conclusions:

    • M4T offers a significant advancement in comparative protein structure modeling.
    • The method successfully overcomes key challenges in template selection and alignment generation.
    • M4T provides a robust and accurate approach for predicting protein structures.