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Related Experiment Videos

M4T: a comparative protein structure modeling server.

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

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

Nucleic Acids Research
|May 23, 2007
PubMed
Summary
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The Multiple Mapping Method with Multiple Templates (M4T) server enhances protein structure modeling. It utilizes novel Multiple Templates and Multiple Mapping Method modules for improved accuracy, especially in comparative modeling.

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein modeling

Background:

  • Comparative protein structure modeling is crucial for understanding protein function.
  • Accurate sequence-to-structure alignment remains a challenge, particularly at low sequence identities.
  • Existing methods may not optimally leverage information from multiple template structures.

Purpose of the Study:

  • To introduce the Multiple Mapping Method with Multiple Templates (M4T) server for automated comparative protein structure modeling.
  • To enhance the accuracy of protein structure prediction through novel template selection and alignment strategies.
  • To provide a robust and efficient tool for researchers in structural biology.

Main Methods:

  • The Multiple Templates (MT) module iteratively selects and combines multiple template structures based on sequence similarity, unique contributions, and experimental resolution.

Related Experiment Videos

  • The Multiple Mapping Method (MMM) module improves sequence-to-structure alignment by integrating three profile-to-profile alignment methods.
  • MMM iteratively refines alignments by ranking regions based on their structural environment fit within templates.

    • Main Results:

    • M4T demonstrated favorable performance compared to state-of-the-art methods on CASP6 targets.
    • Benchmarking on an independent test set confirmed M4T's effectiveness in comparative modeling.
    • The MT and MMM modules contribute to improved alignment accuracy and modeling outcomes.

    Conclusions:

    • M4T offers a significant advancement in automated comparative protein structure modeling.
    • The integrated approach of MT and MMM modules enhances prediction accuracy, especially for challenging targets.
    • M4T provides a valuable resource for structural biology research and drug discovery efforts.