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A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

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Published on: November 3, 2011

Improving threading algorithms for remote homology modeling by combining fragment and template comparisons.

Hongyi Zhou1, Jeffrey Skolnick

  • 1Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA.

Proteins
|May 11, 2010
PubMed
Summary

A new method, fragment comparison and template comparison (FTCOM), improves protein model quality assessment for difficult targets. FTCOM enhances template selection accuracy, increasing the identification of foldable protein structures.

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Structure Prediction

Background:

  • Assessing the global quality of protein structural models is crucial for understanding protein function.
  • Protein structure prediction methods like threading and ab initio approaches often generate models of varying quality, especially for challenging targets (remote homology).
  • Existing template selection procedures can limit the accuracy of predicted protein models.

Purpose of the Study:

  • To develop and evaluate a novel method, fragment comparison and template comparison (FTCOM), for assessing the global quality of protein structural models.
  • To improve the accuracy of template selection for medium and hard difficulty protein targets.
  • To enhance the identification of correctly folded protein structures from prediction algorithms.

Main Methods:

  • The FTCOM method utilizes C(alpha) coordinates of full-length protein models.
  • Quality assessment is based on fragment comparison and a score derived from comparing the model to top threading templates.
  • FTCOM was applied to 361 medium/hard targets and compared against existing threading algorithms (SP(3), SPARKS, PROSPECTOR_3, PRO-SP(3)-TASSER).

Main Results:

  • The FTCOM method demonstrated an average TM-score improvement of 5-10% for the top selected model compared to original selection procedures.
  • The proportion of correctly predicted foldable targets (TM-score >= 0.4) significantly increased.
  • For SPARKS, the number of foldable targets increased from 7.6% to 54% when using FTCOM for template selection.

Conclusions:

  • FTCOM is a promising and effective approach for template selection in protein structure prediction.
  • The method significantly improves the quality assessment and selection of protein models, particularly for challenging targets.
  • FTCOM enhances the ability to identify correctly folded protein structures, advancing the field of computational protein modeling.