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FFAS-3D: improving fold recognition by including optimized structural features and template re-ranking.

Dong Xu1, Lukasz Jaroszewski, Zhanwen Li

  • 1Bioinformatics and Systems Biology Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Dr. La Jolla, CA 92093-0446, USA and Center of Excellence in Genomic Medicine Research (CEGMR), King Fahad Medical Research Center, King Abdulaziz University, P.O. Box 80216, Jeddah 21589, Kingdom of Saudi Arabia.

Bioinformatics (Oxford, England)
|October 17, 2013
PubMed
Summary
This summary is machine-generated.

The improved Fold and Function Assignment System (FFAS-3D) enhances protein homology detection and function prediction using optimized structural features and a neural network. This method increases alignment accuracy and aids in genome-wide functional annotations.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Bioinformatics

Background:

  • Homology detection is crucial for protein family grouping, structure, and function prediction.
  • Current methods can be improved by incorporating sequence profiles and local structural features for enhanced distant relation recognition.

Purpose of the Study:

  • To present recent advancements in the Fold and Function Assignment System (FFAS) method, now termed FFAS-3D.
  • To improve the accuracy and scope of homology-based protein predictions.

Main Methods:

  • Incorporation of optimized structural features (experimental or predicted).
  • Implementation of 'symmetrical' Z-score calculation.
  • Template re-ranking using a neural network.

Main Results:

  • FFAS-3D shows an 11% increase in alignment accuracy compared to the original FFAS.
  • Achieves high success rates at SCOP family, superfamily, and fold levels.
  • Results are not highly correlated with other programs, suggesting utility in meta-predictions.
  • FFAS-3D does not require 3D structures, enabling use with predicted features and expanding database compatibility.

Conclusions:

  • FFAS-3D offers improved accuracy and broader applicability for protein homology detection and functional annotation.
  • The method is valuable for analyzing large-scale genomic and protein family data.