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(PS)2: protein structure prediction server.

Chih-Chieh Chen1, Jenn-Kang Hwang, Jinn-Moon Yang

  • 1Institute of Bioinformatics, National Chiao Tung University, Hsinchu, 30050, Taiwan.

Nucleic Acids Research
|July 18, 2006
PubMed
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We developed (PS)2, an automatic protein structure prediction server using consensus strategies for template selection and alignment. (PS)2 significantly improves protein 3D structure prediction accuracy and speed.

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein science

Background:

  • Comparative modeling is crucial for predicting 3D protein structures from amino acid sequences.
  • Template selection and sequence alignment present significant challenges in comparative modeling.

Purpose of the Study:

  • To develop an automatic protein structure prediction server, (PS)2, that enhances accuracy and efficiency.
  • To address limitations in template selection and target-template alignment for comparative modeling.

Main Methods:

  • Implemented a consensus strategy combining PSI-BLAST and IMPALA for template selection.
  • Integrated PSI-BLAST, IMPALA, and T-Coffee for target-template alignment.
  • Evaluated (PS)2 on 47 comparative modeling targets from CASP6.

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Main Results:

  • The (PS)2 server demonstrated superior predictive performance compared to 10 other automatic servers based on the mean GTD_TS score.
  • The method relies solely on consensus sequence, offering faster computation than structure-consensus methods.
  • Consensus strategies and a novel similarity score significantly improved structure prediction accuracy.

Conclusions:

  • The (PS)2 server provides an effective and efficient approach for protein structure prediction.
  • The developed consensus strategies offer a significant advancement in comparative modeling.
  • The (PS)2 server is publicly available for research use.