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Related Experiment Videos

Protein threading using PROSPECT: design and evaluation.

Y Xu1, D Xu

  • 1Computational Biosciences Section, Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830-6480, USA. xyn@ornl.gov

Proteins
|June 22, 2000
PubMed
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PROSPECT, a protein fold recognition system, accurately identifies protein structures using threading alignment. Its scoring function, including pairwise contacts, significantly improves fold recognition and residue alignment accuracy.

Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Science

Background:

  • Protein structure prediction and fold recognition are crucial for understanding protein function.
  • Threading methods align protein sequences to known structures to infer folds.
  • Accurate alignment and fold recognition remain challenging, especially for low-sequence identity proteins.

Purpose of the Study:

  • To describe and evaluate the PROSPECT computer system for protein fold recognition using the threading method.
  • To assess the contribution of different scoring terms, particularly pairwise contacts, to threading accuracy.
  • To investigate the impact of incorporating secondary structure information and user-defined constraints on performance.

Main Methods:

  • PROSPECT employs a threading approach to find a globally optimal alignment between a target protein sequence and a template structure.

Related Experiment Videos

  • The scoring function integrates mutation, singleton fitness, pairwise-contact potentials, and gap penalties.
  • Pairwise contacts are considered between spatially close residues (≤7 Å Cβ-Cβ distance).
  • Secondary structure information (from PHD) and user constraints (disulfide bonds, active sites, NOE restraints) can be incorporated.

    • Main Results:

    • PROSPECT achieved 69% fold recognition and 66% alignment accuracy on a test set of 137 protein pairs (≤30% sequence identity).
    • The pairwise-contact term significantly improved performance compared to scoring without it (55% fold recognition, 64% alignment accuracy).
    • Incorporating PHD-predicted secondary structures further enhanced accuracy to 72% fold recognition and 74% alignment accuracy.
    • User-defined constraints demonstrated potential to further improve PROSPECT's performance.

    Conclusions:

    • PROSPECT effectively performs protein fold recognition and threading alignment, achieving high accuracy.
    • The pairwise-contact term is a critical component for improving threading performance.
    • Integration of predicted secondary structures and user constraints enhances the system's capabilities.
    • PROSPECT provides a robust tool for structural bioinformatics and protein structure prediction.