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Automatic consensus-based fold recognition using Pcons, ProQ, and Pmodeller.

Björn Wallner1, Huisheng Fang, Arne Elofsson

  • 1Stockholm Bioinformatics Center, Stockholm University, Stockholm, Sweden.

Proteins
|October 28, 2003
PubMed
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A new automatic protein fold recognition server, Pmodeller, approaches expert performance in CASP5. Consensus servers like Pmodeller, enhanced by quality assessment methods like ProQ2, show the power of collective prediction development.

Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Science

Background:

  • The Critical Assessment of protein Structure Prediction (CASP) benchmark evaluates protein structure prediction methods.
  • Automatic methods are increasingly used, but their performance relative to human experts is a key question.

Purpose of the Study:

  • To assess the performance of a novel automatic fold recognition server, Pmodeller, against human experts in CASP5.
  • To evaluate the impact of a new protein model quality assessment method, ProQ2, on prediction accuracy.

Main Methods:

  • Pmodeller, a consensus-based fold recognition server, was evaluated using CASP5 data.
  • The performance of Pmodeller was compared against manual expert predictions.
  • The ProQ2 method was integrated to assess and potentially improve protein model quality.

Related Experiment Videos

Main Results:

  • Pmodeller demonstrated performance comparable to, and in many cases exceeding, that of manual experts in CASP5.
  • While a few experts outperformed Pmodeller, most experts performed worse despite access to automatic predictions.
  • Incorporating ProQ2 for model quality evaluation led to improved prediction accuracy.

Conclusions:

  • Automatic fold recognition servers, particularly consensus-based ones like Pmodeller, are highly competitive with human experts.
  • The success of consensus servers highlights the value of integrating predictions from multiple sources.
  • Quality assessment tools such as ProQ2 are crucial for enhancing the reliability of protein structure predictions.