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Benchmarking consensus model quality assessment for protein fold recognition.

Liam J McGuffin1

  • 1The School of Biological Sciences, University of Reading, Whiteknights, Reading RG6 6AS, UK. l.j.mcguffin@reading.ac.uk

BMC Bioinformatics
|September 20, 2007
PubMed
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Selecting the best protein 3D model is challenging. New methods like ModSSEA and ModFOLD improve protein 3D model quality assessment, outperforming existing techniques for better structural bioinformatics.

Area of Science:

  • Structural Bioinformatics
  • Computational Biology
  • Protein Modeling

Background:

  • Accurate protein 3D model selection is a significant challenge in structural bioinformatics.
  • Existing Model Quality Assessment Programs (MQAPs) use diverse strategies, but none consistently identify the highest accuracy models.
  • There is a need for improved methods to assess and rank protein structural models.

Purpose of the Study:

  • To benchmark top-performing MQAP methods in the context of protein fold recognition.
  • To introduce and evaluate two novel methods: ModSSEA and ModFOLD.
  • To assess the added value of MQAPs in improving protein model selection.

Main Methods:

  • Benchmarking of existing top-performing MQAP methods.
  • Development of ModSSEA based on predicted secondary structure element alignment.

Related Experiment Videos

  • Development of ModFOLD using an artificial neural network to combine multiple MQAP methods.
  • Evaluation of methods in the context of protein fold recognition.

    • Main Results:

    • ModSSEA effectively ranks multiple models from various servers.
    • The consensus approach of ModFOLD further enhances accuracy.
    • ModFOLD significantly outperforms tested "true" MQAPs and is competitive with server-based methods.
    • "True" MQAPs can improve model selection as post-filters for individual fold recognition servers.

    Conclusions:

    • MQAP benchmarking should align with their intended practical application.
    • Clustering-based MQAPs excel when numerous models from multiple servers are available.
    • "True" MQAPs are effective post-filters for re-ranking limited models from individual servers, with consensus methods offering further improvements.