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Related Experiment Videos

EVA: continuous automatic evaluation of protein structure prediction servers.

V A Eyrich1, M A Martí-Renom, D Przybylski

  • 1Department of Chemistry, Columbia University, 3000 Broadway MC 3136, New York, NY 10027, USA. eva@cubic.bioc.columbia.edu

Bioinformatics (Oxford, England)
|December 26, 2001
PubMed
Summary

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EVA is a new web server that automates protein structure prediction method evaluation. It continuously assesses various methods, providing valuable insights for developers and users.

Area of Science:

  • Computational Biology
  • Structural Biology
  • Bioinformatics

Background:

  • Evaluating protein structure prediction methods is challenging and resource-intensive.
  • Existing methods for assessment are often manual and time-consuming.
  • There is a need for automated and large-scale evaluation of these predictive tools.

Purpose of the Study:

  • To introduce EVA, a novel web server designed for automated, continuous, and large-scale assessment of protein structure prediction methods.
  • To provide a centralized platform for evaluating the performance of diverse protein structure prediction tools.
  • To offer valuable insights to both the developers and users of these prediction methods.

Main Methods:

  • EVA automatically retrieves sequences of newly determined experimental protein structures weekly.

Related Experiment Videos

  • It submits these sequences to various available online prediction servers.
  • Collected prediction results are systematically evaluated, and performance summaries are generated and published online.

    • Main Results:

    • EVA has successfully collected performance data for over 3000 protein chains to date.
    • The server continuously monitors and evaluates multiple protein structure prediction methods.
    • Performance summaries are regularly updated and accessible via the web.

    Conclusions:

    • EVA offers an automated and scalable solution for assessing protein structure prediction methods.
    • The continuous data collection and evaluation by EVA provide ongoing insights into method performance.
    • This resource benefits developers by highlighting areas for improvement and users by informing tool selection.