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Related Experiment Videos

Servers for protein structure prediction.

Daniel Fischer1

  • 1Buffalo Center of Excellence in Bioinformatics, and Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY 14260, USA. df33@cse.buffalo.edu

Current Opinion in Structural Biology
|March 21, 2006
PubMed
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Automated protein structure prediction servers are rapidly advancing, with meta-servers outperforming others. These tools are becoming essential for biologists, easing the burden of predicting protein structures.

Area of Science:

  • Computational Biology
  • Structural Biology
  • Bioinformatics

Background:

  • The 1990s saw a rise in human protein structure prediction.
  • Advancements in structural genomics and genome sequencing have driven progress.
  • Automated protein structure prediction servers have evolved significantly.

Purpose of the Study:

  • To review the current state of automated protein structure prediction servers.
  • To guide biologists in selecting appropriate servers.
  • To assess the performance of various prediction methods.

Main Methods:

  • Evaluation of automated servers through LiveBench and CAFASP experiments.
  • Analysis of homology modeling servers for close and distant targets.
  • Comparison of autonomous servers and meta-servers.

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Main Results:

  • Meta-servers, which combine predictions from multiple servers, demonstrate superior performance.
  • Automated servers are increasingly used in experimental structure determination and genome annotation.
  • The performance difference among top autonomous servers is minimal and difficult to assess.

Conclusions:

  • Automated protein structure prediction servers are becoming highly reliable.
  • Meta-servers are challenging expert human predictors.
  • These servers are poised to significantly reduce the manual effort in protein structure prediction.