Protein structure prediction center in CASP8

Area of Science:

• Computational Biology
• Structural Bioinformatics
• Protein Structure Prediction

Background:

• The Critical Assessment of Protein Structure Prediction (CASP) is a community-wide effort to assess the accuracy of protein structure prediction methods.
• Effective infrastructure is crucial for organizing and executing large-scale prediction experiments like CASP.
• The University of California, Davis, Protein Structure Prediction Center plays a role in supporting these efforts.

Purpose of the Study:

• To outline the infrastructure developed at the University of California, Davis, Protein Structure Prediction Center for supporting CASP experiments.
• To detail the functionalities of the infrastructure, including target selection, prediction collection, and result evaluation.
• To provide insights into the technical aspects and statistics of conducting a CASP experiment (CASP8).

Main Methods:

• Development and implementation of a comprehensive infrastructure for managing CASP experiments.
• Systematic processes for target selection and validation.
• Protocols for collecting, evaluating, and presenting submitted protein structure predictions.
• Facilitation of information exchange among participants and the broader scientific community.

Main Results:

• A robust infrastructure capable of supporting all phases of CASP experiments, from target selection to results presentation.
• Successful execution of the CASP8 experiment, with detailed technical aspects and statistics provided.
• Enhanced capabilities for information exchange related to protein structure prediction.

Conclusions:

• The implemented infrastructure at UC Davis is effective in supporting the Critical Assessment of Protein Structure Prediction.
• The system streamlines the complex process of organizing and conducting large-scale structure prediction assessments.
• This infrastructure contributes to advancing the field of protein structure prediction through organized experimentation and data sharing.