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Assessment of protein structure refinement in CASP9
Justin L MacCallum1, Alberto Pérez, Michael J Schnieders
1Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA. justin.maccallum@me.com.
Proteins
|November 10, 2011
Summary
Protein structure refinement performance in CASP9 showed no significant improvement. While some predictors enhance model physicality, limitations include small target numbers and conservative strategies, hindering major advancements.
Area of Science:
- Computational Biology
- Structural Biology
- Biophysics
Background:
- The Critical Assessment of protein Structure Prediction (CASP) is a community-wide experiment to assess protein structure prediction.
- Structure refinement is a crucial step in improving the accuracy of predicted protein models.
Purpose of the Study:
- To evaluate the performance of automated methods in protein structure refinement during the CASP9 experiment.
- To identify limitations and potential areas for improvement in current structure refinement strategies.
Main Methods:
- Analysis of submitted models in the CASP9 structure refinement category.
- Statistical assessment of refinement performance across participating groups.
- Comparison of refined models with experimental data and assessment of their physical realism.
Main Results:
- No significant improvement in the performance of top refinement groups compared to CASP8.
- Some predictors consistently improved model physicality, but few achieved statistically significant score improvements.
- Refinement challenges include insufficient targets for statistical significance, conservative predictor strategies, and difficulty in ranking submissions.
- No single sampling strategy proved optimal; conservative approaches were generally safer, while adventurous sampling yielded greater improvements at the cost of consistency.
- Improved backbone geometry did not always correlate with better agreement with experimental data.
Conclusions:
- Protein structure refinement performance has plateaued, with limited statistical gains.
- Current refinement strategies face challenges related to sample size, predictor conservatism, and sampling strategy selection.
- Refined protein models remain valuable for applications like solving the crystallographic phase problem via molecular replacement.
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