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Assessment of model accuracy estimations in CASP12
Andriy Kryshtafovych1, Bohdan Monastyrskyy1, Krzysztof Fidelis1
1Genome Center, University of California, Davis, California.
Proteins
|August 24, 2017
Summary
In CASP12, 42 protein model accuracy estimation methods were evaluated. Single-model methods excel at selecting the best models, while consensus methods better predict local accuracy.
Area of Science:
- Computational Biology
- Structural Bioinformatics
- Protein Modeling
Background:
- Protein structure prediction is crucial for understanding biological function.
- Assessing the accuracy of predicted protein models is a significant challenge.
- CASP (Critical Assessment of protein Structure Prediction) provides a benchmark for evaluating prediction methods.
Purpose of the Study:
- To assess the performance of 42 protein model accuracy estimation methods in the CASP12 experiment.
- To compare whole-model and per-residue accuracy estimation modes.
- To evaluate methods' ability to select best models, predict accuracy, and identify reliable regions.
Main Methods:
- Utilized scores from four evaluation packages (GDT_TS, LDDT, CAD, SphereGrinder) as ground truth.
- Assessed methods' performance in identifying best models, predicting absolute accuracy, distinguishing good/bad models, and assessing local accuracy.
- Compared single-model, consensus, and clustering methods.
Main Results:
- Single-model methods now outperform clustering methods in selecting the best models from decoy sets.
- Consensus methods remain superior for distinguishing between good and bad models and predicting local accuracy.
- The top accuracy estimation methods showed improvement over previous CASP benchmarks and reference methods.
Conclusions:
- Significant advancements were observed in protein model accuracy estimation methods.
- Single-model and consensus approaches offer complementary strengths for model evaluation.
- Top-performing methods demonstrate competitive accuracy comparable to leading tertiary structure predictors.
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