Benchmarking Protein Docking Performance in Rosetta v3.2
Author Information
Author(s): Sidhartha Chaudhury, Monica Berrondo, Brian D. Weitzner, Pravin Muthu, Hannah Bergman, Jeffrey J. Gray
Primary Institution: Johns Hopkins University
Hypothesis
Does RosettaDock v3.2 improve docking performance compared to v2.3?
Conclusion
RosettaDock v3.2 achieved a 48% success rate in predicting near-native structures across a diverse set of protein complexes.
Supporting Evidence
- RosettaDock v3.2 achieved 56 successful predictions compared to 49 in v2.3.
- The new version was three times faster in generating decoys than the previous version.
- RosettaDock v3.2 performed better in terms of accuracy, with 50 predictions of medium or high accuracy.
Takeaway
This study tested a computer program that predicts how proteins stick together, and found that the new version works better than the old one.
Methodology
The study used a benchmark set of 116 docking targets to compare the performance of RosettaDock v3.2 against v2.3.
Potential Biases
Potential biases in the selection of docking targets and the inherent limitations of the docking algorithms.
Limitations
The study's success rate varied significantly across different types of protein complexes and docking difficulties.
Statistical Information
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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