SODa: An Mn/Fe superoxide dismutase prediction and design server
2008
SODa: A Tool for Predicting Superoxide Dismutase Properties
Sample size: 374
publication
Evidence: high
Author Information
Author(s): Kwasigroch Jean Marc, Wintjens René, Gilis Dimitri, Rooman Marianne
Primary Institution: Université Libre de Bruxelles
Hypothesis
Can we develop a web tool that accurately predicts the properties of Fe/Mn superoxide dismutases?
Conclusion
SODa is a superior tool for predicting SOD properties and suggesting mutations for protein design.
Supporting Evidence
- SODa correctly identified 99.7% of the SOD sequences in the learning set.
- The prediction of cofactor specificity and oligomer state reached a score of 97%.
- SODa outperformed traditional methods based on pairwise sequence alignments.
Takeaway
SODa helps scientists figure out what type of superoxide dismutase a protein is and how to make it better.
Methodology
The SODa method uses residue and interaction fingerprints derived from a dataset of SOD sequences and structures to predict SOD properties.
Limitations
No cross-validation was performed in the predictions.
Statistical Information
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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