EdaFold: An Evolutionary Algorithm based Fragment Assembly Method for De Novo Protein Structure Prediction
Fragment assembly is a powerful method of protein structure prediction that builds protein models from a pool of candidate fragments taken from known structures. Stochastic sampling is subsequently used to refine the models. We have developed a new method for fragment-based protein structure prediction based on an Estimation of Distribution Algorithm called EdaFold. This algorithm learns from previously generated decoys and steers the search toward native-like regions. A comparison with Rosetta AbInitio protocol shows that EdaFold is able to generate models with lower energies and to enhance the percentage of near-native decoys on a benchmark of 20 proteins.
We have used this EdaFold method to participate in the recent “10th Community Wide Experiment on theCritical Assessment of Techniques for Protein Structure Prediction (CASP10)”. Our method was ranked No. 1 out of 143 groups from world-wide participants in the template-free modeling category as judged by the average Z-score on GDT_TS. This prospective exercise has further validated the utility of method.