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Quality Assessment of Low Free-Energy Protein Structure Predictions

Citation Cazzanti, Luca; Gupta, Maya; Malmstrom, Lars; Baker, David; Quality Assessment of Low Free-Energy Protein Structure Predictions. Machine Learning for Signal Processing (2005), -: 375-380.
Abstract Analyzing and engineering cellular signaling processes requires accurate estimation of cellular sub processes such as protein-folding. We apply parametric and nonparametric classification to the problem of assessing three-dimensional protein domain structure predictions generated by the Rosetta ab initio structure prediction method. The assessment is based on whether the predicted structure is similar enough to a known protein structure to be classified as being in the same protein superfamily. We develop appropriate features and apply Gaussian mixture models, K-nearest-neighbors, and the recently developed linear interpolation with maximum entropy method (LIME). The proposed learning methods outperform a previous quality assessment method based on generalized linear models. Results show that the proposed methods reject the vast majority of poor structural predictions while identifying a useful number of good predictions.
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