| Nr |
7 (Research article) |
| Authors |
Cazzanti, Luca; Gupta, Maya; Malmström, Lars; Baker, David |
| Title |
Quality Assessment of Low Free-Energy Protein Structure Predictions |
| Journal |
Machine Learning for Signal Processing (2005) 2005 375-380 |
| DOI |
10.1109/MLSP.2005.1532932 |
| Citations |
3 citations (journal impact: 0.0) |
| Abstract |
Analyzing and engineering cellular signaling processes requires accurate estimation of cellular subprocesses 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 |