Online supplement to "Ranking predicted protein structures with support vector regression"
Jian Qiu, Will Sheffler, David Baker and William Stafford Noble
Submitted for publication.
Protein structure prediction is an important problem of both intellectual and practical interest. Most protein structure prediction approaches generate multiple candidate models first, and then use a scoring function to select the best model among these candidates. In this work, we develop a scoring function using support vector regression (SVR). Both consensus-based features and features from individual structures are extracted from a training data set containing native protein structures and predicted structural models submitted to CASP5 and CASP6. The SVR learns a scoring function that is a linear combination of these features. We test this scoring function on two data sets. First, when used to rank server models submitted to CASP7, the SVR score selects predictions that are comparable to the best performing server in CASP7, Zhang-Server, and significantly better than all the other servers. Even if the SVR score is not allowed to select Zhang-Server models, the SVR score still selects predictions that are significantly better than all the other servers. In addition, the SVR is able to select significantly better models and yield significantly better Pearson correlation coefficients than the two best Quality Assessment groups in CASP7, QA556 (LEE) and QA634 (Pcons). Second, this work aims to improve the ability of the Robetta server to select best models, and hence we evaluate the performance of the SVR score on ranking the Robetta server template-based models for the CASP7 targets. The SVR selects significantly better models than the Robetta K*Sync consensus alignment score.
- Supplementary tables
- Training set. The first column is the name of a structure, the second column is the GDT_TS of the structure, and the remaining 25 columns are the computed features, 21 features of individual structures and 4 consensus-based features. All the features have been standardized to have a mean of 0 and standard deviation of 1. A name of the form "T0???TSnat_1" refers to the native structure of CASP target T0???.
- CASP7 server evaluations by the SVR score.