## Homepage of QASproGP and QASproCL

A number of methods are available to predict the tertiary structure of proteins. These methods generate a selected number of tertiary structure models among which lie the near-native structures. The current challenge faced by these prediction methods is to select the best model from a number of structural models.### QASproGP

QASproGP selects and ranks the predicted structural models based on different geometrical parameters of the models. QASproGP is a single-model method and is trained on a large dataset of structural models (~16000 models) obtained from CASP8, CASP9 and CASP10 experiments.### QASproCL

The QASproCL method is trained on a large dataset of structural models obtained from CASP8, CASP9 and CASP10 experiments. A cutoff of ≥ 0.3 GDT_TS score was applied to select the structural models used in the training set. The test set comprised of 79 targets obtained from recent CASP11 experiments (stage1 tarballs) without applying any cutoff criteria. We calculated 3 different types of scores for each structural model of a given target: (a) average GDT_TS score of the structural model with all the other structural models of a given target; (b) average Pearson Correlation Coefficient (PCC) between the computed accessible surface area (using DSSP software 1) of the structural model with all the other structural models; and (c) the prediction score as given by QASproGP method. These 3 scores were then used to fit the linear regression model to finally predict the GDT_TS score of the structural model. The equation of the linear regression model was as follows:(1.1167*a) + (0.0407*b) + (0.1891*c)

Where a, b and c represents average GDT_TS score, average PCC and QASproGP score respectively.