Prediction of global and local model quality in CASP7 using Pcons and ProQ

Abstract

The ability to rank and select the best model is important in protein structure prediction. Model Quality Assessment Programs (MQAPs) are programs developed to perform this task. They can be divided into three categories based on the information they use. Consensus based methods use the similarity to other models, structure-based methods use features calculated from the structure and evolutionary based methods use the sequence similarity between a model and a template. These methods can be trained to predict the overall global quality of a model, that is, how much a model is likely to differ from the native structure. The methods can also be trained to pinpoint which local regions in a model are likely to be incorrect. In CASP7, we participated with three predictors of global and four of local quality using information from the three categories described above. The result shows that the MQAP using consensus, Pcons, was significantly better at predicting both global and local quality compared with MQAPs using only structure or sequence based information.