Sorting protein decoys by machine-learning-to-rank

Abstract

Much progress has been made in Protein structure prediction during the last few decades. As the predicted models can span a broad range of accuracy spectrum, the accuracy of quality estimation becomes one of the key elements of successful protein structure prediction. Over the past years, a number of methods have been developed to address this issue, and these methods could be roughly divided into three categories: the single-model methods, clustering-based methods and quasi single-model methods. In this study, we develop a single-model method MQAPRank based on the learning-to-rank algorithm firstly, and then implement a quasi single-model method Quasi-MQAPRank. The proposed methods are benchmarked on the 3DRobot and CASP11 dataset. The five-fold cross-validation on the 3DRobot dataset shows the proposed single model method outperforms other methods whose outputs are taken as features of the proposed method, and the quasi single-model method can further enhance the performance. On the CASP11 dataset, the proposed methods also perform well compared with other leading methods in corresponding categories. In particular, the Quasi-MQAPRank method achieves a considerable performance on the CASP11 Best150 dataset.

Figure 1. The overall flowchart of the proposed methods.

Figure 2. The ROC curves of compared methods on the 3DRobot dataset based on GDT_TS score.

The ModFOLDclust2 is a clustering method, other compared methods are listed in “feature extraction” section.

Figure 3. The ROC curves of compared methods on the CASP11 dataset based on GDT_TS score.

(a) The ROC curves for Best150 dataset and (b) the corresponding AUCs for Select20 dataset.