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 singlemodel methods. In this study, we develop a single-model method MQAPRank based on the learningto-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 crossvalidation 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. In the last two decades, with the increasing number of experimental structures and the promotion of computational techniques and computing power, various protein three-dimensional structure prediction methods have been developed and a lot of progress has been made in this area 1 . It is possible to generate numerous predicted models for a given protein sequence, and these models often span a broad range of accuracy spectrum and need to be annotated with accurate quality estimation for specific biomedical applications 2 . However, ranking the predicted models correctly and selecting the best predicted model from the candidate pool remain as challenging tasks. With the rapid increase in computing power, a greater number of models for a certain sequence can be predicted, consequently, evaluating the quality of a certain protein model in perspective is emerging as a problem that cannot be ignored. Since CASP7 (7th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction) 3 , a new prediction category evaluating the quality of predicted protein models and the reliability of predicting certain residues in the structure was implemented, which is called model quality assessment (MQA). Over the past years, a number of methods have been developed to address this issue 4,5 , and these methods could roughly be divided into three categories: single-model methods, clustering-based (or consensus-based) methods and quasi single-model methods. Single-model methods evaluate the model quality using the inputted model only [6] [7] [8] . Three conceptual approaches are often used in this category: the physical model, the statistical model and the comparison between the predicted properties and the actual properties extracted from the model, such as the uniformity of secondary structures, the solvent accessibility, the contact map, residue environment and other features. Many machine learning algorithms have been used to get an accurate estimation of the model quality from various features, such as support vector machine 9 , deep learning 10 , random forest 11 etc. The basic assumption of the clustering-based methods is that the near-native conformations have more chance to be re-sampled by structure prediction methods than the low quality structure models, so the conformations predicted with high frequency are more likely to be correct than structural patterns occurring in only a few models. The clustering-based methods often use clustering algorithm to cluster a set of decoys generated by structure prediction programs for a certain target sequence [12] [13] [14] . Previous studies have found that clustering-based methods generally outperform single-model methods when numerous models are available from several different structure prediction methods 15, 16 , which is also confirmed by the CASP (Critical Assessment of protein Structure Prediction) 4, 5, 17 . However, because of the strong dependence of model quality, the clustering-based methods will
These methods first select some high-quality structures as references and then compare the rest of the decoys with the reference structures REF .
Sorting protein decoys by machine-learning-to-rank
Scientific Reports
Scientific Reports
Computer Science Medicine