Improved protein residue-residue contacts prediction using learning-to-rank

Protein residue-residue contacts dictate the topology of protein structure and play an important role in structural biology, especially in de novo protein structure prediction. Accurate prediction of residue contacts could improve the performance of de novo protein structure prediction methods. In this study, a novel method based on learning-to-rank (RRCRank) has been presented to predict protein residue-residue contacts. The proposed method formulates the contacts prediction problem as a ranking problem. Firstly, the contact probabilities of residue pairs are predicted by ensemble machine-learning classifiers and correlated mutations approaches. And then, the proposed method integrates the complementary outputs of machine-learning and correlated mutations approaches and uses the learning-to-rank algorithm to rank residue pairs based on their probabilities to be contacts. Benchmarked on the CASP11 dataset, the proposed method achieves an improved performance for all three categories of contacts (short-range, medium-range and long-range contacts), which shows the proposed method based on learning-to-rank could take advantage of machine-learning and correlated mutations approaches and could provide the state-of-the-art performance.