MLDH-Fold: Protein fold recognition based on multi-view low-rank modeling

Protein fold recognition is critical for understanding the molecular functions of proteins and drug design. Computational predictors have been proposed to identify protein into one of the known folds based only on the protein sequence information. However, how to combine different features to improve predictive performance remains a challenging problem. In this study, two novel methods (MVLR and MLDH-Fold) were proposed for protein fold recognition. We proposed a novel multi-view learning framework to combine the different views of protein sequences. Each view represents the similarity scores between the target sequences and template sequences calculated by the threading method. The proposed method extracts the low-rank principal features to precisely represent the similarity scores of each view and constructs the latent subspace with the common information of different views to predict the target proteins. Furthermore, we proposed an ensemble method called MLDH-Fold to combine the MVLR with the template-based methods. Predictive results on the two widely used datasets (LE and YK) show that the proposed computational methods outperform other computational predictors, indicating that the MVLR and MLDH-Fold are useful tools for protein fold recognition. CO 2020 Elsevier B.V. All rights reserved.

Automated summary

Protein fold recognition is crucial for understanding protein functions and drug design. New methods (MVLR and MLDH-Fold) were proposed to improve predictive performance by combining different views of protein sequences. Experimental results show that these computational methods outperform other predictors, indicating their usefulness for protein fold recognition.