DeepSVM-fold: protein fold recognition by combining support vector machines and pairwise sequence similarity scores generated by deep learning networks

• Published in: Briefings in bioinformatics Vol. 21; no. 5; pp. 1733 - 1741
• Main Authors: Liu, Bin, Li, Chen-Chen, Yan, Ke
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.09.2020; Oxford Publishing Limited (England)
• Subjects: Computer applications; Deep learning; Feature extraction; Mathematical analysis; Protein folding; Proteins; Similarity; Support vector machines; Vector processing (computers); support vector machine; pairwise sequence similarity scores; convolutional neural network; protein fold recognition; long short-term memory
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbz098

Abstract Protein fold recognition is critical for studying the structures and functions of proteins. The existing protein fold recognition approaches failed to efficiently calculate the pairwise sequence similarity scores of the proteins in the same fold sharing low sequence similarities. Furthermore, the existing feature vectorization strategies are not able to measure the global relationships among proteins from different protein folds. In this article, we proposed a new computational predictor called DeepSVM-fold for protein fold recognition by introducing a new feature vector based on the pairwise sequence similarity scores calculated from the fold-specific features extracted by deep learning networks. The feature vectors are then fed into a support vector machine to construct the predictor. Experimental results on the benchmark dataset (LE) show that DeepSVM-fold obviously outperforms all the other competing methods.