DeepFrag-k: a fragment-based deep learning approach for protein fold recognition
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RESEARCH
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DeepFrag-k: a fragment-based deep learning approach for protein fold recognition Wessam Elhefnawy1 , Min Li2 , Jianxin Wang2 and Yaohang Li1* From 15th International Symposium on Bioinformatics Research and Applications (ISBRA’19) Barcelona, Spain. 3-6 June 2019 *Correspondence: [email protected] 1 Department of Computer Science, Old Dominion University, Norfolk, U.S.A. Full list of author information is available at the end of the article
Abstract Background: One of the most essential problems in structural bioinformatics is protein fold recognition. In this paper, we design a novel deep learning architecture, so-called DeepFrag-k, which identifies fold discriminative features at fragment level to improve the accuracy of protein fold recognition. DeepFrag-k is composed of two stages: the first stage employs a multi-modal Deep Belief Network (DBN) to predict the potential structural fragments given a sequence, represented as a fragment vector, and then the second stage uses a deep convolutional neural network (CNN) to classify the fragment vector into the corresponding fold. Results: Our results show that DeepFrag-k yields 92.98% accuracy in predicting the top-100 most popular fragments, which can be used to generate discriminative fragment feature vectors to improve protein fold recognition. Conclusions: There is a set of fragments that can serve as structural “keywords” distinguishing between major protein folds. The deep learning architecture in DeepFrag-k is able to accurately identify these fragments as structure features to improve protein fold recognition. Keywords: Fold recognition, Protein fragments, Deep learning
Background The relationship between the protein amino acid sequence and its tertiary structure is revealed by protein folding. A specific protein fold describes the distinctive arrangement of secondary structure elements in the nearly-infinite conformation space, which denotes the structural characteristics of a protein molecule. A number of protein fold databases, including CATH [1] and SCOP [2], have been developed to classify
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