Journal Article

DeepFrag-k: a fragment-based deep learning approach for protein fold recognition

Li, Yaohang, Li, Min, Elhefnawy, Wessam, Wang, Jianxin

2020

Overview

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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Artificial neural networks / Belief networks / Bioinformatics / Biomedical and Life Sciences / Computational Biology / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Deep Learning / Fold recognition / Fragments / Life Sciences / Machine learning / Methods / Microarrays / Neural networks / Neural Networks, Computer / Protein Folding / Protein fragments / Protein research / Protein structure / Proteins