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.
Elhefnawy, W., Li, M., Wang, J. et al. DeepFrag-k: a fragment-based deep learning approach for protein fold recognition. BMC Bioinformatics 21, 203 (2020).
Deep learning, Fold recognition, Protein fragments,
Curator: laiwei.cool@gmail.com