Improving Protein Fold Recognition by Deep Learning Networks

By Taeho C. Jo, Jie Hou, Jesse Eickholt, Jianlin Cheng. 2015

114 citations recorded by the source corpus.

For accurate recognition of protein folds, a deep learning network method (DN-Fold) was developed to predict if a given query-template protein pair belongs to the same structural fold. The input used stemmed from the protein sequence and structural features extracted from the protein pair. We evaluated the performance of DN-Fold along with 18 different methods on Lindahl’s benchmark dataset and on a large benchmark set extracted from SCOP 1.75 consisting of about one million protein pairs, at three different levels of fold recognition (i.e., protein family, superfamily and fold) depending on the evolutionary distance between protein sequences. The correct recognition rate of ensembled DN-Fold for Top 1 predictions is 84.5%, 61.5% and 33.6% and for Top 5 is 91.2%, 76.5% and 60.7% at family, superfamily and fold levels, respectively. We also evaluated the performance of single DN-Fold (DN-FoldS), which showed the comparable results at the level of family and superfamily, compared to ensemble DN-Fold. Finally, we extended the binary classification problem of fold recognition to real-value regression task, which also show a promising performance. DN-Fold is freely available through a web server at http://iris.rnet.missouri.edu/dnfold.

Subfields: Molecular Biology

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In Molecular Biology

1. Jinbo Xu
2. Sheng Wang
3. Jianlin Cheng
4. Jie Hou
5. Renzhi Cao
6. Badri Adhikari
7. Dongbo Bu
8. Jianwei Zhu

Citations in the corpus

• Protein threading using residue co-variation and deep learning (2018, background): “…and correct template recognition, both of which are challenging when only distantly-related templates are available for a protein sequence under prediction (Cozzetto and Tramontano, 2004; Hou, et al., 2018; Jo, et al., 2015; Jones, 1997; Peng and Xu, 2011a; Peng and Xu, 2011b; Zhu, et al., 2017).”
• Deep convolutional neural networks for predicting the quality of single protein structural models (2019, background): “Recently, deep learning techniques that can handle input of varied size have achieved significant success in the bioinformatics field 14,34,35 .”
• Deep Learning in Spiking Neural Networks (2018, methodology): “DBNs and convolutional DBNs have successfully been employed in many areas such as visual processing [192], [193], [194], [195], audio processing [196], [197], [198], [199], [200], time series forecasting [201], and protein folding [202].”