DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields

By Sheng Wang, Shunyan Weng, Jianzhu Ma, Qingming Tang, Lukasz Kurgan, Vladimir N Uversky. 2015

67 citations recorded by the source corpus.

Intrinsically disordered proteins or protein regions are involved in key biological processes including regulation of transcription, signal transduction, and alternative splicing. Accurately predicting order/disorder regions ab initio from the protein sequence is a prerequisite step for further analysis of functions and mechanisms for these disordered regions. This work presents a learning method, weighted DeepCNF (Deep Convolutional Neural Fields), to improve the accuracy of order/disorder prediction by exploiting the long-range sequential information and the interdependency between adjacent order/disorder labels and by assigning different weights for each label during training and prediction to solve the label imbalance issue. Evaluated by the CASP9 and CASP10 targets, our method obtains 0.855 and 0.898 AUC values, which are higher than the state-of-the-art single ab initio predictors.

Subfields: Molecular Biology

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In Molecular Biology

1. Sheng Wang
2. Jinbo Xu
3. Jianzhu Ma
4. Siqi Sun
5. Lukasz Kurgan
6. Vladimir N Uversky
7. Fanchi Meng
8. Shiwang Liu

Citations in the corpus

• RaptorX-Property: a web server for protein structure property prediction (2016, background): “The background distribution of these two states is 94:6 (24).”
• RaptorX-Angle: real-value prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning (2018, methodology): “Inspired by the excellent performance of convolution neural network in predicting secondary structure [31] and order/disorder regions [32] and also the success of residual framework to do contact prediction [33], we adopt the ultra deep residual framework of convolutional neural network to do…”
• Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions (2017, methodology): “DeepCNF-D [70] was created by Wang et al.”
• Deep Learning and Its Applications in Biomedicine (2018, methodology): “In addition to the secondary structure prediction, deep learning was also employed in protein region prediction [183,184].”
• A survey of deep neural network architectures and their applications (2017, methodology): “More specifically, to predict the protein order/disorder regions, the deep convolutional neural fields (DCNF) method was introduced in [157].”
• AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields (2016, methodology): “These features may allow for a richer representation of amino acids (Ma and Wang, 2015; Walsh et al., 2012).”
• AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling (2016, background): “The distribution of these two labels (ordered vs. disordered) is 94:6 [45].”