Learning to predict if protein model is correct

Bioinformatics
Juin 2018

Deep convolutional networks for quality assessment of protein folds

Juin 2018
Motivation

The computational prediction of a protein structure from its sequence generally relies on a method to assess the quality of protein models. Most assessment methods rank candidate models using heavily engineered structural features, defined as complex functions of the atomic coordinates. However, very few methods have attempted to learn these features directly from the data.

Results

We show that deep convolutional networks can be used to predict the ranking of model structures solely on the basis of their raw three-dimensional atomic densities, without any feature tuning. We develop a deep neural network that performs on par with state-of-the-art algorithms from the literature. The network is trained on decoys from the CASP7 to CASP10 datasets and its performance is tested on the CASP11 dataset. Additional testing on decoys from the CASP12, CAMEO, and 3DRobot datasets confirms that the network performs consistently well across a variety of protein structures. While the network learns to assess structural decoys globally and does not rely on any predefined features, it can be analyzed to show that it implicitly identifies regions that deviate from the native structure.

Availability

The code and the datasets are available at https://github.com/lamoureux-lab/3DCNN_MQA.

Supplementary information

Supplementary data are available at Bioinformatics online.

Reference

Georgy Derevyanko, Sergei Grudinin, Yoshua Bengio, Guillaume Lamoureux; Deep convolutional networks for quality assessment of protein folds, Bioinformatics, bty494 https://doi.org/10.1093/bioinformatics/bty494

Source code

https://github.com/lamoureux-lab/3DCNN_MQA

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