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Bioinformatics. 2019 Nov 20. pii: btz870. doi: 10.1093/bioinformatics/btz870. [Epub ahead of print]

Protein Docking Model Evaluation by 3D Deep Convolutional Neural Networks.

Author information

1
Department of Computer Science, Purdue University, West Lafayette, IN, USA.
2
Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.

Abstract

MOTIVATION:

Many important cellular processes involve physical interactions of proteins. Therefore, determining protein quaternary structures provides critical insights for understanding molecular mechanisms of functions of the complexes. To complement experimental methods, many computational methods have been developed to predict structures of protein complexes. One of the challenges in computational protein complex structure prediction is to identify near-native models from a large pool of generated models.

RESULTS:

We developed a convolutional deep neural network-based approach named DOVE (DOcking decoy selection with Voxel-based deep neural nEtwork) for evaluating protein docking models. To evaluate a protein docking model, DOVE scans the protein-protein interface of the model with a 3D voxel and considers atomic interaction types and their energetic contributions as input features applied to the neural network. The deep learning models were trained and validated on docking models available in the ZDock and DockGround databases. Among the different combinations of features tested, almost all outperformed existing scoring functions.

AVAILABILITY:

Codes available at http://github.com/kiharalab/DOVEhttp://kiharalab.org/dove/.

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

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