DeepSymmetry : Using 3D convolutional networks for identification of tandem repeats and internal symmetries in protein structures - Archive ouverte HAL Access content directly
Journal Articles Bioinformatics Year : 2019

DeepSymmetry : Using 3D convolutional networks for identification of tandem repeats and internal symmetries in protein structures

(1) , (1)
1

Abstract

Motivation Thanks to the recent advances in structural biology, nowadays 3D structures of various proteins are solved on a routine basis. A large portion of these structures contain structural repetitions or internal symmetries. To understand the evolution mechanisms of these proteins and how structural repetitions affect the protein function, we need to be able to detect such proteins very robustly. As deep learning is particularly suited to deal with spatially organized data, we applied it to the detection of proteins with structural repetitions.
Results We present DeepSymmetry, a versatile method based on 3D convolutional networks that detects structural repetitions in proteins and their density maps. Our method is designed to identify tandem repeat proteins, proteins with internal symmetries, symmetries in the raw density maps, their symmetry order and also the corresponding symmetry axes. Detection of symmetry axes is based on learning 6D Veronese mappings of 3D vectors, and the median angular error of axis determination is less than one degree. We demonstrate the capabilities of our method on benchmarks with tandem-repeated proteins and also with symmetrical assemblies. For example, we have discovered about 7800 putative tandem repeat proteins in the PDB.
Availability and implementation The method is available at https://team.inria.fr/nano-d/software/deepsymmetry. It consists of a C++ executable that transforms molecular structures into volumetric density maps, and a Python code based on the TensorFlow framework for applying the DeepSymmetry model to these maps.
Fichier principal
Vignette du fichier
deepSymmetryArxiv.pdf (6.37 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01903624 , version 1 (26-10-2018)

Licence

Attribution - NonCommercial - CC BY 4.0

Identifiers

Cite

Guillaume Pagès, Sergei Grudinin. DeepSymmetry : Using 3D convolutional networks for identification of tandem repeats and internal symmetries in protein structures. Bioinformatics, 2019, 35 (24), pp.5113-5120. ⟨10.1093/bioinformatics/btz454⟩. ⟨hal-01903624⟩
534 View
342 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More