Analytical symmetry detection in protein assemblies. I. Cyclic symmetries

Pagès, Guillaume; Kinzina, Elvira; Grudinin, Sergei

doi:10.1016/j.jsb.2018.04.004

Cited by 22 publications

(29 citation statements)

References 29 publications

(39 reference statements)

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“…To assess the quality of predicting the direction of symmetry axes, we ran our method on 2,183 cyclic assemblies extracted from the PDB, whose symmetry order is between 4 and 10. In our previous work we developed a technique called AnAnaS that analyzes the quality of symmetry of cyclic molecular assemblies, and also determines their symmetry axes [7]. AnAnaS uses atomistic representation of the input data and is based on analytical minimization of a certain norm in the Euclidean space.…”

Section: Detection Of Cyclic Assembliesmentioning

confidence: 99%

“…We can see a very strong angular Figure 2: DeepSymmetry errors in the axis prediction, as tested on 2,183 symmetric assemblies from the PDB. For all the cyclic structures in the PDB with an order between 4 and 10, we computed the axes with the DeepSymmetry method and compared them with the calculations of AnAnaS by Pageès et al [7]. Please note that 90 • is the maximum possible error between two axes.…”

Section: Detection Of Cyclic Assembliesmentioning

confidence: 99%

“…The problem of detecting structural and, more specifically, symmetrical repetitions in protein assemblies can be formulated very well and was demonstrated to have efficient solutions [7,8]. On the other hand, identification of proteins with tandem repeats is a more difficult problem with a looser formulation [9,10,11].…”

Section: Introductionmentioning

confidence: 99%

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

Pagès

Grudinin

2019

Bioinformatics

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Motivation: Thanks to the recent advances in structural biology, nowadays three-dimensional structures of various proteins are solved on a routine basis. A large portion of these 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 three-dimensional (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 six-dimensional 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 over 10,000 putative tandem repeat proteins that are not currently present in the RepeatsDB database. Availability: 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.

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Section: Detection Of Cyclic Assembliesmentioning

confidence: 99%

Section: Detection Of Cyclic Assembliesmentioning

confidence: 99%

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

Pagès

Grudinin

2019

Bioinformatics

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“…Symmetrical protein complexes are very common in nature, as has been highlighted in our previous work on symmetry detection in cyclic protein assemblies [1], and many of these are deposited to the Protein Data Bank (PDB) [2]. As function of proteins is very often determined by their structure, it appears that complex function requires complex structures [3,4].…”

Section: Introductionmentioning

confidence: 99%

Analytical symmetry detection in protein assemblies. II. Dihedral and cubic symmetries

Pagès

Grudinin

2018

Journal of Structural Biology

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Protein assemblies are often symmetric, as this organization has many advantages compared to individual proteins. Complex protein structures thus very often possess high-order symmetries. Detection and analysis of these symmetries has been a challenging problem and no efficient algorithms have been developed so far. This paper presents the extension of our cyclic symmetry detection method for higher-order symmetries with multiple symmetry axes. These include dihedral and cubic, i.e., tetrahedral, octahedral, and icosahedral, groups. Our method assesses the quality of a particular symmetry group and also determines all of its symmetry axes with a machine precision. The method comprises discrete and continuous optimization steps and is applicable to assemblies with multiple chains in the asymmetric subunits or to those with pseudo-symmetry. We implemented the method in C++ and exhaustively tested it on all 51,358 symmetric assemblies from the Protein Data Bank (PDB). It allowed us to study structural organization of symmetric assemblies solved by X-ray crystallography, and also to assess the symmetry annotation in the PDB. For example, in 1.6% of the cases we detected a higher symmetry group compared to the PDB annotation, and we also detected several cases with incorrect annotation. The method is available at http://team.inria.fr/nano-d/software/ananas. The graphical user interface of the method built for the SAMSON platform is available at http://samson-connect.net.

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“…Using MemSTATS, we have assessed the performance of two well-established computational algorithms for detecting internal symmetry in protein structures, namely SymD [4] and CE-Symm [5,13], as well as two methods designed exclusively for quaternary symmetry detection, namely AnAnaS [14,15] and the BioJava module used for symmetry annotations of complexes in the protein data bank, PDB [16] (Fig. 1, 2, 3).…”

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confidence: 99%

MemSTATS: A Benchmark Set of Membrane Protein Symmetries and Pseudo-Symmetries

Aleksandrova

Sarti

Forrest

2019

Preprint

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In membrane proteins, symmetry and pseudo-symmetry often have functional or evolutionary implications. However, available symmetry detection methods have not been tested systematically on this class of proteins due to the lack of an appropriate benchmark set. Here we present MemSTATS, a publicly-available benchmark set of both quaternary and internal symmetries in membrane protein structures described in terms of order, repeated elements, and orientation of the axis with respect to the membrane plane. Moreover, using MemSTATS, we compare the performance of four widely-used symmetry detection algorithms and highlight specific challenges and areas for improvement in the future.

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Analytical symmetry detection in protein assemblies. I. Cyclic symmetries

Cited by 22 publications

References 29 publications

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

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

Analytical symmetry detection in protein assemblies. II. Dihedral and cubic symmetries

MemSTATS: A Benchmark Set of Membrane Protein Symmetries and Pseudo-Symmetries

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