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Article Dans Une Revue Bioinformatics Année : 2021

VoroCNN: Deep convolutional neural network built on 3D Voronoi tessellation of protein structures

Résumé

Motivation: Effective use of evolutionary information has recently led to tremendous progress in computational prediction of three-dimensional (3D) structures of proteins and their complexes. Despite the progress, the accuracy of predicted structures tends to vary considerably from case to case. Since the utility of computational models depends on their accuracy, reliable estimates of deviation between predicted and native structures are of utmost importance. Results: For the first time we present a deep convolutional neural network (CNN) constructed on a Voronoi tessellation of 3D molecular structures. Despite the irregular data domain, our data representation allows to efficiently introduce both convolution and pooling operations of the network. We trained our model, called VoroCNN, to predict local qualities of 3D protein folds. The prediction results are competitive to the state of the art and superior to the previous 3D CNN architectures built for the same task. We also discuss practical applications of VoroCNN, for example, in the recognition of protein binding interfaces.
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Dates et versions

hal-02558004 , version 1 (29-04-2020)

Licence

Paternité - Pas d'utilisation commerciale

Identifiants

Citer

Ilia Igashov, Kliment Olechnovic, Maria Kadukova, Česlovas Venclovas, Sergei Grudinin. VoroCNN: Deep convolutional neural network built on 3D Voronoi tessellation of protein structures. Bioinformatics, 2021, 37 (16), pp.2332-2339. ⟨10.1093/bioinformatics/btab118⟩. ⟨hal-02558004⟩
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