Protein model quality assessment using 3D oriented convolutional neural networks

By Guillaume Pagès, Benoit Charmettant, Sergei Grudinin. 2018

82 citations recorded by the source corpus.

Protein model quality assessment (QA) is a crucial and yet open problem in structural bioinformatics. The current best methods for single-model QA typically combine results from different approaches, each based on different input features constructed by experts in the field. Then, the prediction model is trained using a machine-learning algorithm. Recently, with the development of convolutional neural networks (CNN), the training paradigm has changed. In computer vision, the expert-developed features have been significantly overpassed by automatically trained convolutional filters. This motivated us to apply a three-dimensional (3D) CNN to the problem of protein model QA. We developed a novel method for single-model QA called Ornate. Ornate (Oriented Routed Neural network with Automatic Typing) is a residue-wise scoring function that takes as input 3D density maps. It predicts the local (residue-wise) and the global model quality through a deep 3D CNN. Specifically, Ornate aligns the input density map, corresponding to each residue and its neighborhood, with the backbone topology of this residue. This circumvents the problem of ambiguous orientations of the initial models. Also, Ornate includes automatic identification of atom types and dynamic routing of the data in the network. Established benchmarks (CASP 11 and CASP 12) demonstrate the state-of-the-art performance of our approach among singlemodel QA methods. The method is available at https://team.inria.fr/nanod/software/Ornate/. 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 Ornate model to these maps.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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Citations in the corpus

Learning from Protein Structure with Geometric Vector Perceptrons (2020, methodology): “A number of CPD methods (Anand et al., 2020; Zhang et al., 2019; Shroff et al., 2019) and the MQA methods 3DCNN (Derevyanko et al., 2018) and Ornate (Pag ` es et al., 2019) exemplify the power of this approach.”
ATOM3D: Tasks On Molecules in Three Dimensions (2020, methodology): “Task Metric 3DCNN GNN ENN SOTA Baseline [Pagès et al., 2019]”
GraphQA: protein model quality assessment using graph convolutional networks (2020, background): “Ornate (Pagès et al., 2018) applies a similar 3D approach to predict local CAD-scores (Olechnovic & Venclovas, 2017) and achieves rotation invariance by specifying a canonical residue-centered orientation.”
ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction (2021, background): “Previous works 339 [24, 30] considered other amino acid frames constructed from the backbone atoms only.”