Spherical convolutions on molecular graphs for protein model quality assessment

Abstract Motivation 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. Results We developed Ornate (Oriented Routed Neural network with Automatic Typing)—a novel method for single-model QA. Ornate 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 single-model QA methods. Availability and implementation The method is available at https://team.inria.fr/nano-d/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. Supplementary information Supplementary data are available at Bioinformatics online.

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Protein model quality assessment prediction by combining fragment comparisons and a consensus Cα contact potential

Proteins Structure Function and Bioinformatics ◽

10.1002/prot.21813 ◽

2007 ◽

Vol 71 (3) ◽

pp. 1211-1218 ◽

Cited By ~ 26

Author(s):

Hongyi Zhou ◽

Jeffrey Skolnick

Keyword(s):

Quality Assessment ◽

Model Quality ◽

Contact Potential ◽

Model Quality Assessment ◽

Protein Model ◽

Protein Model Quality Assessment

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NEW MDS AND CLUSTERING BASED ALGORITHMS FOR PROTEIN MODEL QUALITY ASSESSMENT AND SELECTION

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213013600063 ◽

2013 ◽

Vol 22 (05) ◽

pp. 1360006 ◽

Cited By ~ 2

Author(s):

QINGGUO WANG ◽

CHARLES SHANG ◽

DONG XU ◽

YI SHANG

Keyword(s):

Multidimensional Scaling ◽

Quality Assessment ◽

Structure Prediction ◽

Tertiary Structure ◽

Selection Problem ◽

Model Quality ◽

Model Quality Assessment ◽

Protein Model ◽

Protein Model Quality Assessment ◽

Consensus Score

In protein tertiary structure prediction, assessing the quality of predicted models is an essential task. Over the past years, many methods have been proposed for the protein model quality assessment (QA) and selection problem. Despite significant advances, the discerning power of current methods is still unsatisfactory. In this paper, we propose two new algorithms, CC-Select and MDS-QA, based on multidimensional scaling and k-means clustering. For the model selection problem, CC-Select combines consensus with clustering techniques to select the best models from a given pool. Given a set of predicted models, CC-Select first calculates a consensus score for each structure based on its average pairwise structural similarity to other models. Then, similar structures are grouped into clusters using multidimensional scaling and clustering algorithms. In each cluster, the one with the highest consensus score is selected as a candidate model. For the QA problem, MDS-QA combines single-model scoring functions with consensus to determine more accurate assessment score for every model in a given pool. Using extensive benchmark sets of a large collection of predicted models, we compare the two algorithms with existing state-of-the-art quality assessment methods and show significant improvement.

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Protein model quality assessment using 3D oriented convolutional neural networks

10.1101/432146 ◽

2018 ◽

Cited By ~ 1

Author(s):

Guillaume Pagès ◽

Benoit Charmettant ◽

Sergei Grudinin

Keyword(s):

Neural Networks ◽

Quality Assessment ◽

Convolutional Neural Networks ◽

Single Model ◽

Model Quality ◽

Model Quality Assessment ◽

Density Maps ◽

Protein Model ◽

Protein Model Quality Assessment ◽

3D Cnn

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.

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Improved protein model quality assessment by integrating sequential and pairwise features using deep learning

10.1101/2020.09.30.321661 ◽

2020 ◽

Author(s):

Xiaoyang Jing ◽

Jinbo Xu

Keyword(s):

Neural Networks ◽

Quality Assessment ◽

Single Model ◽

Model Quality ◽

Protein Targets ◽

Model Quality Assessment ◽

Global Quality ◽

Protein Model ◽

Protein Model Quality Assessment ◽

Experimental Structure

AbstractMotivationAccurately estimating protein model quality in the absence of experimental structure is not only important for model evaluation and selection, but also useful for model refinement. Progress has been steadily made by introducing new features and algorithms (especially deep neural networks), but accuracy of quality assessment (QA) is still not very satisfactory, especially local QA on hard protein targets.ResultsWe propose a new single-model-based QA method ResNetQA for both local and global quality assessment. Our method predicts model quality by integrating sequential and pairwise features using a deep neural network composed of both 1D and 2D convolutional residual neural networks (ResNet). The 2D ResNet module extracts useful information from pairwise features such as model-derived distance maps, co-evolution information and predicted distance potential. The 1D ResNet is used to predict local (global) model quality from sequential features and pooled pairwise information generated by 2D ResNet. Tested on the CASP12 and CASP13 datasets, our experimental results show that our method greatly outperforms existing state-of-the-art methods. Our ablation studies indicate that the 2D ResNet module and pairwise features play an important role in improving model quality assessment.Availability and Implementationhttps://github.com/AndersJing/[email protected]

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