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Fast and effective protein model refinement using deep graph neural networks

Nature Computational Science volume 1, pages 462–469 (2021)Cite this article

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A preprint version of the article is available at bioRxiv.

Abstract

Protein model refinement is the last step applied to improve the quality of a predicted protein model. Currently, the most successful refinement methods rely on extensive conformational sampling and thus take hours or days to refine even a single protein model. Here, we propose a fast and effective model refinement method that applies graph neural networks (GNNs) to predict a refined inter-atom distance probability distribution from an initial model and then rebuilds three-dimensional models from the predicted distance distribution. Tested on the Critical Assessment of Structure Prediction refinement targets, our method has an accuracy that is comparable to those of two leading human groups (FEIG and BAKER), but runs substantially faster. Our method may refine one protein model within ~11 min on one CPU, whereas BAKER needs ~30 h on 60 CPUs and FEIG needs ~16 h on one GPU. Finally, our study shows that GNN outperforms ResNet (convolutional residual neural networks) for model refinement when very limited conformational sampling is allowed.

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Fig. 1: GNNRefine for protein model refinement.
Fig. 2: Successful examples of refinement by GNNRefine.
Fig. 3: Running times of GNNRefine and DeepAccNet on CASP14 targets with respect to protein sequence length.

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Data availability

Our in-house data are available at http://raptorx.uchicago.edu/download/. Click on this link and fill in your name, email address and organization name to obtain a data link, through which you will find a text file 0README.Data4GNNRefine.txt that specifies the names of the data files to be downloaded. The data are also available at Zenodo38. The DeepAccNet data are available at https://github.com/hiranumn/DeepAccNet. The CASP13 and CASP14 models for refinement are available at https://predictioncenter.org/. The CAMEO models are available at https://www.cameo3d.org/modeling/. The CAMEO dataset includes 208 starting models for all the CAMEO hard targets released between 1 May 2018 and 1 May 2020. We keep only the targets with sequence length in [50, 500] and native structures containing at least 80% of sequence residues. Following CASPs, we select the best-predicted models (in terms of GDT-HA) for each target as the starting models, and only keep the starting models with lDDT > 50. For the CASP13 FM (free-modeling) dataset, there are 28 test targets corresponding to 32 official FM domains. For each target we build ~150 decoys as its starting models using our in-house template-free modeling software RaptorX-Contact. Source data are provided with this paper.

Code availability

The source code is available at Code Ocean39.

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Acknowledgements

We thank D. Baker’s team, including H. Park, who provided us with the DeepAccNet training data and helpful comments on our manuscript. We are also grateful to L. Heo for explaining FEIG and FEIG-S to us. This work is supported by National Institutes of Health grant no. R01GM089753 to J.X. and National Science Foundation grant no. DBI1564955 to J.X. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Toyota Technological Institute at Chicago, Chicago, IL, USA

    Xiaoyang Jing & Jinbo Xu

Authors
  1. Xiaoyang Jing
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  2. Jinbo Xu
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Contributions

X.J. conceived the research, developed the GNNRefine and carried out the benchmarking experiments. J.X. built the in-house training data and guided the research. X.J. and J.X. analyzed the results and wrote the manuscript.

Corresponding author

Correspondence to Jinbo Xu.

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The authors declare no competing interests.

Additional information

Peer review information Nature Computational Science thanks Hahnbeom Park, Lim Heo and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Handling editor: Jie Pan, in collaboration with the Nature Computational Science team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Extended Data Fig. 1 Quality improvement by different methods on the CASP13 refinement targets.

Boxplot of the distribution of ΔGDT-HA, ΔGDT-TS, and ΔlDDT on the CASP13 refinement targets. The five lines in each boxplot from top to bottom in turn mean: Maximum (Q3 + 1.5IQR), Third quartile (Q3, 75th percentile), Median (50th percentile), First quartile (Q1, 25th percentile), and Minimum (Q1-1.5IQR), where IQR is Q3-Q1. The precision is 2.

Source data

Extended Data Fig. 2 Quality improvement by different methods on the CASP14 refinement targets.

Box plot of the distribution of ΔGDT-HA, ΔGDT-TS, and ΔlDDT on the CASP14 refinement targets.The five lines in each boxplot from top to bottom in turn mean: Maximum (Q3 + 1.5IQR), Third quartile (Q3, 75th percentile), Median (50th percentile), First quartile (Q1, 25th percentile), and Minimum (Q1-1.5IQR), where IQR is Q3-Q1. The precision is 2.

Source data

Supplementary information

Supplementary Information

Supplementary sections 1–12, Figs. 1–6 and Tables 1–23.

Source data

Source Data Fig. 2

The PDB structure files for Fig. 2.

Source Data Fig. 3

The running time data for GNNRefine and DeepAccNet.

Source Data Extended Data Fig. 1

The source data used to draw the boxplot of Extended Data Fig. 1.

Source Data Extended Data Fig. 2

The source data used to draw the boxplot of Extended Data Fig. 2.

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Jing, X., Xu, J. Fast and effective protein model refinement using deep graph neural networks. Nat Comput Sci 1, 462–469 (2021). https://doi.org/10.1038/s43588-021-00098-9

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