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Predicting disease-associated mutation of metal-binding sites in proteins using a deep learning approach

Abstract

Metalloproteins play important roles in many biological processes. Mutations at the metal-binding sites may functionally disrupt metalloproteins, initiating severe diseases; however, there seemed to be no effective approach to predict such mutations until now. Here we develop a deep learning approach to successfully predict disease-associated mutations that occur at the metal-binding sites of metalloproteins. We generate energy-based affinity grid maps and physiochemical features of the metal-binding pockets (obtained from different databases as spatial and sequential features) and subsequently implement these features into a multichannel convolutional neural network. After training the model, the multichannel convolutional neural network can successfully predict disease-associated mutations that occur at the first and second coordination spheres of zinc-binding sites with an area under the curve of 0.90 and an accuracy of 0.82. Our approach stands for the first deep learning approach for the prediction of disease-associated metal-relevant site mutations in metalloproteins, providing a new platform to tackle human diseases.

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Fig. 1: Workflow of data collection and feature extraction to train the deep learning model.
Fig. 2: A proportional graph of disease-associated mutations of the metal-binding pocket.
Fig. 3: Performance of MCCNN to predict disease-associated mutations.
Fig. 4: MCCNN can accurately predict disease-associated site mutations in both the first and second coordination spheres of metals in metalloproteins.

Data availability

The disease-associated and benign mutations data have been attached as supporting tables. The implemented model and spatial and sequential features for training the model is available in BitBucket code repository: https://bitbucket.org/mkoohim/multichannel-cnn.

Code availability

The implemented model of MCCNN is publicly available in BitBucket repository under GPL v3.0 license: https://bitbucket.org/mkoohim/multichannel-cnn.

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Acknowledgements

We thank the Research Grants Council of Hong Kong (grant nos. 17307017P and R7070-18), the National Science Foundation of China (grant no. 21671203), the University of Hong Kong (for a studentship for M.K. and a Norman and Cecilia Yip Foundation for H.S.) and the Hong Kong PhD Fellowship (HKPF for H.W.) for support. A startup fund from the Mayo Clinic Arizona, Mayo Clinic Center for Individualized Medicine and Mayo Clinic Cancer Center (grant no. P30CA015083-45 for M.K. and J.W.) is acknowledged for support. We thank G.H. Chen (University of Hong Kong) and X.H. Xia (University of Ottawa) for helpful comments.

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Authors

Contributions

For the work described herein, M.K. designed and implemented the pipeline. M.K., H.W., Y.W., X.Y. and H.L. performed data intergradation and result validation. M.K., H.W., H.L. and H.S. wrote the paper. Y.W., X.Y. and J.W. commented on and edited the manuscript. J.W. and H.S. provided overall project leadership.

Corresponding authors

Correspondence to Junwen Wang or Hongzhe Sun.

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The authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary figs., tables and notes

Supplementary Table 1

Disease-associated mutations of metal-binding pocket

Supplementary Table 2

Benign mutations of metal-binding pocket

Supplementary Table 9

The prediction result of the unseen data

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Koohi-Moghadam, M., Wang, H., Wang, Y. et al. Predicting disease-associated mutation of metal-binding sites in proteins using a deep learning approach. Nat Mach Intell 1, 561–567 (2019). https://doi.org/10.1038/s42256-019-0119-z

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