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

Nature Machine Intelligence volume 1, pages 561–567 (2019)Cite this article

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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.

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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.

References

  1. Waldron, K. J. & Robinson, N. J. How do bacterial cells ensure that metalloproteins get the correct metal? Nat. Rev. Microbiol. 7, 25–35 (2009).

    Article  Google Scholar 

  2. Finney, L. A. & O’Halloran, T. V. Transition metal speciation in the cell: insights from the chemistry of metal ion receptors. Science 300, 931–936 (2003).

    Article  Google Scholar 

  3. Changela, A. et al. Molecular basis of metal-ion selectivity and zeptomolar sensitivity by CueR. Science 301, 1383–1387 (2003).

    Article  Google Scholar 

  4. Barnham, K. J. & Bush, A. I. Biological metals and metal-targeting compounds in major neurodegenerative diseases. Chem. Soc. Rev. 43, 6727–6749 (2014).

    Article  Google Scholar 

  5. Waldron, K. J., Rutherford, J. C., Ford, D. & Robinson, N. J. Metalloproteins and metal sensing. Nature 460, 823–830 (2009).

    Article  Google Scholar 

  6. Yang, X., Li, H., Lai, T. P. & Sun, H. UreE–UreG complex facilitates nickel transfer and preactivates GTPase of UreG in Helicobacter pylori. J. Biol. Chem. 290, 12474–12485 (2015).

    Article  Google Scholar 

  7. Yang, X. et al. Nickel translocation between metallochaperones HypA and UreE in Helicobacter pylori. Metallomics 6, 1731–1736 (2014).

    Article  Google Scholar 

  8. Zhao, M., Wang, H. B., Ji, L. N. & Mao, Z. W. Insights into metalloenzyme microenvironments: biomimetic metal complexes with a functional second coordination sphere. Chem. Soc. Rev. 42, 8360–8375 (2013).

    Article  Google Scholar 

  9. Mirts, E. N., Bhagi-Damodaran, A. & Lu, Y. Understanding and modulating metalloenzymes with unnatural amino acids, non-native metal ions, and non-native metallocofactors. Acc. Chem. Res. 52, 935–944 (2019).

    Article  Google Scholar 

  10. Lu, Y., Yeung, N., Sieracki, N. & Marshall, N. M. Design of functional metalloproteins. Nature 460, 855–862 (2009).

    Article  Google Scholar 

  11. Dudev, T. & Lim, C. Metal binding affinity and selectivity in metalloproteins: insights from computational studies. Annu. Rev. Biophys. 37, 97–116 (2008).

    Article  Google Scholar 

  12. Haas, K. L. & Franz, K. J. Application of metal coordination chemistry to explore and manipulate cell biology. Chem. Rev. 109, 4921–4960 (2009).

    Article  Google Scholar 

  13. Levy, R., Sobolev, V. & Edelman, M. First- and second-shell metal binding residues in human proteins are disproportionately associated with disease-related SNPs. Hum. Mutat. 32, 1309–1318 (2011).

    Article  Google Scholar 

  14. Jackson, S. P. & Bartek, J. The DNA-damage response in human biology and disease. Nature 461, 1071–1078 (2009).

    Article  Google Scholar 

  15. Chan, P. A. et al. Interpreting missense variants: comparing computational methods in human disease genes CDKN2A, MLH1, MSH2, MECP2, and tyrosinase (TYR). Hum. Mutat. 28, 683–693 (2007).

    Article  Google Scholar 

  16. Bao, L., Zhou, M. & Cui, Y. nsSNPAnalyzer: identifying disease-associated nonsynonymous single nucleotide polymorphisms. Nucleic Acids Res. 33, W480–W482 (2005).

    Article  Google Scholar 

  17. Bromberg, Y. & Rost, B. SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Res. 35, 3823–3835 (2007).

    Article  Google Scholar 

  18. Calabrese, R., Capriotti, E., Fariselli, P., Martelli, P. L. & Casadio, R. Functional annotations improve the predictive score of human disease-related mutations in proteins. Hum. Mutat. 30, 1237–1244 (2009).

    Article  Google Scholar 

  19. Thusberg, J., Olatubosun, A. & Vihinen, M. Performance of mutation pathogenicity prediction methods on missense variants. Hum. Mutat. 32, 358–368 (2011).

    Article  Google Scholar 

  20. Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods. 7, 248–249 (2010).

    Article  Google Scholar 

  21. Putignano, V., Rosato, A., Banci, L. & Andreini, C. MetalPDB in 2018: a database of metal sites in biological macromolecular structures. Nucleic Acids Res. 46, D459–D464 (2017).

    Article  Google Scholar 

  22. Gohlke, B. O., Nickel, J., Otto, R., Dunkel, M. & Preissner, R. CancerResource–updated database of cancer-relevant proteins, mutations and interacting drugs. Nucleic Acids Res. 44, D932–D937 (2016).

    Article  Google Scholar 

  23. Landrum, M. J. et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic. Acids. Res. 42, D980–D985 (2013).

    Article  Google Scholar 

  24. Wu, C. H. et al. The Universal Protein Resource (UniProt): an expanding universe of protein information. Nucleic Acids Res. 34, D187–D191 (2006).

    Article  Google Scholar 

  25. Pommié, C., Levadoux, S., Sabatier, R., Lefranc, G. & Lefranc, M. P. IMGT standardized criteria for statistical analysis of immunoglobulin V‐REGION amino acid properties. J. Mol. Recognit. 17, 17–32 (2004).

    Article  Google Scholar 

  26. Yarden, R. I., Pardo-Reoyo, S., Sgagias, M., Cowan, K. H. & Brody, L. C. BRCA1 regulates the G2/M checkpoint by activating Chk1 kinase upon DNA damage. Nat. Genet. 30, 285–289 (2002).

    Article  Google Scholar 

  27. Chenevix-Trench, G. et al. Genetic and histopathologic evaluation of BRCA1 and BRCA2 DNA sequence variants of unknown clinical significance. Cancer. Res. 66, 2019–2027 (2006).

    Article  Google Scholar 

  28. Kruse, J. P. & Gu, W. Modes of p53 regulation. Cell 137, 609–622 (2009).

    Article  Google Scholar 

  29. Bachinski, L. L. et al. Genetic mapping of a third Li-Fraumeni syndrome predisposition locus to human chromosome 1q23. Cancer Res. 65, 427–431 (2005).

    Google Scholar 

  30. Zenker, M. et al. Deficiency of UBR1, a ubiquitin ligase of the N-end rule pathway, causes pancreatic dysfunction, malformations and mental retardation (Johanson-Blizzard syndrome). Nat. Genet. 37, 1345–1350 (2005).

    Article  Google Scholar 

  31. Kwak, K. S. et al. Regulation of protein catabolism by muscle-specific and cytokine-inducible ubiquitin ligase E3alpha-II during cancer cachexia. Cancer Res. 64, 8193–8198 (2004).

    Article  Google Scholar 

  32. Runtuwene, V. et al. Noonan syndrome gain-of-function mutations in NRAS cause zebrafish gastrulation defects. Dis. Model. Mech. 4, 393–399 (2011).

    Article  Google Scholar 

  33. Monti, P. et al. Transcriptional functionality of germ line p53 mutants influences cancer phenotype. Clin. Cancer Res. 13, 3789–3795 (2007).

    Article  Google Scholar 

  34. Wang, Y., Wang, H., Li, H. & Sun, H. Metallomic and metalloproteomic strategies in elucidating the molecular mechanisms of metallodrugs. Dalton. Trans. 44, 437–447 (2015).

    Article  Google Scholar 

  35. Lipscomb, C. E. Medical subject headings (MeSH). Bull. Med. Libr. Assoc. 88, 265 (2000).

    Google Scholar 

  36. Morris, G. M. et al. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem. 19, 1639–1662 (1998).

    Article  Google Scholar 

  37. Cao, D. S., Xu, Q. S. & Liang, Y. Z. propy: a tool to generate various modes of Chou’s PseAAC. Bioinformatics 29, 960–962 (2013).

    Article  Google Scholar 

  38. Chollet, F. Keras. GitHub https://github.com/keras-team/keras (2015).

Download references

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.

Author information

Authors and Affiliations

  1. Department of Chemistry, The University of Hong Kong, Hong Kong, China

    Mohamad Koohi-Moghadam, Haibo Wang, Yuchuan Wang, Xinming Yang, Hongyan Li & Hongzhe Sun

  2. Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ, USA

    Mohamad Koohi-Moghadam & Junwen Wang

  3. Department of Health Sciences, Mayo Clinic, Scottsdale, AZ, USA

    Junwen Wang

  4. Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Scottsdale, AZ, USA

    Junwen Wang

  5. College of Health Solutions, Arizona State University, Scottsdale, AZ, USA

    Junwen Wang

Authors
  1. Mohamad Koohi-Moghadam
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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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