Predicting disease-associated mutation of metal-binding sites in proteins using a deep learning approach-Reference-Cited by-同舟云学术

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

Published:2019-12 Issue:12 Volume:1 Page:561-567
ISSN:2522-5839
Container-title:Nature Machine Intelligence
language:en
Short-container-title:Nat Mach Intell

Author:

Koohi-Moghadam Mohamad^ORCID,Wang Haibo,Wang Yuchuan,Yang Xinming,Li Hongyan,Wang Junwen,Sun Hongzhe^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software

Link

http://www.nature.com/articles/s42256-019-0119-z.pdf

Reference38 articles.

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).

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).

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

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

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

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