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- Publisher Website: 10.1038/s42256-019-0119-z
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Article: Predicting disease-associated mutation of metal-binding sites in proteins using a deep learning approach
Title | Predicting disease-associated mutation of metal-binding sites in proteins using a deep learning approach |
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Authors | |
Issue Date | 2019 |
Publisher | Nature Research (part of Springer Nature). The Journal's web site is located at https://www.nature.com/natmachintell/ |
Citation | Nature Machine Intelligence, 2019, v. 1, p. 561-567 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/279974 |
ISSN | 2023 Impact Factor: 18.8 2023 SCImago Journal Rankings: 5.940 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Koohi-Moghadam, M | - |
dc.contributor.author | Wang, H | - |
dc.contributor.author | Wang, Y | - |
dc.contributor.author | Yang, X | - |
dc.contributor.author | Li, H | - |
dc.contributor.author | Wang, J | - |
dc.contributor.author | Sun, H | - |
dc.date.accessioned | 2019-12-23T08:24:27Z | - |
dc.date.available | 2019-12-23T08:24:27Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Nature Machine Intelligence, 2019, v. 1, p. 561-567 | - |
dc.identifier.issn | 2522-5839 | - |
dc.identifier.uri | http://hdl.handle.net/10722/279974 | - |
dc.description.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. | - |
dc.language | eng | - |
dc.publisher | Nature Research (part of Springer Nature). The Journal's web site is located at https://www.nature.com/natmachintell/ | - |
dc.relation.ispartof | Nature Machine Intelligence | - |
dc.title | Predicting disease-associated mutation of metal-binding sites in proteins using a deep learning approach | - |
dc.type | Article | - |
dc.identifier.email | Wang, H: wanghaib@hku.hk | - |
dc.identifier.email | Li, H: hylichem@hku.hk | - |
dc.identifier.email | Sun, H: hsun@hku.hk | - |
dc.identifier.authority | Sun, H=rp00777 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1038/s42256-019-0119-z | - |
dc.identifier.hkuros | 308801 | - |
dc.identifier.volume | 1 | - |
dc.identifier.spage | 561 | - |
dc.identifier.epage | 567 | - |
dc.identifier.isi | WOS:000571267000007 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 2522-5839 | - |