Nphos: Database and Predictor of Protein N-phosphorylation.

Zhao M-X; Ding R-F; Chen Q; Meng J; Li F; Fu S; Huang B; Liu Y; Ji Z-L; Zhao Y

Nphos: Database and Predictor of Protein N-phosphorylation.

dc.citation.issue	3
dc.citation.volume	22
dc.contributor.author	Zhao M-X
dc.contributor.author	Ding R-F
dc.contributor.author	Chen Q
dc.contributor.author	Meng J
dc.contributor.author	Li F
dc.contributor.author	Fu S
dc.contributor.author	Huang B
dc.contributor.author	Liu Y
dc.contributor.author	Ji Z-L
dc.contributor.author	Zhao Y
dc.contributor.editor	Xue Y
dc.coverage.spatial	England
dc.date.accessioned	2024-11-06T19:02:43Z
dc.date.available	2024-11-06T19:02:43Z
dc.date.issued	2024-04-10
dc.description.abstract	Protein N-phosphorylation is widely present in nature and participates in various biological processes. However, current knowledge on N-phosphorylation is extremely limited compared to that on O-phosphorylation. In this study, we collected 11,710 experimentally verified N-phosphosites of 7344 proteins from 39 species and subsequently constructed the database Nphos to share up-to-date information on protein N-phosphorylation. Upon these substantial data, we characterized the sequential and structural features of protein N-phosphorylation. Moreover, after comparing hundreds of learning models, we chose and optimized gradient boosting decision tree (GBDT) models to predict three types of human N-phosphorylation, achieving mean area under the receiver operating characteristic curve (AUC) values of 90.56%, 91.24%, and 92.01% for pHis, pLys, and pArg, respectively. Meanwhile, we discovered 488,825 distinct N-phosphosites in the human proteome. The models were also deployed in Nphos for interactive N-phosphosite prediction. In summary, this work provides new insights and points for both flexible and focused investigations of N-phosphorylation. It will also facilitate a deeper and more systematic understanding of protein N-phosphorylation modification by providing a data and technical foundation. Nphos is freely available at http://www.bio-add.org/Nphos/ and http://ppodd.org.cn/Nphos/.
dc.description.confidential	false
dc.edition.edition	June 2024
dc.format.pagination	qzae032-
dc.identifier.author-url	https://www.ncbi.nlm.nih.gov/pubmed/39380205
dc.identifier.citation	Zhao M-X, Ding R-F, Chen Q, Meng J, Li F, Fu S, Huang B, Liu Y, Ji Z-L, Zhao Y. (2024). Nphos: Database and Predictor of Protein N-phosphorylation.. Genomics Proteomics Bioinformatics. 22. 3. (pp. qzae032-).
dc.identifier.doi	10.1093/gpbjnl/qzae032
dc.identifier.eissn	2210-3244
dc.identifier.elements-type	journal-article
dc.identifier.issn	1672-0229
dc.identifier.number	qzae032
dc.identifier.pii	7643521
dc.identifier.uri	https://mro.massey.ac.nz/handle/10179/71933
dc.language	eng
dc.publisher	Oxford University Press
dc.publisher.uri	https://academic.oup.com/gpb/article/22/3/qzae032/7643521
dc.relation.isPartOf	Genomics Proteomics Bioinformatics
dc.rights	(c) 2024 The Author/s
dc.rights	CC BY 4.0
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/
dc.subject	N-phosphorylation
dc.subject	Benchmark dataset
dc.subject	Database
dc.subject	Machine learning
dc.subject	Post-translational modification
dc.subject	Phosphorylation
dc.subject	Databases, Protein
dc.subject	Humans
dc.subject	Phosphoproteins
dc.subject	Proteome
dc.title	Nphos: Database and Predictor of Protein N-phosphorylation.
dc.type	Journal article
pubs.elements-id	491832
pubs.organisational-group	College of Health