PXD028601

PXD028601 is an original dataset announced via ProteomeXchange.

Title	DeepPhospho generated libraries, search results, models, and synthetic data
Description	In this study, we present a hybrid deep neural network DeepPhospho which conceptually differs from all previous deep learning models for unmodified or modified peptide predictions in regard to peptide representation learning. Our approach utilizes a multi-module network and self attention mechanism to learn a highly expressive peptide representation, yielding more accurate predictions. When evaluated with multiple phosphoproteomics datasets acquired by DIA or DDA methods, DeepPhospho surpasses existing benchmarks and tools in the prediction of fragmentation patterns for phosphopeptides. In certain cases, the large variance between a DeepPhospho predicted MSMS spectrum and an experimentally assigned spectrum revealed the latter was a false identification while the predicted spectrum closely mimics the bona fide spectrum. Moreover, accurate prediction of chromatographic retention time for any phosphopeptide sequence is integrated into DeepPhospho, which allows for convenient construction of in silico spectral libraries to enhance DIA phosphoproteomics data mining.
HostingRepository	iProX
AnnounceDate	2021-09-17
AnnouncementXML	Submission_2021-11-24_23:49:56.298.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Ronghui Lou
SpeciesList	scientific name: Homo sapiens; NCBI TaxID: 9606;
ModificationList	phosphorylated residue
Instrument	Q Exactive HF

Revision	Datetime	Status	ChangeLog Entry
0	2021-09-16 23:49:16	ID requested
1	2021-09-16 23:49:39	announced
⏵ 2	2021-11-24 23:49:57	announced	2021-11-25: Update publication information.

Lou R, Liu W, Li R, Li S, He X, Shui W, DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation. Nat Commun, 12(1):6685(2021) [pubmed]

submitter keyword: DeepPhospho

Wenqing Shui
contact affiliation	ShanghaiTech University
contact email	shuiwq@shanghaitech.edu.cn
lab head
Ronghui Lou
contact affiliation	ShanghaiTech University
contact email	lourh@shanghaitech.edu.cn
dataset submitter

iProX dataset URI