DeepPhospho generated libraries, search results, models, and synthetic data

ABSTRACT: 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.

ORGANISM(S): Homo Sapiens

SUBMITTER: Wenqing Shui  

PROVIDER: PXD028601 | iProX | Fri Sep 17 00:00:00 BST 2021

REPOSITORIES: iProX