Functional analysis of the stable phosphoproteome reveals cancer vulnerabilities

Xiao, Di; Kim, Hani Jieun; Pang, Chi Nam Ignatius; Yang, Pengyi

doi:10.1093/bioinformatics/btac015

Cited by 3 publications

(4 citation statements)

References 53 publications

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“…PTMsea [41] Time-course kinase activity analysis CLUE [42] Time-course phospho-event ordering Minardo [43] Data processing & Funcational analysis Comprehensive suite PhosR [9], Perseus [44] Utilising mutation information HotPho [45], MIMP [46] Characterisation Utilising evolutionary information Strumillo et al [47] Utilising structural information Betts et al [48] Integration of multiple features SAPH-ire [49], SAPH-ire TFx [50], Beltrao et al [51], Ochoa et al [52], Xiao et al [53] Integration ESC differentiation Yang et al [54], AdaEnsemble [55] HRAS signaling MiNETi [56] Yeast pheromone response; Glioblastoma multiforme PSC Forest algorithm [57] Non-small cell lung cancer Balbin et al [58] Renal cell carcinoma drug targets COSMOS [59] Prostate cancer drug targets TieDIE [60] genesis, MaxQuant and Proteios [71]. The presence of missing values significantly affects the completeness of the data and distorts the biological signal.…”

Section: Specific Application Method/studymentioning

confidence: 99%

“…Finally, while most studies focus on the dynamics of phosphorylation, the phosphoproteome that is stable and common across cell tissue types was found to be also functional and its disruptions were linked to cancer development [53]. These works together demonstrate the complexity of the phosphoproteome and the power of integrative analyses in their characterisation.…”

Section: Characterising the Phosphoproteome Using Databases And Other...mentioning

confidence: 95%

“…They further demonstrated that this score was capable of accurately identifying regulatory phosphosites for a wide array of processes and predicting the effect of deleterious mutations. Finally, while most studies focus on the dynamics of phosphorylation, the phosphoproteome that is stable and common across cell tissue types was found to be also functional and its disruptions were linked to cancer development [53]. These works together demonstrate the complexity of the phosphoproteome and the power of integrative analyses in their characterisation.…”

Section: Characterising the Phosphoproteome Using Databases And Other...mentioning

confidence: 99%

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Computational systems approach towards phosphoproteomics and their downstream regulation

2022

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Protein phosphorylation plays an essential role in modulating cell signalling and its downstream transcriptional and translational regulations. Until recently, protein phosphorylation has been studied mostly using low‐throughput biochemical assays. The advancement of mass spectrometry (MS)‐based phosphoproteomics transformed the field by enabling measurement of proteome‐wide phosphorylation events, where tens of thousands of phosphosites are routinely identified and quantified in an experiment. This has brought a significant challenge in analysing large‐scale phosphoproteomic data, making computational methods and systems approaches integral parts of phosphoproteomics. Previous works have primarily focused on reviewing the experimental techniques in MS‐based phosphoproteomics, yet a systematic survey of the computational landscape in this field is still missing. Here, we review computational methods and tools, and systems approaches that have been developed for phosphoproteomics data analysis. We categorise them into four aspects including data processing, functional analysis, phosphoproteome annotation and their integration with other omics, and in each aspect, we discuss the key methods and example studies. Lastly, we highlight some of the potential research directions on which future work would make a significant contribution to this fast‐growing field. We hope this review provides a useful snapshot of the field of computational systems phosphoproteomics and stimulates new research that drives future development.

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Section: Specific Application Method/studymentioning

confidence: 99%

Section: Characterising the Phosphoproteome Using Databases And Other...mentioning

confidence: 95%

Section: Characterising the Phosphoproteome Using Databases And Other...mentioning

confidence: 99%

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Computational systems approach towards phosphoproteomics and their downstream regulation

2022

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“…Missing values in the retained phosphosites were imputed first by a site-and sample condition-specific imputation method, where for a phosphosite that contains missing values in a condition, if more than three samples were quantified in that condition, the missing values were imputed based on these quantified values for that phosphosite in that condition, and then by a random-tail imputation method (Kim et al, 2021b). The imputed data were normalized using 'Combat' function in sva package (Johnson et al, 2007) for removing batch effects and then 'RUVphospho' function in PhosR for the removal of additional unwanted variation with a set of stably phosphorylated sites as negative controls (Xiao et al, 2022). The batch-corrected data were further converted to ratios relative to the preexercise samples (i.e., '0 min' controls).…”

Section: Bioinformatic Analysismentioning

confidence: 99%

Phosphoproteomics uncovers exercise intensity-specific signaling networks underlying high-intensity interval training in human skeletal muscle

Hoffman,

Whitfield,

Xiao

et al. 2024

Preprint

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SUMMARYIn response to exercise, protein kinases and signaling networks are rapidly engaged in skeletal muscle to maintain energy homeostasis. High-intensity interval training (HIIT) induces superior or similar health-promoting skeletal muscle and whole-body adaptations compared to prolonged, moderate-intensity continuous training (MICT). However, the exercise intensity-specific signaling pathways underlying HIIT versus MICT are unknown. Ten healthy male participants completed bouts of work- and duration-matched HIIT and MICT cycling in randomized crossover trials. Mass spectrometry-based phosphoproteomic analysis of human muscle biopsies mapped acute signaling responses to HIIT and MICT, identifying 14,931 phosphopeptides and 8,509 phosphosites. Bioinformatics uncovered >1,000 phosphosites significantly regulated by HIIT and/or MICT, including 92 and 348 respective HIIT-specific phosphosites after 5 and 10 min and >3,000 total phosphosites significantly correlated with plasma lactate. This first human muscle HIIT signaling network map has revealed rapid exercise intensity-specific regulation of kinases, substrates and pathways that may contribute to HIIT’s unique health-promoting effects.

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SnapKin: a snapshot deep learning ensemble for kinase-substrate prediction from phosphoproteomics data

Xiao,

Lin,

Liu

et al. 2023

NAR Genomics and Bioinformatics

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A major challenge in mass spectrometry-based phosphoproteomics lies in identifying the substrates of kinases, as currently only a small fraction of substrates identified can be confidently linked with a known kinase. Machine learning techniques are promising approaches for leveraging large-scale phosphoproteomics data to computationally predict substrates of kinases. However, the small number of experimentally validated kinase substrates (true positive) and the high data noise in many phosphoproteomics datasets together limit their applicability and utility. Here, we aim to develop advanced kinase-substrate prediction methods to address these challenges. Using a collection of seven large phosphoproteomics datasets, and both traditional and deep learning models, we first demonstrate that a ‘pseudo-positive’ learning strategy for alleviating small sample size is effective at improving model predictive performance. We next show that a data resampling-based ensemble learning strategy is useful for improving model stability while further enhancing prediction. Lastly, we introduce an ensemble deep learning model (‘SnapKin’) by incorporating the above two learning strategies into a ‘snapshot’ ensemble learning algorithm. We propose SnapKin, an ensemble deep learning method, for predicting substrates of kinases from large-scale phosphoproteomics data. We demonstrate that SnapKin consistently outperforms existing methods in kinase-substrate prediction. SnapKin is freely available at https://github.com/PYangLab/SnapKin.

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Functional analysis of the stable phosphoproteome reveals cancer vulnerabilities

Cited by 3 publications

References 53 publications

Computational systems approach towards phosphoproteomics and their downstream regulation

Computational systems approach towards phosphoproteomics and their downstream regulation

Phosphoproteomics uncovers exercise intensity-specific signaling networks underlying high-intensity interval training in human skeletal muscle

SnapKin: a snapshot deep learning ensemble for kinase-substrate prediction from phosphoproteomics data

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