The DOI matches its Crossref record; 107 of the 107 references it cites were found in a registry or an index.
The functional landscape of the human phosphoproteome
Open accessPublished version of a preprint
Abstract
Protein phosphorylation is a key post-translational modification regulating protein function in almost all cellular processes. Although tens of thousands of phosphorylation sites have been identified in human cells, approaches to determine the functional importance of each phosphosite are lacking. Here, we manually curated 112 datasets of phospho-enriched proteins, generated from 104 different human cell types or tissues. We re-analyzed the 6,801 proteomics experiments that passed our quality control criteria, creating a reference phosphoproteome containing 119,809 human phosphosites. To prioritize functional sites, we used machine learning to identify 59 features indicative of proteomic, structural, regulatory or evolutionary relevance and integrate them into a single functional score. Our approach identifies regulatory phosphosites across different molecular mechanisms, processes and diseases, and reveals genetic susceptibilities at a genomic scale. Several regulatory phosphosites were experimentally validated, including identifying a role in neuronal differentiation for phosphosites in SMARCC2, a member of the SWI/SNF chromatin-remodeling complex.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The inferred transcription factors and kinases indicate the well-annotated players in the DNA damage response, whereas proteins with altered thermal stability reflect less annotated parts of the signaling space with strong links to the phosphoproteomic measurements which aligns with observations linking functionality of phosphosites with thermal stability (Ochoa et al, 2020; Potel et al, 2021).”
From Network integration of thermal proteome profiling with multi-omics data decodes PARP inhibition (2024) · cites this paper as Ochoa et al, 2020
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
Hartkopf, A. D., Taran, F., Wallwiener, M., Walter, C. B., Krämer, B., Grischke, E., & Brucker, S. Y. (2016) · Breast Care · doi:10.1159/000453569 · Cited by 30,989
HISAT: a fast spliced aligner with low memory requirements
Kim, D., Langmead, B., & Salzberg, S. L. (2015) · Nature Methods · doi:10.1038/nmeth.3317 · Cited by 24,112
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464
Cox, J., & Mann, M. (2008) · Nature Biotechnology · doi:10.1038/nbt.1511 · Cited by 14,855
The Molecular Signatures Database Hallmark Gene Set Collection
Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015) · Cell Systems · doi:10.1016/j.cels.2015.12.004 · Cited by 13,807
Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
Edgar, R. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.207 · Cited by 13,288
Nishimura, R., Osako, T., Nishiyama, Y., Tashima, R., Nakano, M., Fujisue, M., Toyozumi, Y., & Arima, N. (2013) · Oncology · doi:10.1159/000353099 · Cited by 13,239
Comprehensive molecular portraits of human breast tumours
The Cancer Genome Atlas Network (2012) · Nature · doi:10.1038/nature11412 · Cited by 12,129
Cited by
Sorted by citations
The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences
Perez-Riverol, Y., Bai, J., Bandla, C., García-Seisdedos, D., Hewapathirana, S., Kamatchinathan, S., Kundu, D. J., Prakash, A., Frericks-Zipper, A., Eisenacher, M., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1038 · Cited by 6,125
Self-citationThe Global Phosphorylation Landscape of SARS-CoV-2 Infection
Bouhaddou, M., Memon, D., Meyer, B., White, K. M., Rezelj, V. V., Marrero, M. C., Polacco, B. J., Melnyk, J. E., Ulferts, S., Kaake, R. M., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.034 · Cited by 1,018
Self-citationThe ProteomeXchange consortium at 10 years: 2023 update
Deutsch, E. W., Bandeira, N., Pérez‐Riverol, Y., Sharma, V., Carver, J., Mendoza, L., Kundu, D. J., Wang, S., Bandla, C., Kamatchinathan, S., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1040 · Cited by 830
Self-citationControl of protein stability by post-translational modifications
Lee, J. M., Hammarén, H. M., Savitski, M. M., & Baek, S. H. (2023) · Nature Communications · doi:10.1038/s41467-023-35795-8 · Cited by 896
Self-citationAn atlas of substrate specificities for the human serine/threonine kinome
Johnson, J. L., Yaron, T. M., Huntsman, E. M., Kerelsky, A., Song, J., Regev, A., Lin, T.-Y., Liberatore, K., Cizin, D. M., Cohen, B. M., et al. (2023) · Nature · doi:10.1038/s41586-022-05575-3 · Cited by 728
The emerging role of mass spectrometry-based proteomics in drug discovery
Meissner, F., Geddes‐McAlister, J., Mann, M., & Bantscheff, M. (2022) · Nature Reviews Drug Discovery · doi:10.1038/s41573-022-00409-3 · Cited by 373
Mass-spectrometry-based proteomics: from single cells to clinical applications
Guo, T., Steen, J. A. J., & Mann, M. (2025) · Nature · doi:10.1038/s41586-025-08584-0 · Cited by 362
Skowronek, P., Thielert, M., Voytik, E., Tanzer, M. C., Hansen, F. M., Willems, S., Karayel, Ö., Brunner, A., Meier, F., & Mann, M. (2022) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2022.100279 · Cited by 305
Transcription factors: Bridge between cell signaling and gene regulation
Weidemüller, P., Kholmatov, M. M., Petsalaki, E., & Zaugg, J. B. (2021) · PROTEOMICS · doi:10.1002/pmic.202000034 · Cited by 295
Towards a structurally resolved human protein interaction network
Burke, D. F., Bryant, P., Barrio-Hernandez, I., Memon, D., Pozzati, G., Shenoy, A., Zhu, W., Dunham, A. S., Albanese, P., Keller, A., et al. (2023) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00910-8 · Cited by 269
Thermal proteome profiling for interrogating protein interactions
Mateus, A., Kurzawa, N., Becher, I., Sridharan, S., Helm, D., Stein, F., Typas, A., & Savitski, M. M. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199232 · Cited by 258
Self-citationInsulin signalling and GLUT4 trafficking in insulin resistance
van Gerwen, J., Shun-Shion, A. S., & Fazakerley, D. J. (2023) · Biochemical Society Transactions · doi:10.1042/bst20221066 · Cited by 277
Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry
Jiang, Y., Balaya, R. D. A., Schuster, D., Neely, B. A., Rosano, G. L., Volkmar, N., Momenzadeh, A., Peters-Clarke, T. M., Egbert, S., Kreimer, S. D., et al. (2024) · ACS Measurement Science Au · doi:10.1021/acsmeasuresciau.3c00068 · Cited by 217
Exploiting vulnerabilities of SWI/SNF chromatin remodelling complexes for cancer therapy
Wanior, M., Krämer, A., Knapp, S., & Joerger, A. C. (2021) · Oncogene · doi:10.1038/s41388-021-01781-x · Cited by 162
In-depth and 3-dimensional exploration of the budding yeast phosphoproteome
Lanz, M. C., Yugandhar, K., Gupta, S., Sanford, E. J., Faça, V. M., Vega, S., Joiner, A. M. N., Fromme, J. C., Yu, H., & Smolka, M. B. (2021) · EMBO Reports · doi:10.15252/embr.202051121 · Cited by 169
Discovering the landscape of protein modifications
Keenan, E. K., Zachman, D. K., & Hirschey, M. D. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.03.015 · Cited by 160
Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023
Lou, R., & Shui, W. (2024) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2024.100712 · Cited by 168
The intrinsic substrate specificity of the human tyrosine kinome
Yaron-Barir, T. M., Joughin, B. A., Huntsman, E. M., Kerelsky, A., Cizin, D. M., Cohen, B. M., Regev, A., Song, J., Vasan, N., Lin, T.-Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07407-y · Cited by 189
Wen, B., Zeng, W., Liao, Y., Shi, Z., Savage, S. R., Jiang, W., & Zhang, B. (2020) · PROTEOMICS · doi:10.1002/pmic.201900335 · Cited by 160
PaxDb 5.0: Curated Protein Quantification Data Suggests Adaptive Proteome Changes in Yeasts
Huang, Q. D., Szklarczyk, D., Wang, M., Simonovic, M., & von Mering, C. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100640 · Cited by 131
In-Cell Structural Biology by NMR: The Benefits of the Atomic Scale
Theillet, F. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00937 · Cited by 130
The structural context of posttranslational modifications at a proteome-wide scale
Bludau, I., Willems, S., Zeng, W., Strauss, M. T., Hansen, F. M., Tanzer, M. C., Karayel, Ö., Schulman, B. A., & Mann, M. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001636 · Cited by 127
Decrypting drug actions and protein modifications by dose- and time-resolved proteomics
Zecha, J., Bayer, F. P., Wiechmann, S., Woortman, J., Berner, N., Müller, J., Schneider, A., Kramer, K., Abril-Gil, M., Hopf, T. A., et al. (2023) · Science · doi:10.1126/science.ade3925 · Cited by 115
Advances in targeting the WNT/β-catenin signaling pathway in cancer
Chatterjee, A., Paul, S., Bisht, B., Bhattacharya, S., Sivasubramaniam, S., & Paul, M. K. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.07.007 · Cited by 116
Blazev, R., Carl, C. S., Ng, Y., Molendijk, J., Voldstedlund, C. T., Zhao, Y., Xiao, D., Kueh, A. J., Miotto, P. M., Haynes, V. R., et al. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2022.07.003 · Cited by 113
SARS-CoV-2 variants evolve convergent strategies to remodel the host response
Bouhaddou, M., Reuschl, A., Polacco, B. J., Thorne, L., Ummadi, M. R., Ye, C., Rosales, R., Pelin, A., Batra, J., Jang, G. Μ., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.026 · Cited by 120
Self-citationThe Role and Activity of SWI/SNF Chromatin Remodelers
Bieluszewski, T., Prakash, S., Roulé, T., & Wagner, D. (2023) · Annual Review of Plant Biology · doi:10.1146/annurev-arplant-102820-093218 · Cited by 106
Impact of phosphorylation on thermal stability of proteins
Potel, C. M., Kurzawa, N., Becher, I., Typas, A., Mateus, A., & Savitski, M. M. (2021) · Nature Methods · doi:10.1038/s41592-021-01177-5 · Cited by 103
Self-citationGlobal and Site-Specific Effect of Phosphorylation on Protein Turnover
Wu, C., Ba, Q., Lu, D., Li, W., Šalovská, B., Hou, P., Mueller, T., Rosenberger, G., Gao, E., Di, Y., et al. (2020) · Developmental Cell · doi:10.1016/j.devcel.2020.10.025 · Cited by 97
Personalized phosphoproteomics identifies functional signaling
Needham, E. J., Hingst, J. R., Parker, B. L., Morrison, K. R., Yang, G., Onslev, J., Kristensen, J. M., Højlund, K., Ling, N. X. Y., Oakhill, J. S., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-01099-9 · Cited by 95
Insulin signaling regulates longevity through protein phosphorylation in Caenorhabditis elegans
Li, W.-J., Wang, C., Li, T., Yan, Y.-H., Zhang, M.-J., Liu, Z., Li, Y.-X., Zhao, H., Li, X.-M., He, X.-D., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24816-z · Cited by 86
Nelson, M. E., Parker, B. L., Burchfield, J. G., Hoffman, N. J., Needham, E. J., Cooke, K. C., Naim, T., Sylow, L., Ling, N. X. Y., Francis, D., et al. (2019) · The EMBO Journal · doi:10.15252/embj.2019102578 · Cited by 81
Suskiewicz, M. J. (2024) · BioEssays · doi:10.1002/bies.202300178 · Cited by 91
Instrumentation at the Leading Edge of Proteomics
Peters-Clarke, T. M., Coon, J. J., & Riley, N. M. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.3c04497 · Cited by 89
Determining the ERK-regulated phosphoproteome driving KRAS-mutant cancer
Klomp, J. E., Diehl, J. N., Klomp, J. A., Edwards, A. C., Yang, R., Morales, A. J., Taylor, K. E., Drizyte-Miller, K., Bryant, K. L., Schaefer, A., et al. (2024) · Science · doi:10.1126/science.adk0850 · Cited by 83
Proteome‐scale mapping of binding sites in the unstructured regions of the human proteome
Benz, C., Ali, M., Krystkowiak, I., Simonetti, L., Sayadi, A., Mihalič, F., Kliche, J., Andersson, E., Jemth, P., Davey, N. E., et al. (2022) · Molecular Systems Biology · doi:10.15252/msb.202110584 · Cited by 86
Fazakerley, D. J., van Gerwen, J., Cooke, K. C., Duan, X., Needham, E. J., Díaz‐Vegas, A., Madsen, S., Norris, D. M., Shun-Shion, A. S., Krycer, J. R., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36549-2 · Cited by 83
The evolution of post-translational modifications
Bradley, D. T. C. (2022) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2022.101956 · Cited by 79
Ketogenic diet reshapes cancer metabolism through lysine β-hydroxybutyrylation
Qin, J., Huang, X., Gou, S., Zhang, S., Gou, Y., Zhang, Q., Chen, H., Sun, L., Chen, M., Liu, D., et al. (2024) · Nature Metabolism · doi:10.1038/s42255-024-01093-w · Cited by 85
Toward an Integrated Machine Learning Model of a Proteomics Experiment
Neely, B. A., Dorfer, V., Martens, L., Bludau, I., Bouwmeester, R., Degroeve, S., Deutsch, E. W., Gessulat, S., Käll, L., Palczynski, P., et al. (2023) · Journal of Proteome Research · doi:10.1021/acs.jproteome.2c00711 · Cited by 79
Self-citationNuclear lamin phosphorylation: an emerging role in gene regulation and pathogenesis of laminopathies
Liu, S. Y., & Ikegami, K. (2020) · Nucleus · doi:10.1080/19491034.2020.1832734 · Cited by 74
Integrating knowledge and omics to decipher mechanisms via large‐scale models of signaling networks
Garrido‐Rodríguez, M., Zirngibl, K., Ivanova, O., Lobentanzer, S., & Sáez-Rodríguez, J. (2022) · Molecular Systems Biology · doi:10.15252/msb.202211036 · Cited by 66
Structural basis for the intracellular regulation of ferritin degradation
Hoelzgen, F., Nguyen, T. T. P., Klukin, E., Boumaiza, M., Srivastava, A. K., Kim, E. Y., Zalk, R., Shahar, A., Cohen-Schwartz, S., Meyron‐Holtz, E. G., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48151-1 · Cited by 79
Mari, T., Mösbauer, K., Wyler, E., Landthaler, M., Drosten, C., & Selbach, M. (2022) · Journal of Proteome Research · doi:10.1021/acs.jproteome.2c00198 · Cited by 70
Drug Target Identification in Tissues by Thermal Proteome Profiling
Mateus, A., Kurzawa, N., Perrin, J., Bergamini, G., & Savitski, M. M. (2021) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-052120-013205 · Cited by 71
Self-citationPARPs and ADP-ribosylation: Deciphering the complexity with molecular tools
Dasovich, M., & Leung, A. K. L. (2023) · Molecular Cell · doi:10.1016/j.molcel.2023.04.009 · Cited by 69
System-wide identification and prioritization of enzyme substrates by thermal analysis
Saei, A. A., Beusch, C. M., Sabatier, P., Astorga‐Wells, J., Gharibi, H., Meng, Z., Chernobrovkin, A. L., Rodin, S., Näreoja, K., Thorsell, A. G., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21540-6 · Cited by 68
Computational Screening of Phase-Separating Proteins
Shen, B., Chen, Z., Yu, C., Chen, T., Shi, M., & Li, T. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.11.003 · Cited by 67
An integrated landscape of protein expression in human cancer
Jarnuczak, A. F., Najgebauer, H., Barzine, M., Kundu, D. J., Ghavidel, F. Z., Pérez‐Riverol, Y., Papatheodorou, I., Brāzma, A., & Vizcaíno, J. A. (2021) · Scientific Data · doi:10.1038/s41597-021-00890-2 · Cited by 65
Self-citationGong, T., Jiang, Y., Shao, C., Peng, W., Liu, M., Li, D., Zhang, B.-Y., Du, P., Huang, Y., Li, F., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110460 · Cited by 61
Nuclear RIPK1 promotes chromatin remodeling to mediate inflammatory response
Li, W., Shan, B., Zou, C., Wang, H., Zhang, M., Zhu, H., Naito, M. G., Xu, D., Manuel, V. J., Mifflin, L., et al. (2022) · Cell Research · doi:10.1038/s41422-022-00673-3 · Cited by 61
Post-translational control of the long and winding road to cholesterol.
Sharpe, L. J., Coates, H., & Brown, A. J. (2020) · Journal of Biological Chemistry · doi:10.1074/jbc.rev120.010723 · Cited by 46
Skinnider, M. A., & Foster, L. J. (2021) · Nature Methods · doi:10.1038/s41592-021-01194-4 · Cited by 54
Martínez‐Val, A., Fort, K. L., Koenig, C., van der Hoeven, L., Franciosa, G., Moehring, T., Ishihama, Y., Chen, Y., Makarov, A., Xuan, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39347-y · Cited by 56
Phosphoproteome profiling uncovers a key role for CDKs in TNF signaling
Tanzer, M. C., Bludau, I., Stafford, C. A., Hornung, V., & Mann, M. (2021) · Nature Communications · doi:10.1038/s41467-021-26289-6 · Cited by 53
Bortel, P., Piga, I., Koenig, C., Gerner, C., Martínez‐Val, A., & Olsen, J. V. (2024) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2024.100754 · Cited by 51
Gaubitz, C., Liu, X., Magrino, J., Stone, N. P., Landeck, J. T., Hedglin, M., & Kelch, B. A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2007437117 · Cited by 50
High-throughput functional characterization of protein phosphorylation sites in yeast
Viéitez, C., Busby, B. P., Ochoa, D., Mateus, A., Memon, D., Galardini, M., Yildiz, U., Trovato, M., Jawed, A., Geiger, A. G., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-01051-x · Cited by 60
Self-citationThe tissue proteome in the multi-omic landscape of kidney disease
Rinschen, M. M., & Sáez-Rodríguez, J. (2020) · Nature Reviews Nephrology · doi:10.1038/s41581-020-00348-5 · Cited by 56
Identification of phosphosites that alter protein thermal stability
Smith, I. R. L., Hess, K. N., Bakhtina, A. A., Valente, A. S., Rodríguez‐Mias, R. A., & Villén, J. (2021) · Nature Methods · doi:10.1038/s41592-021-01178-4 · Cited by 49
EMBL’s European Bioinformatics Institute (EMBL-EBI) in 2022
Thakur, M., Bateman, A. G., Brooksbank, C., Freeberg, M., Harrison, M., Hartley, M., Keane, T., Kleywegt, G. J., Leach, A. R., Levchenko, M., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1098 · Cited by 52
Self-citationThe regulatory landscape of the yeast phosphoproteome
Leutert, M., Barente, A. S., Fukuda, N. K., Rodriguez-Mias, R. A., & Villén, J. (2023) · Nature Structural & Molecular Biology · doi:10.1038/s41594-023-01115-3 · Cited by 52
Proteomic characterization of post-translational modifications in drug discovery
Zhai, L., Chen, K., Hao, B., & Tan, M. (2022) · Acta Pharmacologica Sinica · doi:10.1038/s41401-022-01017-y · Cited by 53
Vo, M.-C., Tran, V.-D.-H., Nguyen, V., Ruzimurodov, N., Trung, D. T., Kim, S. K., Jung, S., & Lee, J. (2025) · Journal of Hematology & Oncology · doi:10.1186/s13045-025-01744-9 · Cited by 59
Bell, R., & Vendruscolo, M. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.661117 · Cited by 52
Cryo-EM structures reveal high-resolution mechanism of a DNA polymerase sliding clamp loader
Gaubitz, C., Liu, X., Pajak, J., Stone, N. P., Hayes, J. A., Demo, G., & Kelch, B. A. (2022) · eLife · doi:10.7554/elife.74175 · Cited by 50
Analysis of protein phosphorylation using Phos-tag gels
O’Donoghue, L., & Smolenski, A. (2022) · Journal of Proteomics · doi:10.1016/j.jprot.2022.104558 · Cited by 50
Extensive regulation of enzyme activity by phosphorylation in Escherichia coli
Schastnaya, E., Nakic, Z. R., Gruber, C., Doubleday, P. F., Krishnan, A., Johns, N. I., Park, J., Wang, H. H., & Sauer, U. (2021) · Nature Communications · doi:10.1038/s41467-021-25988-4 · Cited by 50
ULK1 phosphorylation of striatin activates protein phosphatase 2A and autophagy
Hu, Z., Sankar, D. S., Vu, B., Leytens, A., Vionnet, C., Wu, W., Stumpe, M., Martínez-Martínez, E., Stork, B., & Dengjel, J. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109762 · Cited by 50
A multi-purpose, regenerable, proteome-scale, human phosphoserine resource for phosphoproteomics
Gassaway, B. M., Li, J., Rad, R., Mintseris, J., Mohler, K., Levy, T. M., Aguiar, M., Beausoleil, S. A., Paulo, J. A., Rinehart, J., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01638-5 · Cited by 47
Profiling the Human Phosphoproteome to Estimate the True Extent of Protein Phosphorylation
Kalyuzhnyy, A., Eyers, P. A., Eyers, C. E., Bowler-Barnett, E., Martin, M. J., Sun, Z., Deutsch, E. W., & Jones, A. R. (2022) · Journal of Proteome Research · doi:10.1021/acs.jproteome.2c00131 · Cited by 45
Rebak, A. S., Hendriks, I. A., Elsborg, J. D., Buch-Larsen, S. C., Nielsen, C. H., Terslev, L., Kirsch, R., Damgaard, D., Doncheva, N. T., Lennartsson, C., et al. (2024) · Nature Structural & Molecular Biology · doi:10.1038/s41594-024-01214-9 · Cited by 45
Importance of protein Ser/Thr/Tyr phosphorylation for bacterial pathogenesis
Køhler, J. B., Jers, C., Senissar, M., Shi, L., Derouiche, A., & Mijakovic̀, I. (2020) · FEBS Letters · doi:10.1002/1873-3468.13797 · Cited by 44
Functions of TopBP1 in preserving genome integrity during mitosis
Bagge, J., Oestergaard, V. H., & Lisby, M. (2020) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2020.08.009 · Cited by 39
qPTM: an updated database for PTM dynamics in human, mouse, rat and yeast
Yu, K., Wang, Y., Zheng, Y., Liu, Z., Zhang, Q., Wang, S., Zhao, Q., Zhang, X.-L., Li, X., Xu, R., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac820 · Cited by 48
Target Discovery for Host-Directed Antiviral Therapies: Application of Proteomics Approaches
Çakır, M., Obernier, K., Forget, A., & Krogan, N. J. (2021) · mSystems · doi:10.1128/msystems.00388-21 · Cited by 45
Self-citationTurnover atlas of proteome and phosphoproteome across mouse tissues and brain regions
Li, W., Dasgupta, A., Yang, K., Wang, S., Kumar, N. H., Chepyala, S. R., Yarbro, J. M., Hu, Z., Šalovská, B., Fornasiero, E. F., et al. (2025) · Cell · doi:10.1016/j.cell.2025.02.021 · Cited by 51
Enzymology and significance of protein histidine methylation
Jakobsson, M. E. (2021) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2021.101130 · Cited by 46
Samodova, D., Hosfield, C. M., Cramer, C. N., Giuli, M. V., Cappellini, E., Franciosa, G., Rosenblatt, M. M., Kelstrup, C. D., & Olsen, J. V. (2020) · Molecular & Cellular Proteomics · doi:10.1074/mcp.tir120.002129 · Cited by 43
Breckels, L. M., Hutchings, C., Ingole, K. D., Kim, S. Y., Lilley, K. S., Makwana, M. V., McCaskie, K. J. A., & Villanueva, E. (2024) · Cell chemical biology · doi:10.1016/j.chembiol.2024.08.008 · Cited by 45
Thermal proteome profiling: Insights into protein modifications, associations, and functions.
Sueur, C. L., Hammarén, H. M., Sridharan, S., & Savitski, M. (2022) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2022.102225 · Cited by 31
Self-citationComputational Analysis of Phosphoproteomics Data in Multi‐Omics Cancer Studies
Mantini, G., Pham, T. V., Piersma, S. R., & Jiménez, C. R. (2020) · PROTEOMICS · doi:10.1002/pmic.201900312 · Cited by 38
Application of nanomaterials in proteomics-driven precision medicine
Zhang, Y., Yang, H., Yu, Y., & Zhang, Y. (2022) · Theranostics · doi:10.7150/thno.64325 · Cited by 39
Emdal, K. B., Palacio‐Escat, N., Wigerup, C., Eguchi, A., Nilsson, H., Bekker‐Jensen, D. B., Rönnstrand, L., Kazi, J. U., Puissant, A., Itzykson, R., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111177 · Cited by 37
Mahin, A., Gopalakrishnan, A. P., Ahmed, M., Nisar, M., John, L., Shivamurthy, P. B., Ummar, S., Varghese, S., Modi, P. K., Pai, V. R., et al. (2025) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2024.0196 · Cited by 38
Significant non-existence of sequences in genomes and proteomes
Koulouras, G., & Frith, M. C. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab139 · Cited by 38
Recent advances in kinase signaling network profiling by mass spectrometry
Franciosa, G., Locard‐Paulet, M., Jensen, L. J., & Olsen, J. V. (2023) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2022.102260 · Cited by 37
Muneer, G., Chen, C., & Chen, Y. (2024) · PROTEOMICS · doi:10.1002/pmic.202400087 · Cited by 40
Franciosa, G., Smits, J. G. A., Minuzzo, S. A., Martínez‐Val, A., Indraccolo, S., & Olsen, J. V. (2021) · Nature Communications · doi:10.1038/s41467-021-22787-9 · Cited by 37
Substrate and phosphorylation site selection by phosphoprotein phosphatases
Nguyen, H. T., & Kettenbach, A. N. (2023) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2023.04.004 · Cited by 36
Large-scale identification of protein histidine methylation in human cells
Kapell, S., & Jakobsson, M. E. (2021) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqab045 · Cited by 36
Exploring Nanozymes for Organic Substrates: Building Nano‐organelles
Liu, X., Gao, M., Qin, Y., Xiong, Z., Zheng, H., Willner, I., Cai, X., & Li, R. (2024) · Angewandte Chemie International Edition · doi:10.1002/anie.202408277 · Cited by 35
Needham, E. J., Humphrey, S. J., Cooke, K. C., Fazakerley, D. J., Duan, X., Parker, B. L., & James, D. E. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.10.001 · Cited by 33
mTORC1 controls Golgi architecture and vesicle secretion by phosphorylation of SCYL1
Kaeser-Pebernard, S., Vionnet, C., Mari, M. C., Sankar, D. S., Hu, Z., Roubaty, C., Martínez-Martínez, E., Zhao, H., Spuch‐Calvar, M., Petri‐Fink, A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32487-7 · Cited by 36
Pathogenic mutations of human phosphorylation sites affect protein–protein interactions
Rrustemi, T., Meyer, K., Roske, Y., Uyar, B., Akalin, A., Imami, K., Ishihama, Y., Daumke, O., & Selbach, M. (2024) · Nature Communications · doi:10.1038/s41467-024-46794-8 · Cited by 33
Niinae, T., Imami, K., Sugiyama, N., & Ishihama, Y. (2021) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2021.100119 · Cited by 32
Oxidative Stress Promotes Liver Cancer Metastasis via RNF25‐Mediated E‐Cadherin Protein Degradation
Huang, Z., Zhou, L., Duan, J., Qin, S., Wang, Y., JIANG, J., Jin, P., Liu, R., Luo, M., He, B., et al. (2024) · Advanced Science · doi:10.1002/advs.202306929 · Cited by 36
µPhos: a scalable and sensitive platform for high-dimensional phosphoproteomics
Oliinyk, D., Will, A., Schneidmadel, F. R., Böhme, M., Rinke, J., Hochhaus, A., Ernst, T., Hahn, N., Geis, C., Lubeck, M., et al. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00050-9 · Cited by 37
Infection-induced lysine lactylation enables herpesvirus immune evasion
Tyl, M. D., Merengwa, V. U., & Cristea, I. M. (2025) · Science Advances · doi:10.1126/sciadv.ads6215 · Cited by 35
Advances in the computational landscape for repurposed drugs against COVID-19
Aronskyy, I., Masoudi‐Sobhanzadeh, Y., Cappuccio, A., & Zaslavsky, E. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.07.026 · Cited by 33
Metadata
- Authors
- Ochoa, D., Jarnuczak, A. F., Viéitez, C., Gehre, M., Soucheray, M., Mateus, A., Kleefeldt, A. A., Hill, A., Garcia-Alonso, L., Stein, F., Krogan, N. J., Savitski, M. M., Swaney, D. L., Vizcaíno, J. A., Noh, K.-M., & Beltrao, P.
- Affiliations
- European Bioinformatics Institute (GB)
- European Molecular Biology Laboratory (DE)
- Gladstone Institutes (US)
- Corresponding author
- Ochoa, David
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2019-12-09
- Volume
- 38
- Issue
- 3
- Pages
- 365-373
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC7100915
- OpenAlex
- W2994464657
- Cited by
- 491
- References
- 106
- Citations by year
- 2026: 83 · 2025: 85 · 2024: 80 · 2023: 74 · 2022: 83 · 2021: 54 · 2020: 25 · 2019: 6
- FWCI
- 16.77
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Nature Biotechnology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Wellcome Trust (WT101477MA, 101477, 208391/Z/17/Z, Z/17/Z, 208391)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Tyrosine Phosphatases
- Keywords
- protein phosphorylation, phosphosites, post-translational modification, machine learning feature selection, genetic susceptibility, SMARCC2, SWI/SNF chromatin remodeling, neuronal differentiation
- MeSH terms
- Machine Learning, Binding Sites, Cell Line, DNA-Binding Proteins, HeLa Cells, Humans, Phosphoproteins, Protein Processing, Post-Translational, Mass Spectrometry, Transcription Factors, Computational Biology, Databases, Protein, Proteomics, Neurogenesis, Data Curation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review