Chemoproteogenomic stratification of the missense variant cysteinome.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
28 Oct 2024
28 Oct 2024
Historique:
received:
28
08
2023
accepted:
15
10
2024
medline:
29
10
2024
pubmed:
29
10
2024
entrez:
29
10
2024
Statut:
epublish
Résumé
Cancer genomes are rife with genetic variants; one key outcome of this variation is widespread gain-of-cysteine mutations. These acquired cysteines can be both driver mutations and sites targeted by precision therapies. However, despite their ubiquity, nearly all acquired cysteines remain unidentified via chemoproteomics; identification is a critical step to enable functional analysis, including assessment of potential druggability and susceptibility to oxidation. Here, we pair cysteine chemoproteomics-a technique that enables proteome-wide pinpointing of functional, redox sensitive, and potentially druggable residues-with genomics to reveal the hidden landscape of cysteine genetic variation. Our chemoproteogenomics platform integrates chemoproteomic, whole exome, and RNA-seq data, with a customized two-stage false discovery rate (FDR) error controlled proteomic search, which is further enhanced with a user-friendly FragPipe interface. Chemoproteogenomics analysis reveals that cysteine acquisition is a ubiquitous feature of both healthy and cancer genomes that is further elevated in the context of decreased DNA repair. Reference cysteines proximal to missense variants are also found to be pervasive, supporting heretofore untapped opportunities for variant-specific chemical probe development campaigns. As chemoproteogenomics is further distinguished by sample-matched combinatorial variant databases and is compatible with redox proteomics and small molecule screening, we expect widespread utility in guiding proteoform-specific biology and therapeutic discovery.
Identifiants
pubmed: 39468056
doi: 10.1038/s41467-024-53520-x
pii: 10.1038/s41467-024-53520-x
doi:
Substances chimiques
Cysteine
K848JZ4886
Proteome
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
9284Subventions
Organisme : Arnold and Mabel Beckman Foundation
ID : Beckman Ynoung Investigator Award
Organisme : V Foundation for Cancer Research (V Foundation)
ID : V2019-017
Organisme : UC | UCLA | Jonsson Comprehensive Cancer Center (UCLA Jonsson Comprehensive Cancer Center)
ID : Seed Grant
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
ID : T32GM136614
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
ID : R01-GM094231
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : U24-CA271037
Organisme : U.S. Department of Energy (DOE)
ID : DE-FC02-02ER63421
Informations de copyright
© 2024. The Author(s).
Références
Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
pubmed: 26432245
doi: 10.1038/nature15393
Rivero-Hinojosa, S. et al. Proteogenomic discovery of neoantigens facilitates personalized multi-antigen targeted T cell immunotherapy for brain tumors. Nat. Commun. 12, 6689 (2021).
pubmed: 34795224
pmcid: 8602676
doi: 10.1038/s41467-021-26936-y
Sheynkman, G. M., Shortreed, M. R., Frey, B. L., Scalf, M. & Smith, L. M. Large-scale mass spectrometric detection of variant peptides resulting from nonsynonymous nucleotide differences. J. Proteome Res. 13, 228–240 (2014).
pubmed: 24175627
doi: 10.1021/pr4009207
Lau, E. et al. Splice-Junction-Based Mapping of Alternative Isoforms in the Human Proteome. Cell Rep. 29, 3751–3765 (2019).
pubmed: 31825849
pmcid: 6961840
doi: 10.1016/j.celrep.2019.11.026
Chen, Y. J. et al. Proteogenomics of non-smoking Lung cancer in East Asia delineates molecular signatures of pathogenesis and progression. Cell 182, 226–244 (2020).
pubmed: 32649875
doi: 10.1016/j.cell.2020.06.012
Vasaikar, S. et al. Proteogenomic analysis of human colon cancer reveals new therapeutic opportunities. Cell 177, 1035–1049 (2019).
pubmed: 31031003
pmcid: 6768830
doi: 10.1016/j.cell.2019.03.030
Wang, X. et al. Protein identification using customized protein sequence databases derived from RNA-seq data. J. Proteome Res. 11, 1009–1017 (2012).
pubmed: 22103967
doi: 10.1021/pr200766z
Sheynkman, G. M., Shortreed, M. R., Cesnik, A. J. & Smith, L. M. Proteogenomics: Integrating next-generation sequencing and mass spectrometry to characterize human proteomic variation. Annu. Rev. Anal. Chem. 9, 521–545 (2016).
doi: 10.1146/annurev-anchem-071015-041722
Sinitcyn, P. et al. Global detection of human variants and isoforms by deep proteome sequencing. Nat. Biotechnol. 1–11 https://doi.org/10.1038/s41587-023-01714-x (2023).
Sinitcyn, P., Gerwien, M. & Cox, J. MaxQuant module for the identification of genomic variants propagated into peptides. Methods Mol. Biol. 2456, 339–347 (2022).
Cesnik, A. J. et al. Spritz: A Proteogenomic Database Engine. J. Proteome Res. https://doi.org/10.1021/acs.jproteome.0c00407 (2020).
Wang, X. & Zhang, B. customProDB: an R package to generate customized protein databases from RNA-Seq data for proteomics search. Bioinformatics 29, 3235–3237 (2013).
pubmed: 24058055
pmcid: 3842753
doi: 10.1093/bioinformatics/btt543
Sheynkman, G. M. et al. Using Galaxy-P to leverage RNA-Seq for the discovery of novel protein variations. BMC Genomics 15, 703 (2014).
pubmed: 25149441
pmcid: 4158061
doi: 10.1186/1471-2164-15-703
Wen, B. et al. sapFinder: an R/Bioconductor package for detection of variant peptides in shotgun proteomics experiments. Bioinformatics 30, 3136–3138 (2014).
pubmed: 25053745
pmcid: 4609003
doi: 10.1093/bioinformatics/btu397
Miller, R. M. et al. Enhanced protein isoform characterization through long-read proteogenomics. Genome Biol. 23, 69 (2022).
pubmed: 35241129
pmcid: 8892804
doi: 10.1186/s13059-022-02624-y
Nesvizhskii, A. I. Proteogenomics: Concepts, applications and computational strategies. Nat. Methods 11, 1114–1125 (2014).
pubmed: 25357241
pmcid: 4392723
doi: 10.1038/nmeth.3144
Woo, S. et al. Proteogenomic strategies for identification of aberrant cancer peptides using large-scale next-generation sequencing data. PROTEOMICS 14, 2719–2730 (2014).
pubmed: 25263569
pmcid: 4256132
doi: 10.1002/pmic.201400206
Woo, S. et al. Advanced proteogenomic analysis reveals multiple peptide mutations and complex immunoglobulin peptides in colon cancer. J. Proteome Res. 14, 3555–3567 (2015).
pubmed: 26139413
pmcid: 4868822
doi: 10.1021/acs.jproteome.5b00264
Wen, B., Li, K., Zhang, Y. & Zhang, B. Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis. Nat. Commun. 11, 1–14 (2020).
doi: 10.1038/s41467-020-15456-w
Weerapana, E. et al. Quantitative reactivity profiling predicts functional cysteines in proteomes. https://doi.org/10.1038/nature09472 (2010).
Backus, K. M. et al. Proteome-wide covalent ligand discovery in native biological systems. https://doi.org/10.1038/nature18002 (2016).
Xiao, H. et al. A quantitative tissue-specific landscape of protein redox regulation during aging. Cell 180, 968–983 (2020).
pubmed: 32109415
pmcid: 8164166
doi: 10.1016/j.cell.2020.02.012
Leichert, L. I. et al. Quantifying changes in the thiol redox proteome upon oxidative stress in vivo. Proc. Natl. Acad. Sci. USA 105, 8197–8202 (2008).
pubmed: 18287020
pmcid: 2448814
doi: 10.1073/pnas.0707723105
Desai, H. S. et al. SP3-Enabled rapid and high coverage chemoproteomic identification of cell-state–dependent redox-sensitive cysteines. Mol. Cell. Proteom. 21, 100218 (2022).
doi: 10.1016/j.mcpro.2022.100218
Bar-Peled, L. et al. Chemical proteomics identifies druggable vulnerabilities in a genetically defined cancer. Cell 171, 696–709 (2017).
pubmed: 28965760
pmcid: 5728659
doi: 10.1016/j.cell.2017.08.051
Hacker, S. M. et al. Global profiling of lysine reactivity and ligandability in the human proteome. Nat. Chem. 9, 1181–1190 (2017).
pubmed: 29168484
pmcid: 5726523
doi: 10.1038/nchem.2826
Kemper, E. K., Zhang, Y., Dix, M. M. & Cravatt, B. F. Global profiling of phosphorylation-dependent changes in cysteine reactivity. Nat. Methods https://doi.org/10.1038/s41592-022-01398-2 (2022).
Assigning functionality to cysteines by base editing of cancer dependency genes. Nat. Chem. Biol. 19, 1320–1330 (2023).
Boatner, L. M., Palafox, M. F., Schweppe, D. K. & Backus, K. M. CysDB: a human cysteine database based on experimental quantitative chemoproteomics. Cell Chem. Biol. https://doi.org/10.1016/j.chembiol.2023.04.004 (2023).
Kuljanin, M. et al. Reimagining high-throughput profiling of reactive cysteines for cell-based screening of large electrophile libraries. Nat. Biotechnol. 39, 630–641 (2021).
pubmed: 33398154
pmcid: 8316984
doi: 10.1038/s41587-020-00778-3
Yan, T. et al. SP3‐FAIMS Chemoproteomics for high‐coverage profiling of the human cysteinome**. ChemBioChem https://doi.org/10.1002/cbic.202000870 (2021).
Cao, J. et al. Multiplexed CuAAC Suzuki–miyaura labeling for tandem activity-based chemoproteomic profiling. Anal. Chem. 93, 2610–2618 (2021).
pubmed: 33470097
pmcid: 8849040
doi: 10.1021/acs.analchem.0c04726
Li, Z., Liu, K., Xu, P. & Yang, J. Benchmarking cleavable biotin tags for peptide-centric chemoproteomics. J. Proteome Res. 21, 1349–1358 (2022).
pubmed: 35467356
doi: 10.1021/acs.jproteome.2c00174
Vinogradova, E. V. et al. An activity-guided map of electrophile-cysteine interactions in primary human T cells. Cell 182, 1009–1026 (2020).
pubmed: 32730809
pmcid: 7775622
doi: 10.1016/j.cell.2020.07.001
Palafox, M. F., Desai, H. S., Arboleda, V. A. & Backus, K. M. From chemoproteomic‐detected amino acids to genomic coordinates: insights into precise multi‐omic data integration. Mol. Syst. Biol. 17, e9840 (2021).
pubmed: 33599394
pmcid: 7890448
doi: 10.15252/msb.20209840
Yang, F., Jia, G., Guo, J., Liu, Y. & Wang, C. Quantitative chemoproteomic profiling with data-independent acquisition-based mass spectrometry. J. Am. Chem. Soc. 144, 901–911 (2022).
pubmed: 34986311
doi: 10.1021/jacs.1c11053
Miseta, A. & Csutora, P. Relationship between the occurrence of cysteine in proteins and the complexity of organisms. Mol. Biol. Evol. 17, 1232–1239 (2000).
pubmed: 10908643
doi: 10.1093/oxfordjournals.molbev.a026406
Tsuber, V., Kadamov, Y., Brautigam, L., Berglund, U. W. & Helleday, T. Mutations in cancer cause gain of cysteine, histidine, and tryptophan at the expense of a net loss of arginine on the proteome level. Biomolecules 7, 49 (2017).
pubmed: 28671612
pmcid: 5618230
doi: 10.3390/biom7030049
Kim, J. Y., Plaman, B. A. & Bishop, A. C. Targeting a pathogenic cysteine mutation: Discovery of a specific inhibitor of Y279C SHP2. Biochemistry 59, 3498–3507 (2020).
pubmed: 32871078
doi: 10.1021/acs.biochem.0c00471
Ostrem, J. M., Peters, U., Sos, M. L., Wells, J. A. & Shokat, K. M. K-Ras(G12C) inhibitors allosterically control GTP affinity and effector interactions. Nature 503, 548–551 (2013).
pubmed: 24256730
pmcid: 4274051
doi: 10.1038/nature12796
Slebos, R. J. C. et al. K-ras oncogene activation as a prognostic marker in adenocarcinoma of the lung. N. Engl. J. Med. 323, 561–565 (1990).
pubmed: 2199829
doi: 10.1056/NEJM199008303230902
Tomlinson, D. C., Hurst, C. D. & Knowles, M. A. Knockdown by shRNA identifies S249C mutant FGFR3 as a potential therapeutic target in bladder cancer. Oncogene 26, 5889–5899 (2007).
pubmed: 17384684
pmcid: 2443272
doi: 10.1038/sj.onc.1210399
Dang, L. et al. Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 462, 739–744 (2009).
pubmed: 19935646
pmcid: 2818760
doi: 10.1038/nature08617
Sved, J. & Bird, A. The expected equilibrium of the CpG dinucleotide in vertebrate genomes under a mutation model. Proc. Natl. Acad. Sci. USA 87, 4692–4696 (1990).
pubmed: 2352943
pmcid: 54183
doi: 10.1073/pnas.87.12.4692
Kong, A. T., Leprevost, F. V., Avtonomov, D. M., Mellacheruvu, D. & Nesvizhskii, A. I. MSFragger: Ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat. Methods 14, 513–520 (2017).
pubmed: 28394336
pmcid: 5409104
doi: 10.1038/nmeth.4256
Leprevost, F. et al. Philosopher: a versatile toolkit for shotgun proteomics data analysis. Nat. Methods 17, 869–870 (2020).
doi: 10.1038/s41592-020-0912-y
Yan, T. et al. Enhancing cysteine chemoproteomic coverage through systematic assessment of click chemistry product fragmentation. Anal. Chem. 94, 3800–3810 (2022).
pubmed: 35195394
doi: 10.1021/acs.analchem.1c04402
Yu, F., Haynes, S. E. & Nesvizhskii, A. I. IonQuant enables accurate and sensitive label-free quantification with FDR-controlled match-between-runs. Mol. Cell. Proteom. 20, 100077 (2021).
doi: 10.1016/j.mcpro.2021.100077
Yu, F. et al. Fast quantitative analysis of timsTOF PASEF data with MSFragger and IonQuant. Mol. Cell. Proteom. 19, 1575–1585 (2020).
doi: 10.1074/mcp.TIR120.002048
Boutilier, J. M., Warden, H., Doucette, A. A. & Wentzell, P. D. Chromatographic behaviour of peptides following dimethylation with H2/D2-formaldehyde: Implications for comparative proteomics. J. Chromatogr. B 908, 59–66 (2012).
doi: 10.1016/j.jchromb.2012.09.035
Zhang, R., Sioma, C. S., Thompson, R. A., Xiong, L. & Regnier, F. E. Controlling deuterium isotope effects in comparative proteomics. Anal. Chem. 74, 3662–3669 (2002).
pubmed: 12175151
doi: 10.1021/ac025614w
Zhu, Y. et al. Discovery of coding regions in the human genome by integrated proteogenomics analysis workflow. Nat. Commun. 9, 903 (2018).
Yeom, J. et al. A proteogenomic approach for protein-level evidence of genomic variants in cancer cells. Sci. Rep. 6, 35305 (2016).
pubmed: 27734975
pmcid: 5062161
doi: 10.1038/srep35305
Wang, D. et al. A deep proteome and transcriptome abundance atlas of 29 healthy human tissues. Mol. Syst. Biol. 15, e8503 (2019).
pubmed: 30777892
pmcid: 6379049
doi: 10.15252/msb.20188503
Tate, J. G. et al. COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res. 47, D941–D947 (2019).
pubmed: 30371878
doi: 10.1093/nar/gky1015
Iorio, F. et al. A landscape of pharmacogenomic interactions in cancer. Cell 166, 740–754 (2016).
pubmed: 27397505
pmcid: 4967469
doi: 10.1016/j.cell.2016.06.017
Wishart, D. S. et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 34, D668–D672 (2006).
pubmed: 16381955
doi: 10.1093/nar/gkj067
Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).
pubmed: 31068700
pmcid: 6697103
doi: 10.1038/s41586-019-1186-3
Glaab, W. E. et al. Characterization of distinct human endometrial carcinoma cell lines deficient in mismatch repair that originated from a single tumor *. J. Biol. Chem. 273, 26662–26669 (1998).
pubmed: 9756907
doi: 10.1074/jbc.273.41.26662
Matheson, E. C. & Hall, A. G. Assessment of mismatch repair function in leukaemic cell lines and blasts from children with acute lymphoblastic leukaemia. Carcinogenesis 24, 31–38 (2003).
pubmed: 12538346
doi: 10.1093/carcin/24.1.31
Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020).
pubmed: 32025018
pmcid: 7054213
doi: 10.1038/s41586-020-1943-3
Alexandrov, L. B. et al. Mutational signatures associated with tobacco smoking in human cancer. Science 354, 618–622 (2016).
pubmed: 27811275
pmcid: 6141049
doi: 10.1126/science.aag0299
Van Houten, B. & Kong, M. Eukaryotic nucleotide excision repair. Encycl. Cell Biol. 1, 435–441 (2016).
doi: 10.1016/B978-0-12-394447-4.10045-8
Schulze, K. V., Hanchard, N. A. & Wangler, M. F. Biases in arginine codon usage correlate with genetic disease risk. Genet. Med. 1–6 https://doi.org/10.1038/s41436-020-0813-6 (2020).
Sherry, S. T., Ward, M. & Sirotkin, K. dbSNP—Database for single nucleotide polymorphisms and other classes of minor genetic variation. Genome Res. 9, 677–679 (1999).
pubmed: 10447503
doi: 10.1101/gr.9.8.677
Landrum, M. J. et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 46, D1062–D1067 (2018).
pubmed: 29165669
doi: 10.1093/nar/gkx1153
Dentro, S. C. et al. Characterizing genetic intra-tumor heterogeneity across 2658 human cancer genomes. Cell 184, 2239–2254 (2021).
pubmed: 33831375
pmcid: 8054914
doi: 10.1016/j.cell.2021.03.009
Choong, W. K., Wang, J. H. & Sung, T. Y. MinProtMaxVP: Generating a minimized number of protein variant sequences containing all possible variant peptides for proteogenomic analysis. J. Proteom. 223, 103819 (2020).
doi: 10.1016/j.jprot.2020.103819
Alfaro, J. A. et al. Detecting protein variants by mass spectrometry: A comprehensive study in cancer cell-lines. Genome Med. 9, 62 (2017).
Zhang, M. et al. CanProVar 2.0: An Updated database of human cancer proteome variation. J. Proteome Res. 16, 421–432 (2017).
pubmed: 27977206
doi: 10.1021/acs.jproteome.6b00505
Robin, T., Bairoch, A., Müller, M., Lisacek, F. & Lane, L. Large-scale reanalysis of publicly available HeLa cell proteomics data in the context of the human proteome project. J. Proteome Res. 17, 4160–4170 (2018).
pubmed: 30175587
doi: 10.1021/acs.jproteome.8b00392
Krug, K., Popic, S., Carpy, A., Taumer, C. & Macek, B. Construction and assessment of individualized proteogenomic databases for large-scale analysis of nonsynonymous single nucleotide variants. PROTEOMICS 14, 2699–2708 (2014).
pubmed: 25251379
doi: 10.1002/pmic.201400219
Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).
pubmed: 24487276
pmcid: 3992975
doi: 10.1038/ng.2892
Terry, A. Q. et al. Disulfide-HMGB1 signals through TLR4 and TLR9 to induce inflammatory macrophages capable of innate-adaptive crosstalk in human liver transplantation. Am. J. Transplant. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 23, 1858–1871 (2023).
doi: 10.1016/j.ajt.2023.08.002
Sosa, R. A. et al. Disulfide high-mobility group box 1 drives ischemia-reperfusion injury in human liver transplantation. Hepatology 73, 1158–1175 (2021).
pubmed: 32426849
doi: 10.1002/hep.31324
Sosa, R. A. et al. Pattern recognition receptor-reactivity screening of liver transplant patients: Potential for personalized and precise organ matching to reduce risks of ischemia-reperfusion injury. Ann. Surg. 271, 922–931 (2020).
pubmed: 30480558
doi: 10.1097/SLA.0000000000003085
Venereau, E. et al. Mutually exclusive redox forms of HMGB1 promote cell recruitment or proinflammatory cytokine release. J. Exp. Med. 209, 1519–1528 (2012).
pubmed: 22869893
pmcid: 3428943
doi: 10.1084/jem.20120189
Chuang, H., Zhang, W. & Gray, W. M. Arabidopsis ETA2, an apparent ortholog of the human cullin-interacting protein CAND1, is required for auxin responses mediated by the SCF(TIR1) ubiquitin ligase. Plant Cell 16, 1883–1897 (2004).
pubmed: 15208392
pmcid: 514168
doi: 10.1105/tpc.021923
Xu, X. et al. Unique domain appended to vertebrate tRNA synthetase is essential for vascular development. Nat. Commun. 3, 681 (2012).
pubmed: 22353712
doi: 10.1038/ncomms1686
Son, J. et al. Conformational changes in human prolyl-tRNA synthetase upon binding of the substrates proline and ATP and the inhibitor halofuginone. Acta Crystallogr. D. Biol. Crystallogr. 69, 2136–2145 (2013).
pubmed: 24100331
doi: 10.1107/S0907444913020556
Hornbeck, P. V. et al. PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res. 43, D512–D520 (2015).
pubmed: 25514926
doi: 10.1093/nar/gku1267
Yan, T. et al. Proximity-labeling chemoproteomics defines the subcellular cysteinome and inflammation-responsive mitochondrial redoxome. Cell Chem. Biol. 30, 811–827 (2023).
pubmed: 37419112
pmcid: 10510412
doi: 10.1016/j.chembiol.2023.06.008
Yan, T., Boatner, L. M., Cui, L., Tontonoz, P. J. & Backus, K. M. Defining the cell surface cysteinome using two-step enrichment proteomics. JACS Au 3, 3506–3523 (2023).
pubmed: 38155636
pmcid: 10751780
doi: 10.1021/jacsau.3c00707
Ying, H. & Huttley, G. Exploiting CpG hypermutability to identify phenotypically significant variation within human protein-coding genes. Genome Biol. Evol. 3, 938–949 (2011).
pubmed: 21398426
pmcid: 3184784
doi: 10.1093/gbe/evr021
Fang, H. et al. Deficiency of replication-independent DNA mismatch repair drives a 5-methylcytosine deamination mutational signature in cancer. Sci. Adv. 7, eabg4398 (2021).
pubmed: 34730999
pmcid: 8565909
doi: 10.1126/sciadv.abg4398
Finkel, T. Signal transduction by reactive oxygen species. J. Cell Biol. 194, 7–15 (2011).
pubmed: 21746850
pmcid: 3135394
doi: 10.1083/jcb.201102095
Dendrou, C. A., Petersen, J., Rossjohn, J. & Fugger, L. HLA variation and disease. Nat. Rev. Immunol. 18, 325–339 (2018).
pubmed: 29292391
doi: 10.1038/nri.2017.143
Mallal, S. et al. HLA-B*5701 Screening for hypersensitivity to Abacavir. N. Engl. J. Med. 358, 568–579 (2008).
pubmed: 18256392
doi: 10.1056/NEJMoa0706135
Weiss, G. A. et al. Covalent HLA-B27/peptide complex induced by specific recognition of an aziridine mimic of arginine. Proc. Natl. Acad. Sci. USA 93, 10945–10948 (1996).
pubmed: 8855288
pmcid: 38263
doi: 10.1073/pnas.93.20.10945
Zhang, Z. et al. A covalent inhibitor of K-Ras(G12C) induces MHC class I presentation of haptenated peptide neoepitopes targetable by immunotherapy. Cancer Cell 40, 1060–1069 (2022).
pubmed: 36099883
pmcid: 10393267
doi: 10.1016/j.ccell.2022.07.005
Grob, N. M. et al. Electrophile scanning reveals reactivity hotspots for the design of covalent peptide binders. ACS Chem. Biol. https://doi.org/10.1021/acschembio.3c00538 (2023).
Cravatt, B. F., Wright, A. T. & Kozarich, J. W. Activity-based protein profiling: from enzyme chemistry to proteomic chemistry. Annu. Rev. Biochem. 77, 383–414 (2008).
pubmed: 18366325
doi: 10.1146/annurev.biochem.75.101304.124125
Nomura, D. K., Dix, M. M. & Cravatt, B. F. Activity-based protein profiling for biochemical pathway discovery in cancer. Nat. Rev. Cancer 10, 630–638 (2010).
pubmed: 20703252
pmcid: 3021511
doi: 10.1038/nrc2901
Porta, E. O. J. & Steel, P. G. Activity-based protein profiling: A graphical review. Curr. Res. Pharmacol. Drug Discov. 5, 100164 (2023).
pubmed: 37692766
pmcid: 10484978
doi: 10.1016/j.crphar.2023.100164
Gygi, S. P., Rochon, Y., Franza, B. R. & Aebersold, R. Correlation between protein and mRNA abundance in yeast. Mol. Cell. Biol. 19, 1720–1730 (1999).
pubmed: 10022859
pmcid: 83965
doi: 10.1128/MCB.19.3.1720
Fortelny, N., Overall, C. M., Pavlidis, P. & Freue, G. V. C. Can we predict protein from mRNA levels? Nature 547, E19–E20 (2017).
pubmed: 28748932
doi: 10.1038/nature22293
Liu, Y., Beyer, A. & Aebersold, R. On the dependency of cellular protein levels on mRNA abundance. Cell 165, 535–550 (2016).
pubmed: 27104977
doi: 10.1016/j.cell.2016.03.014
José O. Castellón et al. Chemoproteomics Identifies State-Dependent and Proteoform-Selective Caspase-2 Inhibitors. J Am Chem Soc. 146, 14972–14988 (2024).
Taylor, I. R. et al. Tryptophan scanning mutagenesis as a way to mimic the compound-bound state and probe the selectivity of allosteric inhibitors in cells. Chem. Sci. 11, 1892–1904 (2020).
pubmed: 34123282
pmcid: 8148087
doi: 10.1039/C9SC04284A
Baek, K. et al. Systemwide disassembly and assembly of SCF ubiquitin ligase complexes. Cell 186, 1895–1911 (2023).
pubmed: 37028429
pmcid: 10156175
doi: 10.1016/j.cell.2023.02.035
Shaaban, M. et al. Structural and mechanistic insights into the CAND1-mediated SCF substrate receptor exchange. Mol. Cell 83, 2332–2346 (2023).
pubmed: 37339624
doi: 10.1016/j.molcel.2023.05.034
Szpiech, Z. A. et al. Prominent features of the amino acid mutation landscape in cancer. PLOS ONE 12, e0183273 (2017).
pubmed: 28837668
pmcid: 5570307
doi: 10.1371/journal.pone.0183273
Yu, Q. et al. Sample multiplexing-based targeted pathway proteomics with real-time analytics reveals the impact of genetic variation on protein expression. Nat. Commun. 14, 555 (2023).
pubmed: 36732331
pmcid: 9894840
doi: 10.1038/s41467-023-36269-7
Jinek, M. et al. A Programmable Dual-RNA–Guided DNA Endonuclease in Adaptive Bacterial Immunity. Science 337, 816–821 (2012).
pubmed: 22745249
pmcid: 6286148
doi: 10.1126/science.1225829
Benns, H. J. et al. CRISPR-based oligo recombineering prioritizes apicomplexan cysteines for drug discovery. Nat. Microbiol. 7, 1891–1905 (2022).
pubmed: 36266336
pmcid: 9613468
doi: 10.1038/s41564-022-01249-y
Gaudelli, N. M. et al. Programmable base editing of A•T to G•C in genomic DNA without DNA cleavage. Nature 551, 464–471 (2017).
pubmed: 29160308
pmcid: 5726555
doi: 10.1038/nature24644
Komor, A. C., Kim, Y. B., Packer, M. S., Zuris, J. A. & Liu, D. R. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature 533, 420–424 (2016).
pubmed: 27096365
pmcid: 4873371
doi: 10.1038/nature17946
Joutel, A. et al. Notch3 mutations in CADASIL, a hereditary adult-onset condition causing stroke and dementia. Nature 383, 707–710 (1996).
pubmed: 8878478
doi: 10.1038/383707a0
Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).
pubmed: 23396013
pmcid: 3833702
doi: 10.1038/nbt.2514
Hattori, T. et al. Creating MHC-restricted neoantigens with covalent inhibitors that can be targeted by immune therapy. Cancer Discov. 13, 132–145 (2023).
pubmed: 36250888
doi: 10.1158/2159-8290.CD-22-1074
Desai, J., Francis, C., Longo, K. & Hoss, A. Predicting exon criticality from protein sequence. Nucleic Acids Res. 50, 3128–3141 (2022).
pubmed: 35286381
pmcid: 8989546
doi: 10.1093/nar/gkac155
Cao, X. et al. Comparative proteomic profiling of unannotated microproteins and alternative proteins in human cell lines. J. Proteome Res. 19, 3418–3426 (2020).
pubmed: 32449352
pmcid: 7429271
doi: 10.1021/acs.jproteome.0c00254
Chen, Y., Cao, X., Loh, K. H. & Slavoff, S. A. Chemical labeling and proteomics for characterization of unannotated small and alternative open reading frame-encoded polypeptides. Biochem. Soc. Trans. 51, 1071–1082 (2023).
pubmed: 37171061
pmcid: 10317152
doi: 10.1042/BST20221074
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886
doi: 10.1093/bioinformatics/bts635
Danecek, P. et al. Twelve years of SAMtools and BCFtools. GigaScience 10, giab008 (2021).
pubmed: 33590861
pmcid: 7931819
doi: 10.1093/gigascience/giab008
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
pubmed: 19505943
pmcid: 2723002
doi: 10.1093/bioinformatics/btp352
Oikkonen, L. & Lise, S. Making the most of RNA-seq: Pre-processing sequencing data with Opossum for reliable SNP variant detection. Wellcome Open Res. 2, 6 (2017).
pubmed: 28239666
pmcid: 5322827
doi: 10.12688/wellcomeopenres.10501.2
Rimmer, A. et al. Integrating mapping-, assembly- and haplotype-based approaches for calling variants in clinical sequencing applications. Nat. Genet. 46, 912–918 (2014).
pubmed: 25017105
pmcid: 4753679
doi: 10.1038/ng.3036
McKenna, A. et al. The genome analysis toolkit: A mapreduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
pubmed: 20644199
pmcid: 2928508
doi: 10.1101/gr.107524.110
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
pubmed: 27043002
doi: 10.1038/nbt.3519
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
pubmed: 25516281
pmcid: 4302049
doi: 10.1186/s13059-014-0550-8
Obenchain, V. et al. VariantAnnotation: a Bioconductor package for exploration and annotation of genetic variants. Bioinformatics 30, 2076–2078 (2014).
pubmed: 24681907
pmcid: 4080743
doi: 10.1093/bioinformatics/btu168
Lawrence, M. et al. Software for computing and annotating genomic ranges. PLOS Comput. Biol. 9, e1003118 (2013).
pubmed: 23950696
pmcid: 3738458
doi: 10.1371/journal.pcbi.1003118
Durinck, S. et al. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 21, 3439–3440 (2005).
pubmed: 16082012
doi: 10.1093/bioinformatics/bti525
UniProt Consortium, T. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 46, 2699 (2018).
pubmed: 29425356
doi: 10.1093/nar/gky092
Nesvizhskii, A. I., Keller, A., Kolker, E. & Aebersold, R. A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 75, 4646–4658 (2003).
pubmed: 14632076
doi: 10.1021/ac0341261
Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: Improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).
pubmed: 30395289
doi: 10.1093/nar/gky1106
Hatos, A. et al. DisProt: intrinsic protein disorder annotation in 2020. Nucleic Acids Res. 48, D269–D276 (2020).
pubmed: 31713636
Piovesan, D. et al. DisProt 7.0: a major update of the database of disordered proteins. Nucleic Acids Res. 45, D219–D227 (2017).
pubmed: 27899601
doi: 10.1093/nar/gkw1056
Quaglia, F. et al. DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation. Nucleic Acids Res. 50, D480–D487 (2022).
pubmed: 34850135
doi: 10.1093/nar/gkab1082
Desai, H. S., Yan, T. & Backus, K. M. SP3-FAIMS-Enabled high-throughput quantitative profiling of the cysteinome. Curr. Protoc. 2, e492 (2022).
pubmed: 35895291
doi: 10.1002/cpz1.492
Hughes, C. S. et al. Single-pot, solid-phase-enhanced sample preparation for proteomics experiments. Nat. Protoc. 14, 68–85 (2019).
pubmed: 30464214
doi: 10.1038/s41596-018-0082-x
Hebert, A. S. et al. Comprehensive single-shot proteomics with FAIMS on a hybrid orbitrap mass spectrometer. Anal. Chem. 90, 9529–9537 (2018).
pubmed: 29969236
pmcid: 6145172
doi: 10.1021/acs.analchem.8b02233