Wednesday, June 24, 2026
No menu items!
HomeNatureAlternate RNA decoding results in stable and abundant proteins in mammals

Alternate RNA decoding results in stable and abundant proteins in mammals

  • Cantwell-Dorris, E. R., O’Leary, J. J. & Sheils, O. M. BRAFV600E: implications for carcinogenesis and molecular therapy. Mol. Cancer Ther. 10, 385–394 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Hart, J. R. et al. The butterfly effect in cancer: a single base mutation can remodel the cell. Proc. Natl Acad. Sci. USA 112, 1131–1136 (2015).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wright, A. & Vissel, B. The essential role of AMPA receptor GluR2 subunit RNA editing in the normal and diseased brain. Front. Mol. Neurosci. 5, 34 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Parker, J. & Friesen, J. D. “Two out of three” codon reading leading to mistranslation in vivo. Mol. Gen. Genet. 177, 439–445 (1980).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Savitski, M. M., Nielsen, M. L. & Zubarev, R. A. ModifiComb, a new proteomic tool for mapping substoichiometric post-translational modifications, finding novel types of modifications, and fingerprinting complex protein mixtures. Mol. Cell. Proteomics 5, 935–948 (2006).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wilhelm, M. et al. Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics. Nat. Commun. 12, 3346 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Picciani, M. et al. Oktoberfest: open-source spectral library generation and rescoring pipeline based on Prosit. Proteomics 24, e2300112 (2024).

    Article 
    PubMed 

    Google Scholar
     

  • Yang, K. L. et al. MSBooster: improving peptide identification rates using deep learning-based features. Nat. Commun. 14, 4539 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Leduc, A. & Slavov, N. Impact of protein degradation and cell growth on mammalian proteomes. Preprint at bioRxiv https://doi.org/10.1101/2025.02.10.637566 (2025).

  • Clark, D. J. et al. Integrated proteogenomic characterization of clear cell renal cell carcinoma. Cell 179, 964–983 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Krug, K. et al. Proteogenomic landscape of breast cancer tumorigenesis and targeted therapy. Cell 183, 1436–1456 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gillette, M. A. et al. Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma. Cell 182, 200–225 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dou, Y. et al. Proteogenomic characterization of endometrial carcinoma. Cell 180, 729–748 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cao, L. et al. Proteogenomic characterization of pancreatic ductal adenocarcinoma. Cell 184, 5031–5052 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Satpathy, S. et al. A proteogenomic portrait of lung squamous cell carcinoma. Cell 184, 4348–4371 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, D. et al. A deep proteome and transcriptome abundance atlas of 29 healthy human tissues. Mol. Syst. Biol. 15, e8503 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Batut, B. et al. Community-driven data analysis training for biology. Cell Syst. 6, 752–758 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mordret, E. et al. Systematic detection of amino acid substitutions in proteomes reveals mechanistic basis of ribosome errors and selection for translation fidelity. Mol. Cell 75, 427–441 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ma, C. et al. Improved peptide retention time prediction in liquid chromatography through deep learning. Anal. Chem. 90, 10881–10888 (2018).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Wen, B., Wang, X. & Zhang, B. PepQuery enables fast, accurate, and convenient proteomic validation of novel genomic alterations. Genome Res. 29, 485–493 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mohler, K. & Ibba, M. Translational fidelity and mistranslation in the cellular response to stress. Nat. Microbiol. 2, 17117 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liigand, P., Kaupmees, K. & Kruve, A. Influence of the amino acid composition on the ionization efficiencies of small peptides. J. Mass Spectrom. 54, 481–487 (2019).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Serrano, G., Guruceaga, E. & Segura, V. DeepMSPeptide: peptide detectability prediction using deep learning. Bioinformatics 36, 1279–1280 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Gessulat, S. et al. Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nat. Methods 16, 509–518 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Gabriel, W. et al. Prosit-TMT: deep learning boosts identification of TMT-labeled peptides. Anal. Chem. 94, 7181–7190 (2022).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Wiśniewski, J. R., Hein, M. Y., Cox, J. & Mann, M. A “proteomic ruler” for protein copy number and concentration estimation without spike-in standards. Mol. Cell. Proteomics 13, 1535–9484 (2014).

    Article 

    Google Scholar
     

  • Wu, Q. et al. Translation affects mRNA stability in a codon-dependent manner in human cells. eLife 8, e45396 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Drummond, D. A. & Wilke, C. O. Mistranslation-induced protein misfolding as a dominant constraint on coding-sequence evolution. Cell 134, 341–352 (2008).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Quax, T. E., Claassens, N. J., Söll, D. & van der Oost, J. Codon bias as a means to fine-tune gene expression. Mol. Cell 59, 149–161 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • McCormick, C. A. et al. mRNA psi profiling using nanopore DRS reveals cell type-specific pseudouridylation. Preprint at bioRxiv https://doi.org/10.1101/2024.05.08.593203 (2024).

  • Mathieson, T. et al. Systematic analysis of protein turnover in primary cells. Nat. Commun. 9, 689 (2018).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chen, S. et al. A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625, 92–100 (2024).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Giansanti, P. et al. Mass spectrometry-based draft of the mouse proteome. Nat. Methods 19, 803–811 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Specht, H. et al. PSMtags improve peptide sequencing and throughput in sensitive proteomics. Preprint at bioRxiv https://doi.org/10.1101/2025.05.22.655509 (2025).

  • Slavov, N. Single-cell proteomic technologies: tools in the quest for principles. Annu. Rev. Biophys. 55, 253–275 (2026).

  • Leduc, A., Khoury, L., Cantlon, J., Khan, S. & Slavov, N. Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP. Nat. Protoc. 19, 3750–3776 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Huffman, R. G. et al. Prioritized mass spectrometry increases the depth, sensitivity and data completeness of single-cell proteomics. Nat. Methods 20, 714–722 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sun, L. et al. Evolutionary gain of alanine mischarging to noncognate tRNAs with a G4: U69 base pair. J. Am. Chem. Soc. 138, 12948–12955 (2016).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Netzer, N. et al. Innate immune and chemically triggered oxidative stress modifies translational fidelity. Nature 462, 522–526 (2009).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Danecek, P. et al. Twelve years of SAMtools and BCFtools. GigaScience https://doi.org/10.1093/gigascience/giab008 (2021).

  • Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pertea, G. & Pertea, M. GFF Utilities: GffRead and GffCompare. F10000Research 9, 304 (2020).

    Article 

    Google Scholar
     

  • Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. Preprint at https://doi.org/10.48550/arXiv.1207.3907 (2012).

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lautenbacher, L. et al. Koina: Democratizing machine learning for proteomics research. Nat. Commun. 16, 9933 (2025).

  • Huber, F. et al. matchms—processing and similarity evaluation of mass spectrometry data. J. Open Source Softw. 5, 2411 (2020).

    Article 
    ADS 

    Google Scholar
     

  • Wan, K. X., Vidavsky, I. & Gross, M. L. Comparing similar spectra: From similarity index to spectral contrast angle. J. Am. Soc. Mass Spectrom. 13, 85–88 (2002).

  • Halloran, J. T. & Rocke, D. M. Matter of time: faster percolator analysis via efficient SVM learning for large-scale proteomics. J. Proteome Res. 17, 1978–1982 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Huttlin, E. L. et al. The BioPlex network: a systematic exploration of the human interactome. Cell 162, 425–440 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Marino, A. et al. Aging and diet alter the protein ubiquitylation landscape in the mouse brain. Nat. Commun. 16, 5266 (2025).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, J. et al. Proteome-wide mapping of short-lived proteins in human cells. Mol. Cell 81, 4722–4735 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nettling, M. et al. DiffLogo: a comparative visualization of sequence motifs. BMC Bioinformatics 16, 387 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Behle, A. et al. Manipulation of topoisomerase expression inhibits cell division but not growth and reveals a distinctive promoter structure in Synechocystis. Nucleic Acids Res. 50, 12790–12808 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Erdős, G., Pajkos, M. & Dosztányi, Z. IUPred3: prediction of protein disorder enhanced with unambiguous experimental annotation and visualization of evolutionary conservation. Nucleic Acids Res. 49, W297–W303 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Eddy, S. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).

    Article 
    ADS 
    MathSciNet 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Moffat, L. & Jones, D. Increasing the accuracy of single sequence prediction methods using a deep semi-supervised learning framework. Bioinformatics 37, 3744–3751 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hu, G. et al. flDPnn: accurate intrinsic disorder prediction with putative propensities of disorder functions. Nat. Commun. 12, 4438 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Peng, Z. & Kurgan, L. High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder. Nucleic Acids Res. 43, e121 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Steinegger, M. & Soding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 1026–1028 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhao, B. et al. DescribePROT: database of amino acid-level protein structure and function predictions. Nucleic Acids Res. 49, D298–D308 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Meng, E. et al. UCSF ChimeraX: Tools for structure building and analysis. Protein Sci. 32, e4792 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16)785–794 (ACM, 2016).

  • Seplyarskiy, V. et al. A mutation rate model at the basepair resolution identifies the mutagenic effect of polymerase III transcription. Nat. Genet. 55, 2235–2242 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cheng, J. et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science 381, eadg7492 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • RELATED ARTICLES

    Most Popular

    Recent Comments