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A synthetic peptide library for benchmarking crosslinking-mass spectrometry search engines for proteins and protein complexes

Author

Listed:
  • Rebecca Beveridge

    (Vienna Biocenter (VBC))

  • Johannes Stadlmann

    (Vienna Biocenter (VBC))

  • Josef M. Penninger

    (Vienna Biocenter (VBC)
    Vancouver Campus)

  • Karl Mechtler

    (Vienna Biocenter (VBC)
    Vienna Biocenter (VBC))

Abstract

Crosslinking-mass spectrometry (XL-MS) serves to identify interaction sites between proteins. Numerous search engines for crosslink identification exist, but lack of ground truth samples containing known crosslinks has precluded their systematic validation. Here we report on XL-MS data arising from measuring synthetic peptide libraries that provide the unique benefit of knowing which identified crosslinks are true and which are false. The data are analysed with the most frequently used search engines and the results filtered to an estimated false discovery rate of 5%. We find that the actual false crosslink identification rates range from 2.4 to 32%, depending on the analysis strategy employed. Furthermore, the use of MS-cleavable crosslinkers does not reduce the false discovery rate compared to non-cleavable crosslinkers. We anticipate that the datasets acquired during this research will further drive optimisation and development of XL-MS search engines, thereby advancing our understanding of vital biological interactions.

Suggested Citation

  • Rebecca Beveridge & Johannes Stadlmann & Josef M. Penninger & Karl Mechtler, 2020. "A synthetic peptide library for benchmarking crosslinking-mass spectrometry search engines for proteins and protein complexes," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14608-2
    DOI: 10.1038/s41467-020-14608-2
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    Cited by:

    1. Manuel Matzinger & Adrian Vasiu & Mathias Madalinski & Fränze Müller & Florian Stanek & Karl Mechtler, 2022. "Mimicked synthetic ribosomal protein complex for benchmarking crosslinking mass spectrometry workflows," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    2. Jian-Hua Wang & Yu-Liang Tang & Zhou Gong & Rohit Jain & Fan Xiao & Yu Zhou & Dan Tan & Qiang Li & Niu Huang & Shu-Qun Liu & Keqiong Ye & Chun Tang & Meng-Qiu Dong & Xiaoguang Lei, 2022. "Characterization of protein unfolding by fast cross-linking mass spectrometry using di-ortho-phthalaldehyde cross-linkers," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    3. Hugo Gizardin-Fredon & Paulo E. Santo & Marie-Eve Chagot & Bruno Charpentier & Tiago M. Bandeiras & Xavier Manival & Oscar Hernandez-Alba & Sarah Cianférani, 2024. "Denaturing mass photometry for rapid optimization of chemical protein-protein cross-linking reactions," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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