Skip to content
2000
image of Applications and Prospects of Artificial Intelligence in Proteomics Via Mass Spectrometry: A Review

Abstract

Proteomics holds immense significance in fundamental and applied research in various fields, including life sciences, medicinal sciences, and pharmaceutical sciences. The rapid development of mass spectrometry (MS) technologies has facilitated MS-based proteomics research, which has emerged as one of the primary methods for determining the composition, structures, and functions of proteins. The necessity of processing these complex datasets has increased significantly owing to the growing volume and diversity of MS data pertaining to proteins. Artificial intelligence (AI) possesses powerful data processing abilities, and is being increasingly employed for handling these challenges. In particular, deep learning has been extensively employed in MS-based proteomics research. This review discusses and compares the different AI algorithms developed for various tasks, including the prediction of protein spectra, retention times, peptide sequences, and MS-based protein structure prediction, and highlights their respective strengths and weaknesses. The limitations and future prospects of AI in MS-based proteomics research are additionally discussed herein.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cpps/10.2174/0113892037368295250520102121
2025-06-05
2025-09-01

  • From This Site

    /content/journals/cpps/10.2174/0113892037368295250520102121
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Aebersold R. Mann M. Mass-spectrometric exploration of proteome structure and function. Nature 2016 537 7620 347 355 10.1038/nature19949 27629641
    [Google Scholar]
  2. Shuken S.R. An introduction to mass spectrometry-based proteomics. J. Proteome Res. 2023 22 7 2151 2171 10.1021/acs.jproteome.2c00838 37260118
    [Google Scholar]
  3. McCarthy J. Minsky M.L. Rochester N. Shannon C.E. A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI Mag. 2006 27 4 12 12
    [Google Scholar]
  4. Abbass H. Artificial intelligence: A new transactions. IEEE Trans. Artif. Intell. 2020 1 1 2 4 10.1109/TAI.2020.3035827
    [Google Scholar]
  5. Alzubi J. Nayyar A. Kumar A. Machine learning from theory to algorithms: An overview. J. Phys. Conf. Ser. 2018 1142 012012 10.1088/1742‑6596/1142/1/012012
    [Google Scholar]
  6. LeCun Y. Bengio Y. Hinton G. Deep Learning. Nature 2015 521 7553 436 10.1038/nature14539
    [Google Scholar]
  7. Goodfellow I. Pouget-Abadie J. Mirza M. Xu B. Warde-Farley D. Ozair S. Courville A. Bengio Y. Generative adversarial nets. Adv. Neural Inf. Process. Syst. 2014 27 27
    [Google Scholar]
  8. Vaswani A. Shazeer N. Parmar N. Uszkoreit J. Jones L. Gomez A.N. Kaiser Ł. Polosukhin I. Attention is all you need. Adv. Neural Inf. Process. Syst. 2017 30.
    [Google Scholar]
  9. Aebersold R. Mann M. Mass spectrometry-based proteomics. Nature 2003 422 6928 198 207 10.1038/nature01511 12634793
    [Google Scholar]
  10. Jiang Y. Rex D.A.B. Schuster D. Neely B.A. Rosano G.L. Volkmar N. Momenzadeh A. Peters-Clarke T.M. Egbert S.B. Kreimer S. Comprehensive overview of bottom-up proteomics using mass spectrometry. ACS Meas Sci Au. 2024 4 4 338 10.1021/acsmeasuresciau.3c00068
    [Google Scholar]
  11. Mann M. Kumar C. Zeng W.F. Strauss M.T. Artificial intelligence for proteomics and biomarker discovery. Cell Syst. 2021 12 8 759 770 10.1016/j.cels.2021.06.006 34411543
    [Google Scholar]
  12. Bandeira N. Clauser K.R. Pevzner P.A. Shotgun protein sequencing: assembly of peptide tandem mass spectra from mixtures of modified proteins. Mol. Cell. Proteomics 2007 6 7 1123 1134 10.1074/mcp.M700001‑MCP200 17446555
    [Google Scholar]
  13. Reinders J. Lewandrowski U. Moebius J. Wagner Y. Sickmann A. Challenges in mass spectrometry-based proteomics. Proteomics 2004 4 12 3686 3703 10.1002/pmic.200400869 15540203
    [Google Scholar]
  14. Angel T.E. Aryal U.K. Hengel S.M. Baker E.S. Kelly R.T. Robinson E.W. Smith R.D. Mass spectrometry-based proteomics: existing capabilities and future directions. Chem. Soc. Rev. 2012 41 10 3912 3928 10.1039/c2cs15331a 22498958
    [Google Scholar]
  15. Smith L.E. Rogowska-Wrzesinska A. The challenge of detecting modifications on proteins. Essays Biochem. 2020 64 1 135 153 10.1042/EBC20190055 31957791
    [Google Scholar]
  16. Chen C. Hou J. Tanner J.J. Cheng J. Bioinformatics methods for mass spectrometry-based proteomics data analysis. Int. J. Mol. Sci. 2020 21 8 2873 10.3390/ijms21082873 32326049
    [Google Scholar]
  17. Zhang C. Lu Y. Study on artificial intelligence: The state of the art and future prospects. J. Ind. Inf. Integr. 2021 23 100224 10.1016/j.jii.2021.100224
    [Google Scholar]
  18. Bengio Y. The consciousness prior. arXiv 2017
    [Google Scholar]
  19. Vellido A. The importance of interpretability and visualization in machine learning for applications in medicine and health care. Neural Comput. Appl. 2020 32 24 18069 18083 10.1007/s00521‑019‑04051‑w
    [Google Scholar]
  20. Moruz L. Käll L. Peptide retention time prediction. Mass Spectrom. Rev. 2017 36 5 615 623 10.1002/mas.21488 26799864
    [Google Scholar]
  21. Henneman A. Palmblad M. Retention time prediction and protein identification. Methods Mol Biol. 2020 2051 115 132 10.1007/978‑1‑4939‑9744‑2_4.
    [Google Scholar]
  22. Bączek T. Kaliszan R. Predictions of peptides’ retention times in reversed-phase liquid chromatography as a new supportive tool to improve protein identification in proteomics. Proteomics 2009 9 4 835 847 10.1002/pmic.200800544 19160394
    [Google Scholar]
  23. Wen B. Li K. Zhang Y. Zhang B. Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis. Nat. Commun. 2020 11 1 1759 10.1038/s41467‑020‑15456‑w 32273506
    [Google Scholar]
  24. Blank-Landeshammer B. Teichert I. Märker R. Nowrousian M. Kück U. Sickmann A. Combination of proteogenomics with peptide de novo sequencing identifies new genes and hidden posttranscriptional modifications. MBio 2019 10 5 e02367-19 10.1128/mBio.02367‑19 31615963
    [Google Scholar]
  25. Meek J.L. Prediction of peptide retention times in high-pressure liquid chromatography on the basis of amino acid composition. Proc. Natl. Acad. Sci. USA 1980 77 3 1632 1636 10.1073/pnas.77.3.1632 6929513
    [Google Scholar]
  26. Moruz L. Tomazela D. Käll L. Training, selection, and robust calibration of retention time models for targeted proteomics. J. Proteome Res. 2010 9 10 5209 5216 10.1021/pr1005058 20735070
    [Google Scholar]
  27. Maboudi A.H. Qiu X. The M. Käll L. Uncertainty estimation of predictions of peptides’ chromatographic retention times in shotgun proteomics. Bioinformatics 2017 33 4 508 513 10.1093/bioinformatics/btw619 27797755
    [Google Scholar]
  28. Ma C. Zhu Z. Ye J. Yang J. Pei J. Xu S. Zhou R. Yu C. Mo F. Wen B. DeepRT: Deep learning for peptide retention time prediction in proteomics. arXiv 2017
    [Google Scholar]
  29. Ma C. Ren Y. Yang J. Ren Z. Yang H. Liu S. Improved peptide retention time prediction in liquid chromatography through deep learning. Anal. Chem. 2018 90 18 10881 10888 10.1021/acs.analchem.8b02386 30114359
    [Google Scholar]
  30. Gessulat S. Schmidt T. Zolg D.P. Samaras P. Schnatbaum K. Zerweck J. Knaute T. Rechenberger J. Delanghe B. Huhmer A. Reimer U. Ehrlich H.C. Aiche S. Kuster B. Wilhelm M. Prosit: Proteome-wide prediction of peptide tandem mass spectra by deep learning. Nat. Methods 2019 16 6 509 518 10.1038/s41592‑019‑0426‑7 31133760
    [Google Scholar]
  31. Yang Y. Liu X. Shen C. Lin Y. Yang P. Qiao L. In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics. Nat. Commun. 2020 11 1 146 10.1038/s41467‑019‑13866‑z 31919359
    [Google Scholar]
  32. Krokhin O.V. Sequence-specific retention calculator. Algorithm for peptide retention prediction in ion-pair RP-HPLC: Application to 300- and 100-A pore size C18 sorbents. Anal. Chem. 2006 78 22 7785 7795 10.1021/ac060777w 17105172
    [Google Scholar]
  33. Guan S. Moran M.F. Ma B. Prediction of lc-ms/ms properties of peptides from sequence by deep learning*. Mol. Cell. Proteomics 2019 18 10 2099 2107 10.1074/mcp.TIR119.001412 31249099
    [Google Scholar]
  34. Bouwmeester R. Gabriels R. Hulstaert N. Martens L. Degroeve S. DeepLC can predict retention times for peptides that carry as-yet unseen modifications. Nat. Methods 2021 18 11 1363 1369 10.1038/s41592‑021‑01301‑5 34711972
    [Google Scholar]
  35. 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 2017 14 5 513 520 10.1038/nmeth.4256 28394336
    [Google Scholar]
  36. Chi H. Liu C. Yang H. Zeng W.F. Wu L. Zhou W.J. Wang R.M. Niu X.N. Ding Y.H. Zhang Y. Wang Z.W. Chen Z.L. Sun R.X. Liu T. Tan G.M. Dong M.Q. Xu P. Zhang P.H. He S.M. Comprehensive identification of peptides in tandem mass spectra using an efficient open search engine. Nat. Biotechnol. 2018 36 11 1059 1061 10.1038/nbt.4236 30295672
    [Google Scholar]
  37. Zeng W.F. Zhou X.X. Willems S. Ammar C. Wahle M. Bludau I. Voytik E. Strauss M.T. Mann M. AlphaPeptDeep: A modular deep learning framework to predict peptide properties for proteomics. Nat. Commun. 2022 13 1 7238 10.1038/s41467‑022‑34904‑3 36433986
    [Google Scholar]
  38. Meier F. Köhler N.D. Brunner A.D. Wanka J.M.H. Voytik E. Strauss M.T. Theis F.J. Mann M. Deep learning the collisional cross sections of the peptide universe from a million experimental values. Nat. Commun. 2021 12 1 1185 10.1038/s41467‑021‑21352‑8 33608539
    [Google Scholar]
  39. Rosenberger G. Koh C.C. Guo T. Röst H.L. Kouvonen P. Collins B.C. Heusel M. Liu Y. Caron E. Vichalkovski A. Faini M. Schubert O.T. Faridi P. Ebhardt H.A. Matondo M. Lam H. Bader S.L. Campbell D.S. Deutsch E.W. Moritz R.L. Tate S. Aebersold R. A repository of assays to quantify 10,000 human proteins by SWATH-MS. Sci. Data 2014 1 1 140031 10.1038/sdata.2014.31 25977788
    [Google Scholar]
  40. Giese S.H. Sinn L.R. Wegner F. Rappsilber J. Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry. Nat. Commun. 2021 12 1 3237 10.1038/s41467‑021‑23441‑0 34050149
    [Google Scholar]
  41. Wen B. Zeng W.F. Liao Y. Shi Z. Savage S.R. Jiang W. Zhang B. Deep learning in proteomics. Proteomics 2020 20 21-22 1900335 10.1002/pmic.201900335 32939979
    [Google Scholar]
  42. Zhao X. Zhang W. Xu X. Ma Z. Yin M. Prediction of protein phosphorylation sites by using the composition of k-spaced amino acid pairs. PLoS One. 2012 7 10 e46302 10.1371/journal.pone.0046302
    [Google Scholar]
  43. Shi Q. Chen W. Huang S. Wang Y. Xue Z. Deep learning for mining protein data. Brief. Bioinform. 2021 22 1 194 218 10.1093/bib/bbz156 31867611
    [Google Scholar]
  44. Lam H. Aebersold R. Building and searching tandem mass (MS/MS) spectral libraries for peptide identification in proteomics. Methods 2011 54 4 424 431 10.1016/j.ymeth.2011.01.007 21277371
    [Google Scholar]
  45. Lam H. Deutsch E.W. Eddes J.S. Eng J.K. Stein S.E. Aebersold R. Building consensus spectral libraries for peptide identification in proteomics. Nat. Methods 2008 5 10 873 875 10.1038/nmeth.1254 18806791
    [Google Scholar]
  46. Craig R. Cortens J.P. Beavis R.C. Open source system for analyzing, validating, and storing protein identification data. J. Proteome Res. 2004 3 6 1234 1242 10.1021/pr049882h 15595733
    [Google Scholar]
  47. Deutsch E.W. The peptideatlas project. Methods Mol Biol. 2010 604 285 296 10.1007/978‑1‑60761‑444‑9_19.
    [Google Scholar]
  48. Martens L. Hermjakob H. Jones P. Adamski M. Taylor C. States D. Gevaert K. Vandekerckhove J. Apweiler R. PRIDE: The proteomics identifications database. Proteomics 2005 5 13 3537 3545 10.1002/pmic.200401303 16041671
    [Google Scholar]
  49. Noor Z. Ahn S.B. Baker M.S. Ranganathan S. Mohamedali A. Mass spectrometry–based protein identification in proteomics: A review. Brief. Bioinform. 2021 22 2 1620 1638 10.1093/bib/bbz163 32047889
    [Google Scholar]
  50. Li S. Arnold R.J. Tang H. Radivojac P. On the accuracy and limits of peptide fragmentation spectrum prediction. Anal. Chem. 2011 83 3 790 796 10.1021/ac102272r 21175207
    [Google Scholar]
  51. Zhang Z. Prediction of low-energy collision-induced dissociation spectra of peptides. Anal. Chem. 2004 76 14 3908 3922 10.1021/ac049951b 15253624
    [Google Scholar]
  52. Zhang Z. Prediction of low-energy collision-induced dissociation spectra of peptides with three or more charges. Anal. Chem. 2005 77 19 6364 6373 10.1021/ac050857k 16194101
    [Google Scholar]
  53. Sun S. Yang F. Yang Q. Zhang H. Wang Y. Bu D. Ma B. MS-Simulator: Predicting y-ion intensities for peptides with two charges based on the intensity ratio of neighboring ions. J. Proteome Res. 2012 11 9 4509 4516 10.1021/pr300235v 22794508
    [Google Scholar]
  54. Degroeve S. Martens L. MS2PIP: A tool for MS/MS peak intensity prediction. Bioinformatics 2013 29 24 3199 3203 10.1093/bioinformatics/btt544 24078703
    [Google Scholar]
  55. Gabriels R. Martens L. Degroeve S. Updated MS²PIP web server delivers fast and accurate MS² peak intensity prediction for multiple fragmentation methods, instruments and labeling techniques. Nucleic Acids Res. 2019 47 W1 W295 W299 10.1093/nar/gkz299 31028400
    [Google Scholar]
  56. Dong N. Liang Y.Z. Xu Q. Mok D.K.W. Yi L. Lu H. He M. Fan W. Prediction of peptide fragment ion mass spectra by data mining techniques. Anal. Chem. 2014 86 15 7446 7454 10.1021/ac501094m 25032905
    [Google Scholar]
  57. Zhou X.X. Zeng W.F. Chi H. Luo C. Liu C. Zhan J. He S.M. Zhang Z. pDeep: Predicting MS/MS spectra of peptides with deep learning. Anal. Chem. 2017 89 23 12690 12697 10.1021/acs.analchem.7b02566 29125736
    [Google Scholar]
  58. Sundermeyer M. Ney H. Schlüter R. From feedforward to recurrent LSTM neural networks for language modeling. IEEE/ACM Trans. Audio Speech Lang. Process. 2015 23 3 517 529 10.1109/TASLP.2015.2400218
    [Google Scholar]
  59. Zeng W.F. Zhou X.X. Zhou W.J. Chi H. Zhan J. He S.M. MS/MS spectrum prediction for modified peptides using pDeep2 trained by transfer learning. Anal. Chem. 2019 91 15 9724 9731 10.1021/acs.analchem.9b01262 31283184
    [Google Scholar]
  60. Tarn C. Zeng W.F. pDeep3: Toward more accurate spectrum prediction with fast few-shot learning. Anal. Chem. 2021 93 14 5815 5822 10.1021/acs.analchem.0c05427 33797898
    [Google Scholar]
  61. Wang Y. Yao Q. Kwok J.T. Ni L.M. Generalizing from a few examples: A survey on few-shot learning. ACM Comput. Surv. 2021 53 3 1 34 [csur]. 10.1145/3386252
    [Google Scholar]
  62. Tiwary S. Levy R. Gutenbrunner P. Salinas S.F. Palaniappan K.K. Deming L. Berndl M. Brant A. Cimermancic P. Cox J. High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis. Nat. Methods 2019 16 6 519 525 10.1038/s41592‑019‑0427‑6 31133761
    [Google Scholar]
  63. Zolg D.P. Wilhelm M. Schnatbaum K. Zerweck J. Knaute T. Delanghe B. Bailey D.J. Gessulat S. Ehrlich H.C. Weininger M. Yu P. Schlegl J. Kramer K. Schmidt T. Kusebauch U. Deutsch E.W. Aebersold R. Moritz R.L. Wenschuh H. Moehring T. Aiche S. Huhmer A. Reimer U. Kuster B. Building ProteomeTools based on a complete synthetic human proteome. Nat. Methods 2017 14 3 259 262 10.1038/nmeth.4153 28135259
    [Google Scholar]
  64. Ekvall M. Truong P. Gabriel W. Wilhelm M. Käll L. Prosit transformer: A transformer for prediction of MS2 spectrum intensities. J. Proteome Res. 2022 21 5 1359 1364 10.1021/acs.jproteome.1c00870 35413196
    [Google Scholar]
  65. Rao R. Bhattacharya N. Thomas N. Duan Y. Chen X. Canny J. Abbeel P. Song Y.S. Evaluating protein transfer learning with TAPE. Adv. Neural Inf. Process. Syst. 2019 32 9689 9701 33390682
    [Google Scholar]
  66. Wolters D.A. Washburn M.P. Yates J.R. III An automated multidimensional protein identification technology for shotgun proteomics. Anal. Chem. 2001 73 23 5683 5690 10.1021/ac010617e 11774908
    [Google Scholar]
  67. Doerr A. DIA mass spectrometry. Nat. Methods 2015 12 1 35 35 10.1038/nmeth.3234 25699317
    [Google Scholar]
  68. Meyer J. G. Deep learning neural network tools for proteomics. Cell Rep Methods. 2021 1 2 100003 10.1016/j.crmeth.2021.100003.
    [Google Scholar]
  69. Lin Y.M. Chen C.T. Chang J.M. MS2CNN: predicting MS/MS spectrum based on protein sequence using deep convolutional neural networks. BMC Genomics 2019 20 S9 Suppl. 9 906 10.1186/s12864‑019‑6297‑6 31874640
    [Google Scholar]
  70. Liu K. Li S. Wang L. Ye Y. Tang H. Full-spectrum prediction of peptides tandem mass spectra using deep neural network. Anal. Chem. 2020 92 6 4275 4283 10.1021/acs.analchem.9b04867 32053352
    [Google Scholar]
  71. Wolf T. Debut L. Sanh V. Chaumond J. Delangue C. Moi A. Cistac P. Rault T. Louf R. Funtowicz M. Transformers: State-of-the-art natural language processing. Proceedings of the 2020 conference on empirical methods in natural language processing: System demonstrations Brooklyn, USA, November 16-20-2020, pp. 38-45. 10.18653/v1/2020.emnlp‑demos.6
    [Google Scholar]
  72. Chen Z.L. Mao P.Z. Zeng W.F. Chi H. He S.M. pDeepXL: MS/MS spectrum prediction for cross-linked peptide pairs by deep learning. J. Proteome Res. 2021 20 5 2570 2582 10.1021/acs.jproteome.0c01004 33821641
    [Google Scholar]
  73. Lou R. Liu W. Li R. Li S. He X. Shui W. DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation. Nat. Commun. 2021 12 1 6685 10.1038/s41467‑021‑26979‑1 34795227
    [Google Scholar]
  74. Zong Y. Wang Y. Yang Y. Zhao D. Wang X. Shen C. Qiao L. DeepFLR facilitates false localization rate control in phosphoproteomics. Nat. Commun. 2023 14 1 2269 10.1038/s41467‑023‑38035‑1 37080984
    [Google Scholar]
  75. Yang Y. Fang Q. Prediction of glycopeptide fragment mass spectra by deep learning. Nat. Commun. 2024 15 1 2448 10.1038/s41467‑024‑46771‑1 38503734
    [Google Scholar]
  76. Beslic D. Tscheuschner G. Renard B.Y. Weller M.G. Muth T. Current state, existing challenges, and promising progress for de novo sequencing and assembly of monoclonal antibodies. bioRxiv 2022 2022.2007
    [Google Scholar]
  77. Vitorino R. Guedes S. Trindade F. Correia I. Moura G. Carvalho P. Santos M.A.S. Amado F. De novo sequencing of proteins by mass spectrometry. Expert Rev. Proteomics 2020 17 7-8 595 607 10.1080/14789450.2020.1831387 33016158
    [Google Scholar]
  78. Fischer B. Roth V. Roos F. Grossmann J. Baginsky S. Widmayer P. Gruissem W. Buhmann J.M. NovoHMM: A hidden Markov model for de novo peptide sequencing. Anal. Chem. 2005 77 22 7265 7273 10.1021/ac0508853 16285674
    [Google Scholar]
  79. Ma B. Novor: Real-time peptide de novo sequencing software. J. Am. Soc. Mass Spectrom. 2015 26 11 1885 1894 10.1007/s13361‑015‑1204‑0 26122521
    [Google Scholar]
  80. Tran N.H. Zhang X. Xin L. Shan B. Li M. De novo peptide sequencing by deep learning. Proc. Natl. Acad. Sci. USA 2017 114 31 8247 8252 10.1073/pnas.1705691114 28720701
    [Google Scholar]
  81. McDonnell K. Howley E. Abram F. The impact of noise and missing fragmentation cleavages on de novo peptide identification algorithms. Comput. Struct. Biotechnol. J. 2022 20 1402 1412 10.1016/j.csbj.2022.03.008 35386104
    [Google Scholar]
  82. Tran N.H. Qiao R. Xin L. Chen X. Liu C. Zhang X. Shan B. Ghodsi A. Li M. Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry. Nat. Methods 2019 16 1 63 66 10.1038/s41592‑018‑0260‑3 30573815
    [Google Scholar]
  83. Karunratanakul K. Tang H.Y. Speicher D.W. Chuangsuwanich E. Sriswasdi S. Uncovering thousands of new peptides with sequence-mask-search hybrid de novo peptide sequencing framework. Mol. Cell. Proteomics 2019 18 12 2478 2491 10.1074/mcp.TIR119.001656 31591261
    [Google Scholar]
  84. Qiao R. Tran N.H. Xin L. Chen X. Li M. Shan B. Ghodsi A. Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices. Nat. Mach. Intell. 2021 3 5 420 425 10.1038/s42256‑021‑00304‑3
    [Google Scholar]
  85. Xu X. Yang C. He Q. Shu K. Xinpu Y. Chen Z. Zhu Y. Chen T. PGPointNovo: An efficient neural network-based tool for parallel de novo peptide sequencing. Bioinformatics 2023 3 1 vbad057
    [Google Scholar]
  86. Ge C. Lu Y. Qu J. Xie L. Wang F. Zhang H. Kong R. Chang S. DePS: An improved deep learning model for de novo peptide sequencing. arXiv 2022
    [Google Scholar]
  87. Liu K. Ye Y. Li S. Tang H. Accurate de novo peptide sequencing using fully convolutional neural networks. Nat. Commun. 2023 14 1 7974 10.1038/s41467‑023‑43010‑x 38042873
    [Google Scholar]
  88. Yilmaz M. Fondrie W.E. Bittremieux W. Melendez C.F. Nelson R. Ananth V. Oh S. Noble W.S. Sequence-to-sequence translation from mass spectra to peptides with a transformer model. BioRxiv 2023 2023.2001
    [Google Scholar]
  89. Yilmaz M. Fondrie W. Bittremieux W. Oh S. Noble W.S. De novo mass spectrometry peptide sequencing with a transformer model. International Conference on Machine Learning Baltimore, Maryland, USA, 2022, pp. 25514-25522. 10.1101/2022.02.07.479481
    [Google Scholar]
  90. Avsec Ž. Agarwal V. Visentin D. Ledsam J.R. Grabska-Barwinska A. Taylor K.R. Assael Y. Jumper J. Kohli P. Kelley D.R. Effective gene expression prediction from sequence by integrating long-range interactions. Nat. Methods 2021 18 10 1196 1203 10.1038/s41592‑021‑01252‑x 34608324
    [Google Scholar]
  91. Ebrahimi S. Guo X. Transformer-based de novo peptide sequencing for data-independent acquisition mass spectrometry. Proc IEEE Int Symp Bioinformatics Bioeng. 2023 2023 28 35 10.1109/BIBE60311.2023.00013
    [Google Scholar]
  92. Wu S. Luan Z. Fu Z. Wang Q. Guo T. BiATNovo: A Self-Attention based Bidirectional Peptide Sequencing Method. bioRxiv 2023 2023.2005
    [Google Scholar]
  93. Lee S. Kim H. Bidirectional de novo peptide sequencing using a transformer model. PLOS Comput. Biol. 2024 20 2 e1011892 10.1371/journal.pcbi.1011892 38416757
    [Google Scholar]
  94. Yang T. Ling T. Sun B. Liang Z. Xu F. Huang X. Xie L. He Y. Li L. He F. Wang Y. Chang C. Introducing π-HelixNovo for practical large-scale de novo peptide sequencing. Brief. Bioinform. 2024 25 2 bbae021 10.1093/bib/bbae021 38340092
    [Google Scholar]
  95. Xia J. Chen S. Zhou J. Lin T. Du W. Li S. AdaNovo: Adaptive de novo peptide sequencing with conditional mutual information. arXiv 2024
    [Google Scholar]
  96. Mao Z. Zhang R. Xin L. Li M. Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model. Nat. Mach. Intell. 2023 5 11 1250 1260 10.1038/s42256‑023‑00738‑x
    [Google Scholar]
  97. Jin Z. Xu S. Zhang X. Ling T. Dong N. Ouyang W. Gao Z. Chang C. Sun S. Contranovo: A contrastive learning approach to enhance de novo peptide sequencing. Proc. Conf. AAAI Artif. Intell. 2024 38 1 144 152 10.1609/aaai.v38i1.27765
    [Google Scholar]
  98. Eloff K. Kalogeropoulos K. Morell O. Mabona A. Jespersen J.B. Williams W. van Beljouw S.P. Skwark M. Laustsen A.H. Brouns S.J. De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments. bioRxiv 2023
    [Google Scholar]
  99. Wang K. Zhu M. Boulila W. Driss M. Gadekallu T.R. Chen C-M. Wang L. Kumari S. Yiu S-M. SeqNovo: de novo peptide sequencing prediction in IoMT via Seq2Seq. IEEE J. Biomed. Health Inform. 2023 PP 37792659
    [Google Scholar]
  100. Klaproth-Andrade D. Hingerl J. Bruns Y. Smith N.H. Träuble J. Wilhelm M. Gagneur J. Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing. Nat. Commun. 2024 15 1 151 10.1038/s41467‑023‑44323‑7 38167372
    [Google Scholar]
  101. Dorfer V. Maltsev S. Winkler S. Mechtler K. CharmeRT: Boosting peptide identifications by chimeric spectra identification and retention time prediction. J. Proteome Res. 2018 17 8 2581 2589 10.1021/acs.jproteome.7b00836 29863353
    [Google Scholar]
  102. Driver T. Bachhawat N. Frasinski L.J. Marangos J.P. Averbukh V. Edelson-Averbukh M. Chimera spectrum diagnostics for peptides using two-dimensional partial covariance mass spectrometry. Molecules 2021 26 12 3728 10.3390/molecules26123728 34207274
    [Google Scholar]
  103. Houel S. Abernathy R. Renganathan K. Meyer-Arendt K. Ahn N.G. Old W.M. Quantifying the impact of chimera MS/MS spectra on peptide identification in large-scale proteomics studies. J. Proteome Res. 2010 9 8 4152 4160 10.1021/pr1003856 20578722
    [Google Scholar]
  104. Guthals A. Clauser K.R. Bandeira N. Shotgun protein sequencing with meta-contig assembly. Mol. Cell. Proteomics 2012 11 10 1084 1096 10.1074/mcp.M111.015768 22798278
    [Google Scholar]
  105. Tran N.H. Rahman M.Z. He L. Xin L. Shan B. Li M. Complete de novo assembly of monoclonal antibody sequences. Sci. Rep. 2016 6 1 31730 10.1038/srep31730 27562653
    [Google Scholar]
  106. Mai Z.B. Zhou Z.H. He Q.Y. Zhang G. Highly robust de novo full-length protein sequencing. Anal. Chem. 2022 94 8 3467 3475 10.1021/acs.analchem.1c03718 35171581
    [Google Scholar]
  107. Beslic D. Tscheuschner G. Renard B.Y. Weller M.G. Muth T. Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly. Brief. Bioinform. 2023 24 1 bbac542 10.1093/bib/bbac542 36545804
    [Google Scholar]
  108. Biehn S.E. Lindert S. Protein structure prediction with mass spectrometry data. Annu. Rev. Phys. Chem. 2022 73 1 1 19 10.1146/annurev‑physchem‑082720‑123928 34724394
    [Google Scholar]
  109. Smyth M.S. Martin J.H. x Ray crystallography. Mol. Pathol. 2000 53 1 8 14 10.1136/mp.53.1.8 10884915
    [Google Scholar]
  110. Cavalli A. Salvatella X. Dobson C.M. Vendruscolo M. Protein structure determination from NMR chemical shifts. Proc. Natl. Acad. Sci. USA 2007 104 23 9615 9620 10.1073/pnas.0610313104 17535901
    [Google Scholar]
  111. Ignatiou A. Macé K. Redzej A. Costa T. R. Waksman G. Orlova E. V. Structural analysis of protein complexes by cryo-electron microscopy. Bacterial Secretion Systems 2023 Springer 431 470 10.1007/978‑1‑0716‑3445‑5_27
    [Google Scholar]
  112. O’Reilly F.J. Rappsilber J. Cross-linking mass spectrometry: methods and applications in structural, molecular and systems biology. Nat. Struct. Mol. Biol. 2018 25 11 1000 1008 10.1038/s41594‑018‑0147‑0 30374081
    [Google Scholar]
  113. Jurneczko E. Barran P.E. How useful is ion mobility mass spectrometry for structural biology? The relationship between protein crystal structures and their collision cross sections in the gas phase. Analyst (Lond.) 2011 136 1 20 28 10.1039/C0AN00373E 20820495
    [Google Scholar]
  114. Drake Z.C. Seffernick J.T. Lindert S. Protein complex prediction using Rosetta, AlphaFold, and mass spectrometry covalent labeling. Nat. Commun. 2022 13 1 7846 10.1038/s41467‑022‑35593‑8 36543826
    [Google Scholar]
  115. Senior A.W. Evans R. Jumper J. Kirkpatrick J. Sifre L. Green T. Qin C. Žídek A. Nelson A.W.R. Bridgland A. Penedones H. Petersen S. Simonyan K. Crossan S. Kohli P. Jones D.T. Silver D. Kavukcuoglu K. Hassabis D. Improved protein structure prediction using potentials from deep learning. Nature 2020 577 7792 706 710 10.1038/s41586‑019‑1923‑7 31942072
    [Google Scholar]
  116. Jumper J. Evans R. Pritzel A. Green T. Figurnov M. Ronneberger O. Tunyasuvunakool K. Bates R. Žídek A. Potapenko A. Bridgland A. Meyer C. Kohl S.A.A. Ballard A.J. Cowie A. Romera-Paredes B. Nikolov S. Jain R. Adler J. Back T. Petersen S. Reiman D. Clancy E. Zielinski M. Steinegger M. Pacholska M. Berghammer T. Bodenstein S. Silver D. Vinyals O. Senior A.W. Kavukcuoglu K. Kohli P. Hassabis D. Highly accurate protein structure prediction with AlphaFold. Nature 2021 596 7873 583 589 10.1038/s41586‑021‑03819‑2 34265844
    [Google Scholar]
  117. Kryshtafovych A. Schwede T. Topf M. Fidelis K. Moult J. Critical assessment of methods of protein structure prediction (CASP)—Round XIII. Proteins 2019 87 12 1011 1020 10.1002/prot.25823 31589781
    [Google Scholar]
  118. Kryshtafovych A. Schwede T. Topf M. Fidelis K. Moult J. Critical assessment of methods of protein structure prediction (CASP)—Round XIV. Proteins 2021 89 12 1607 1617 10.1002/prot.26237 34533838
    [Google Scholar]
  119. Turzo S.M.B.A. Seffernick J.T. Rolland A.D. Donor M.T. Heinze S. Prell J.S. Wysocki V.H. Lindert S. Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction. Nat. Commun. 2022 13 1 4377 10.1038/s41467‑022‑32075‑9 35902583
    [Google Scholar]
  120. Stahl K. Graziadei A. Dau T. Brock O. Rappsilber J. Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning. Nat. Biotechnol. 2023 41 12 1810 1819 10.1038/s41587‑023‑01704‑z 36941363
    [Google Scholar]
  121. Graziadei A. Rappsilber J. Leveraging crosslinking mass spectrometry in structural and cell biology. Structure 2022 30 1 37 54 10.1016/j.str.2021.11.007 34895473
    [Google Scholar]
  122. Leitner A. Faini M. Stengel F. Aebersold R. Crosslinking and mass spectrometry: an integrated technology to understand the structure and function of molecular machines. Trends Biochem. Sci. 2016 41 1 20 32 10.1016/j.tibs.2015.10.008 26654279
    [Google Scholar]
  123. Belsom A. Schneider M. Fischer L. Brock O. Rappsilber J. Serum albumin domain structures in human blood serum by mass spectrometry and computational biology. Mol. Cell. Proteomics 2016 15 3 1105 1116 10.1074/mcp.M115.048504 26385339
    [Google Scholar]
  124. Feng S. Chen Z. Zhang C. Xie Y. Ovchinnikov S. Gao Y.Q. Liu S. Integrated structure prediction of protein–protein docking with experimental restraints using ColabDock. Nat. Mach. Intell. 2024 6 8 924 935 10.1038/s42256‑024‑00873‑z
    [Google Scholar]
  125. Xie Y. Zhang C. Li S. Du X. Wang M. Hu Y. Liu S. Gao Y.Q. Integrating various experimental information to assist protein complex structure prediction by GRASP. bioRxiv 2024
    [Google Scholar]
  126. Cui F. Zhang Z. Zou Q. Sequence representation approaches for sequence-based protein prediction tasks that use deep learning. Brief. Funct. Genomics 2021 20 1 61 73 10.1093/bfgp/elaa030 33527980
    [Google Scholar]
  127. Ribeiro M.T. Singh S. Guestrin C. Why should i trust you?” Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining San Francisco, California, USA, 2016, pp. 1135–1144 10.18653/v1/N16‑3020
    [Google Scholar]
  128. Ribeiro M.T. Singh S. Guestrin C. Anchors: High-precision model-agnostic explanations. Proc Int AAAI Conf.CAI 2018 32 1 10.1609/aaai.v32i1.11491
    [Google Scholar]
  129. Nagaraj N. Alexander Kulak N. Cox J. Neuhauser N. Mayr K. Hoerning O. Vorm O. Mann M. System-wide perturbation analysis with nearly complete coverage of the yeast proteome by single-shot ultra HPLC runs on a bench top Orbitrap. Mol. Cell. Proteomics 2012 11 3 M111.013722 10.1074/mcp.M111.013722 22021278
    [Google Scholar]
  130. Robles M.S. Cox J. Mann M. In-vivo quantitative proteomics reveals a key contribution of post-transcriptional mechanisms to the circadian regulation of liver metabolism. PLoS Genet. 2014 10 1 e1004047 10.1371/journal.pgen.1004047 24391516
    [Google Scholar]
  131. Sharma K. D’Souza R.C.J. Tyanova S. Schaab C. Wiśniewski J.R. Cox J. Mann M. Ultradeep human phosphoproteome reveals a distinct regulatory nature of Tyr and Ser/Thr-based signaling. Cell Rep. 2014 8 5 1583 1594 10.1016/j.celrep.2014.07.036 25159151
    [Google Scholar]
  132. Gussakovsky D. Neustaeter H. Spicer V. Krokhin O.V. Sequence-specific model for peptide retention time prediction in strong cation exchange chromatography. Anal. Chem. 2017 89 21 11795 11802 10.1021/acs.analchem.7b03436 28971681
    [Google Scholar]
  133. Spicer V. Krokhin O.V. Peptide retention time prediction in hydrophilic interaction liquid chromatography. Comparison of separation selectivity between bare silica and bonded stationary phases. J. Chromatogr. A 2018 1534 75 84 10.1016/j.chroma.2017.12.046 29306631
    [Google Scholar]
  134. Bruderer R. Bernhardt O.M. Gandhi T. Xuan Y. Sondermann J. Schmidt M. Gomez-Varela D. Reiter L. Optimization of experimental parameters in data-independent mass spectrometry significantly increases depth and reproducibility of results. Mol. Cell. Proteomics 2017 16 12 2296 2309 10.1074/mcp.RA117.000314 29070702
    [Google Scholar]
  135. Kelstrup C.D. Bekker-Jensen D.B. Arrey T.N. Hogrebe A. Harder A. Olsen J.V. Performance evaluation of the Q exactive HF-X for shotgun proteomics. J. Proteome Res. 2018 17 1 727 738 10.1021/acs.jproteome.7b00602 29183128
    [Google Scholar]
  136. Li W. Chi H. Salovska B. Wu C. Sun L. Rosenberger G. Liu Y. Assessing the relationship between mass window width and retention time scheduling on protein coverage for data-independent acquisition. J. Am. Soc. Mass Spectrom. 2019 30 8 1396 1405 10.1007/s13361‑019‑02243‑1 31147889
    [Google Scholar]
  137. Wang D. Eraslan B. Wieland T. Hallström B. Hopf T. Zolg D.P. Zecha J. Asplund A. Li L. Meng C. Frejno M. Schmidt T. Schnatbaum K. Wilhelm M. Ponten F. Uhlen M. Gagneur J. Hahne H. Kuster B. A deep proteome and transcriptome abundance atlas of 29 healthy human tissues. Mol. Syst. Biol. 2019 15 2 e8503 10.15252/msb.20188503 30777892
    [Google Scholar]
  138. Jarnuczak A.F. Lee D.C.H. Lawless C. Holman S.W. Eyers C.E. Hubbard S.J. Analysis of intrinsic peptide detectability via integrated label-free and SRM-based absolute quantitative proteomics. J. Proteome Res. 2016 15 9 2945 2959 10.1021/acs.jproteome.6b00048 27454336
    [Google Scholar]
  139. Mucha S. Heinzlmeir S. Kriechbaumer V. Strickland B. Kirchhelle C. Choudhary M. Kowalski N. Eichmann R. Hueckelhoven R. Grill E. Kuster B. Glawischnig E. The formation of a camalexin-biosynthetic metabolon. Plant Cell 2019 31 11 tpc.00403.2019 10.1105/tpc.19.00403 31511315
    [Google Scholar]
  140. Zolg D.P. Wilhelm M. Schmidt T. Médard G. Zerweck J. Knaute T. Wenschuh H. Reimer U. Schnatbaum K. Kuster B. ProteomeTools: Systematic characterization of 21 post-translational protein modifications by liquid chromatography tandem mass spectrometry (LC-MS/MS) using synthetic peptides. Mol. Cell. Proteomics 2018 17 9 1850 1863 10.1074/mcp.TIR118.000783 29848782
    [Google Scholar]
  141. Okuda S. Watanabe Y. Moriya Y. Kawano S. Yamamoto T. Matsumoto M. Takami T. Kobayashi D. Araki N. Yoshizawa A.C. Tabata T. Sugiyama N. Goto S. Ishihama Y. JPOSTrepo: An international standard data repository for proteomes. Nucleic Acids Res. 2017 45 D1 D1107 D1111 10.1093/nar/gkw1080 27899654
    [Google Scholar]
  142. Wang S. Li W. Hu L. Cheng J. Yang H. Liu Y. NAguideR: Performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses. Nucleic Acids Res. 2020 48 14 e83 e83 10.1093/nar/gkaa498 32526036
    [Google Scholar]
  143. Huang Y. Triscari J.M. Tseng G.C. Pasa-Tolic L. Lipton M.S. Smith R.D. Wysocki V.H. Statistical characterization of the charge state and residue dependence of low-energy CID peptide dissociation patterns. Anal. Chem. 2005 77 18 5800 5813 10.1021/ac0480949 16159109
    [Google Scholar]
  144. Resing K.A. Meyer-Arendt K. Mendoza A.M. Aveline-Wolf L.D. Jonscher K.R. Pierce K.G. Old W.M. Cheung H.T. Russell S. Wattawa J.L. Goehle G.R. Knight R.D. Ahn N.G. Improving reproducibility and sensitivity in identifying human proteins by shotgun proteomics. Anal. Chem. 2004 76 13 3556 3568 10.1021/ac035229m 15228325
    [Google Scholar]
  145. Li Y.F. Arnold R.J. Tang H. Radivojac P. The importance of peptide detectability for protein identification, quantification, and experiment design in MS/MS proteomics. J. Proteome Res. 2010 9 12 6288 6297 10.1021/pr1005586 21067214
    [Google Scholar]
  146. Helsens K. Colaert N. Barsnes H. Muth T. Flikka K. Staes A. Timmerman E. Wortelkamp S. Sickmann A. Vandekerckhove J. Gevaert K. Martens L. ms_lims, a simple yet powerful open source laboratory information management system for MS-driven proteomics. Proteomics 2010 10 6 1261 1264 10.1002/pmic.200900409 20058248
    [Google Scholar]
  147. Paulovich A.G. Billheimer D. Ham A.J.L. Vega-Montoto L. Rudnick P.A. Tabb D.L. Wang P. Blackman R.K. Bunk D.M. Cardasis H.L. Clauser K.R. Kinsinger C.R. Schilling B. Tegeler T.J. Variyath A.M. Wang M. Whiteaker J.R. Zimmerman L.J. Fenyo D. Carr S.A. Fisher S.J. Gibson B.W. Mesri M. Neubert T.A. Regnier F.E. Rodriguez H. Spiegelman C. Stein S.E. Tempst P. Liebler D.C. Interlaboratory study characterizing a yeast performance standard for benchmarking LC-MS platform performance. Mol. Cell. Proteomics 2010 9 2 242 254 10.1074/mcp.M900222‑MCP200 19858499
    [Google Scholar]
  148. Klimek J. Eddes J.S. Hohmann L. Jackson J. Peterson A. Letarte S. Gafken P.R. Katz J.E. Mallick P. Lee H. Schmidt A. Ossola R. Eng J.K. Aebersold R. Martin D.B. The standard protein mix database: a diverse data set to assist in the production of improved Peptide and protein identification software tools. J. Proteome Res. 2008 7 1 96 103 10.1021/pr070244j 17711323
    [Google Scholar]
  149. Sharma K. Schmitt S. Bergner C.G. Tyanova S. Kannaiyan N. Manrique-Hoyos N. Kongi K. Cantuti L. Hanisch U.K. Philips M.A. Rossner M.J. Mann M. Simons M. Cell type– and brain region–resolved mouse brain proteome. Nat. Neurosci. 2015 18 12 1819 1831 10.1038/nn.4160 26523646
    [Google Scholar]
  150. Kulak N.A. Pichler G. Paron I. Nagaraj N. Mann M. Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells. Nat. Methods 2014 11 3 319 324 10.1038/nmeth.2834 24487582
    [Google Scholar]
  151. Chick J.M. Kolippakkam D. Nusinow D.P. Zhai B. Rad R. Huttlin E.L. Gygi S.P. A mass-tolerant database search identifies a large proportion of unassigned spectra in shotgun proteomics as modified peptides. Nat. Biotechnol. 2015 33 7 743 749 10.1038/nbt.3267 26076430
    [Google Scholar]
  152. Kim M.S. Pinto S.M. Getnet D. Nirujogi R.S. Manda S.S. Chaerkady R. Madugundu A.K. Kelkar D.S. Isserlin R. Jain S. Thomas J.K. Muthusamy B. Leal-Rojas P. Kumar P. Sahasrabuddhe N.A. Balakrishnan L. Advani J. George B. Renuse S. Selvan L.D.N. Patil A.H. Nanjappa V. Radhakrishnan A. Prasad S. Subbannayya T. Raju R. Kumar M. Sreenivasamurthy S.K. Marimuthu A. Sathe G.J. Chavan S. Datta K.K. Subbannayya Y. Sahu A. Yelamanchi S.D. Jayaram S. Rajagopalan P. Sharma J. Murthy K.R. Syed N. Goel R. Khan A.A. Ahmad S. Dey G. Mudgal K. Chatterjee A. Huang T.C. Zhong J. Wu X. Shaw P.G. Freed D. Zahari M.S. Mukherjee K.K. Shankar S. Mahadevan A. Lam H. Mitchell C.J. Shankar S.K. Satishchandra P. Schroeder J.T. Sirdeshmukh R. Maitra A. Leach S.D. Drake C.G. Halushka M.K. Prasad T.S.K. Hruban R.H. Kerr C.L. Bader G.D. Iacobuzio-Donahue C.A. Gowda H. Pandey A. A draft map of the human proteome. Nature 2014 509 7502 575 581 10.1038/nature13302 24870542
    [Google Scholar]
  153. Bekker-Jensen D. B. Kelstrup C. D. Batth T. S. Larsen S. C. Haldrup C. Bramsen J. B. Sørensen K. D. Høyer S. Ørntoft T. F. Andersen C. L. An optimized shotgun strategy for the rapid generation of comprehensive human proteomes. Cell Syst. 2017 4 6 587 10.1016/j.cels.2017.05.009
    [Google Scholar]
  154. Wilhelm M. Schlegl J. Hahne H. Gholami A.M. Lieberenz M. Savitski M.M. Ziegler E. Butzmann L. Gessulat S. Marx H. Mathieson T. Lemeer S. Schnatbaum K. Reimer U. Wenschuh H. Mollenhauer M. Slotta-Huspenina J. Boese J.H. Bantscheff M. Gerstmair A. Faerber F. Kuster B. Mass-spectrometry-based draft of the human proteome. Nature 2014 509 7502 582 587 10.1038/nature13319 24870543
    [Google Scholar]
  155. Marx H. Lemeer S. Schliep J.E. Matheron L. Mohammed S. Cox J. Mann M. Heck A.J.R. Kuster B. A large synthetic peptide and phosphopeptide reference library for mass spectrometry–based proteomics. Nat. Biotechnol. 2013 31 6 557 564 10.1038/nbt.2585 23685481
    [Google Scholar]
  156. Tsou C.C. Tsai C.F. Teo G.C. Chen Y.J. Nesvizhskii A.I. Untargeted, spectral library-free analysis of data-independent acquisition proteomics data generated using Orbitrap mass spectrometers. Proteomics 2016 16 15-16 2257 2271 10.1002/pmic.201500526 27246681
    [Google Scholar]
  157. Yang X. Neta P. Stein S.E. Extending a tandem mass spectral library to include MS2 spectra of fragment ions produced in-source and MSn spectra. J. Am. Soc. Mass Spectrom. 2017 28 11 2280 2287 10.1007/s13361‑017‑1748‑2 28721670
    [Google Scholar]
  158. Wang M. Wang J. Carver J. Pullman B. S. Cha S. W. Bandeira N. Assembling the community-scale discoverable human proteome. Cell systems 2018 7 4 412 10.1016/j.cels.2018.08.004
    [Google Scholar]
  159. Linden A. Deckers M. Parfentev I. Pflanz R. Homberg B. Neumann P. Ficner R. Rehling P. Urlaub H. A cross-linking mass spectrometry approach defines protein interactions in yeast mitochondria. Mol. Cell. Proteomics 2020 19 7 1161 1178 10.1074/mcp.RA120.002028 32332106
    [Google Scholar]
  160. Parfentev I. Schilbach S. Cramer P. Urlaub H. An experimentally generated peptide database increases the sensitivity of XL-MS with complex samples. J. Proteomics 2020 220 103754 10.1016/j.jprot.2020.103754 32201362
    [Google Scholar]
  161. Schnirch L. Nadler-Holly M. Siao S.W. Frese C.K. Viner R. Liu F. Expanding the depth and sensitivity of cross-link identification by differential ion mobility using high-field asymmetric waveform ion mobility spectrometry. Anal. Chem. 2020 92 15 10495 10503 10.1021/acs.analchem.0c01273 32643919
    [Google Scholar]
  162. O’Reilly F.J. Xue L. Graziadei A. Sinn L. Lenz S. Tegunov D. Blötz C. Singh N. Hagen W.J.H. Cramer P. Stülke J. Mahamid J. Rappsilber J. In-cell architecture of an actively transcribing-translating expressome. Science 2020 369 6503 554 557 10.1126/science.abb3758 32732422
    [Google Scholar]
  163. Ryl P.S.J. Bohlke-Schneider M. Lenz S. Fischer L. Budzinski L. Stuiver M. Mendes M.M.L. Sinn L. O’Reilly F.J. Rappsilber J. In situ structural restraints from cross-linking mass spectrometry in human mitochondria. J. Proteome Res. 2020 19 1 327 336 10.1021/acs.jproteome.9b00541 31746214
    [Google Scholar]
  164. Zhao L. Zhao Q. Shan Y. Fang F. Zhang W. Zhao B. Li X. Liang Z. Zhang L. Zhang Y. Smart cutter: An efficient strategy for increasing the coverage of chemical cross-linking analysis. Anal. Chem. 2020 92 1 1097 1105 10.1021/acs.analchem.9b04161 31814401
    [Google Scholar]
  165. Tan D. Li Q. Zhang M.J. Liu C. Ma C. Zhang P. Ding Y.H. Fan S.B. Tao L. Yang B. Li X. Ma S. Liu J. Feng B. Liu X. Wang H.W. He S.M. Gao N. Ye K. Dong M.Q. Lei X. Trifunctional cross-linker for mapping protein-protein interaction networks and comparing protein conformational states. eLife 2016 5 e12509 10.7554/eLife.12509 26952210
    [Google Scholar]
  166. Mendes M.L. Fischer L. Chen Z.A. Barbon M. O’Reilly F.J. Giese S.H. Bohlke-Schneider M. Belsom A. Dau T. Combe C.W. Graham M. Eisele M.R. Baumeister W. Speck C. Rappsilber J. An integrated workflow for crosslinking mass spectrometry. Mol. Syst. Biol. 2019 15 9 e8994 10.15252/msb.20198994 31556486
    [Google Scholar]
  167. Liu J.J. Sharma K. Zangrandi L. Chen C. Humphrey S.J. Chiu Y.T. Spetea M. Liu-Chen L.Y. Schwarzer C. Mann M. In vivo brain GPCR signaling elucidated by phosphoproteomics. Science 2018 360 6395 eaao4927 10.1126/science.aao4927 29930108
    [Google Scholar]
  168. 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. The global phosphorylation landscape of SARS-CoV-2 infection. Cell 2020 182 3 685 10.1016/j.cell.2020.06.034
    [Google Scholar]
  169. Leutert M. Rodríguez-Mias R.A. Fukuda N.K. Villén J. R2-P2 rapid-robotic phosphoproteomics enables multidimensional cell signaling studies. Mol. Syst. Biol. 2019 15 12 e9021 10.15252/msb.20199021 31885202
    [Google Scholar]
  170. Bekker-Jensen D.B. Bernhardt O.M. Hogrebe A. Martinez-Val A. Verbeke L. Gandhi T. Kelstrup C.D. Reiter L. Olsen J.V. Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries. Nat. Commun. 2020 11 1 787 10.1038/s41467‑020‑14609‑1 32034161
    [Google Scholar]
  171. Humphrey S.J. Karayel O. James D.E. Mann M. High-throughput and high-sensitivity phosphoproteomics with the EasyPhos platform. Nat. Protoc. 2018 13 9 1897 1916 10.1038/s41596‑018‑0014‑9 30190555
    [Google Scholar]
  172. Kitata R.B. Choong W.K. Tsai C.F. Lin P.Y. Chen B.S. Chang Y.C. Nesvizhskii A.I. Sung T.Y. Chen Y.J. A data-independent acquisition-based global phosphoproteomics system enables deep profiling. Nat. Commun. 2021 12 1 2539 10.1038/s41467‑021‑22759‑z 33953186
    [Google Scholar]
  173. Rosenberger G. Liu Y. Röst H.L. Ludwig C. Buil A. Bensimon A. Soste M. Spector T.D. Dermitzakis E.T. Collins B.C. Malmström L. Aebersold R. Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS. Nat. Biotechnol. 2017 35 8 781 788 10.1038/nbt.3908 28604659
    [Google Scholar]
  174. Keller A. Purvine S. Nesvizhskii A.I. Stolyar S. Goodlett D.R. Kolker E. Experimental protein mixture for validating tandem mass spectral analysis. OMICS 2002 6 2 207 212 10.1089/153623102760092805 12143966
    [Google Scholar]
  175. Prince J.T. Carlson M.W. Wang R. Lu P. Marcotte E.M. The need for a public proteomics repository. Nat. Biotechnol. 2004 22 4 471 472 10.1038/nbt0404‑471 15085804
    [Google Scholar]
  176. Coyaud E. Mis M. Laurent E.M.N. Dunham W.H. Couzens A.L. Robitaille M. Gingras A.C. Angers S. Raught B. BioID-based identification of skp cullin F-box (SCF)β-TrCP1/2 E3 Ligase substrates. Mol. Cell. Proteomics 2015 14 7 1781 1795 10.1074/mcp.M114.045658 25900982
    [Google Scholar]
  177. Vogel C. de Sousa Abreu R. Ko D. Le S.Y. Shapiro B.A. Burns S.C. Sandhu D. Boutz D.R. Marcotte E.M. Penalva L.O. Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line. Mol. Syst. Biol. 2010 6 1 400 10.1038/msb.2010.59 20739923
    [Google Scholar]
  178. Kirkwood K.J. Ahmad Y. Larance M. Lamond A.I. Characterization of native protein complexes and protein isoform variation using size-fractionation-based quantitative proteomics. Mol. Cell. Proteomics 2013 12 12 3851 3873 10.1074/mcp.M113.032367 24043423
    [Google Scholar]
  179. Hebert A.S. Richards A.L. Bailey D.J. Ulbrich A. Coughlin E.E. Westphall M.S. Coon J.J. The one hour yeast proteome. Mol. Cell. Proteomics 2014 13 1 339 347 10.1074/mcp.M113.034769 24143002
    [Google Scholar]
  180. Muntel J. Xuan Y. Berger S.T. Reiter L. Bachur R. Kentsis A. Steen H. Advancing urinary protein biomarker discovery by data-independent acquisition on a quadrupole-orbitrap mass spectrometer. J. Proteome Res. 2015 14 11 4752 4762 10.1021/acs.jproteome.5b00826 26423119
    [Google Scholar]
  181. Ting Y.S. Egertson J.D. Bollinger J.G. Searle B.C. Payne S.H. Noble W.S. MacCoss M.J. PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data. Nat. Methods 2017 14 9 903 908 10.1038/nmeth.4390 28783153
    [Google Scholar]
  182. Caron E. Espona L. Kowalewski D.J. Schuster H. Ternette N. Alpízar A. Schittenhelm R.B. Ramarathinam S.H. Lindestam Arlehamn C.S. Chiek K.C. Gillet L.C. Rabsteyn A. Navarro P. Kim S. Lam H. Sturm T. Marcilla M. Sette A. Campbell D.S. Deutsch E.W. Moritz R.L. Purcell A.W. Rammensee H.G. Stevanovic S. Aebersold R. An open-source computational and data resource to analyze digital maps of immunopeptidomes. eLife 2015 4 e07661 10.7554/eLife.07661 26154972
    [Google Scholar]
  183. Abelin J.G. Keskin D.B. Sarkizova S. Hartigan C.R. Zhang W. Sidney J. Stevens J. Lane W. Zhang G.L. Eisenhaure T.M. Clauser K.R. Hacohen N. Rooney M.S. Carr S.A. Wu C.J. Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction. Immunity 2017 46 2 315 326 10.1016/j.immuni.2017.02.007 28228285
    [Google Scholar]
  184. Sobolesky P. Parry C. Boxall B. Wells R. Venn-Watson S. Janech M.G. Proteomic analysis of non-depleted serum proteins from bottlenose dolphins uncovers a high vanin-1 phenotype. Sci. Rep. 2016 6 1 33879 10.1038/srep33879 27667588
    [Google Scholar]
  185. Benitez-Amaro A. Pallara C. Nasarre L. Rivas-Urbina A. Benitez S. Vea A. Bornachea O. de Gonzalo-Calvo D. Serra-Mir G. Villegas S. Prades R. Sanchez-Quesada J.L. Chiva C. Sabido E. Tarragó T. Llorente-Cortés V. Molecular basis for the protective effects of low-density lipoprotein receptor-related protein 1 (LRP1)-derived peptides against LDL aggregation. Biochim. Biophys. Acta Biomembr. 2019 1861 7 1302 1316 10.1016/j.bbamem.2019.05.003 31077676
    [Google Scholar]
  186. Sim S.Y. Choi Y.R. Lee J.H. Lim J.M. Lee S.E. Kim K.P. Kim J.Y. Lee S.H. Kim M.S. In-depth proteomic analysis of human bronchoalveolar lavage fluid toward the biomarker discovery for lung cancers. Proteomics Clin. Appl. 2019 13 5 1900028 10.1002/prca.201900028 31119868
    [Google Scholar]
  187. Haythorne E. Rohm M. van de Bunt M. Brereton M.F. Tarasov A.I. Blacker T.S. Sachse G. Silva dos Santos M. Terron Exposito R. Davis S. Baba O. Fischer R. Duchen M.R. Rorsman P. MacRae J.I. Ashcroft F.M. Diabetes causes marked inhibition of mitochondrial metabolism in pancreatic β-cells. Nat. Commun. 2019 10 1 2474 10.1038/s41467‑019‑10189‑x 31171772
    [Google Scholar]
  188. Zhu Y. Dou M. Piehowski P.D. Liang Y. Wang F. Chu R.K. Chrisler W.B. Smith J.N. Schwarz K.C. Shen Y. Shukla A.K. Moore R.J. Smith R.D. Qian W.J. Kelly R.T. Spatially resolved proteome mapping of laser capture microdissected tissue with automated sample transfer to nanodroplets. Mol. Cell. Proteomics 2018 17 9 1864 1874 10.1074/mcp.TIR118.000686 29941660
    [Google Scholar]
  189. Shears M.J. Sekhar N.R. Swearingen K.E. Renuse S. Mishra S. Jaipal R.P. Moritz R.L. Pandey A. Sinnis P. Proteomic analysis of Plasmodium merosomes: The link between liver and blood stages in malaria. J. Proteome Res. 2019 18 9 3404 3418 10.1021/acs.jproteome.9b00324 31335145
    [Google Scholar]
  190. Meier F. Geyer P.E. Virreira W.S. Cox J. Mann M. BoxCar acquisition method enables single-shot proteomics at a depth of 10,000 proteins in 100 minutes. Nat. Methods 2018 15 6 440 448 10.1038/s41592‑018‑0003‑5 29735998
    [Google Scholar]
  191. Fíla J. Klodová B. Potěšil D. Juříček M. Šesták P. Zdráhal Z. Honys D. The beta subunit of nascent polypeptide associated complex plays a role in flowers and Siliques development of Arabidopsis Thaliana. Int. J. Mol. Sci. 2020 21 6 2065 10.3390/ijms21062065 32192231
    [Google Scholar]
  192. Tharyan R.G. Annibal A. Schiffer I. Laboy R. Atanassov I. Weber A.L. Gerisch B. Antebi A. NFYB-1 regulates mitochondrial function and longevity via lysosomal prosaposin. Nat. Metab. 2020 2 5 387 396 10.1038/s42255‑020‑0200‑2 32694663
    [Google Scholar]
  193. Yu Y. O’Rourke A. Lin Y.H. Singh H. Eguez R.V. Beyhan S. Nelson K.E. Predictive signatures of 19 antibiotic-induced Escherichia coli proteomes. ACS Infect. Dis. 2020 6 8 2120 2129 10.1021/acsinfecdis.0c00196 32673475
    [Google Scholar]
  194. Patnode M. L. Beller Z. W. Han N. D. Cheng J. Peters S. L. Terrapon N. Henrissat B. Le Gall S. Saulnier L. Hayashi D. K. Interspecies competition impacts targeted manipulation of human gut bacteria by fiber-derived glycans. Cell 2019 179 1 59 73 10.1016/j.cell.2019.08.011
    [Google Scholar]
  195. Schwede T. Sali A. Honig B. Levitt M. Berman H.M. Jones D. Brenner S.E. Burley S.K. Das R. Dokholyan N.V. Dunbrack R.L. Jr Fidelis K. Fiser A. Godzik A. Huang Y.J. Humblet C. Jacobson M.P. Joachimiak A. Krystek S.R. Jr Kortemme T. Kryshtafovych A. Montelione G.T. Moult J. Murray D. Sanchez R. Sosnick T.R. Standley D.M. Stouch T. Vajda S. Vasquez M. Westbrook J.D. Wilson I.A. Outcome of a workshop on applications of protein models in biomedical research. Structure 2009 17 2 151 159 10.1016/j.str.2008.12.014 19217386
    [Google Scholar]
  196. Vreven T. Moal I.H. Vangone A. Pierce B.G. Kastritis P.L. Torchala M. Chaleil R. Jiménez-García B. Bates P.A. Fernandez-Recio J. Bonvin A.M.J.J. Weng Z. Updates to the integrated protein–protein interaction benchmarks: Docking benchmark version 5 and affinity benchmark version 2. J. Mol. Biol. 2015 427 19 3031 3041 10.1016/j.jmb.2015.07.016 26231283
    [Google Scholar]
  197. Dominguez C. Boelens R. Bonvin A.M.J.J. HADDOCK: A protein-protein docking approach based on biochemical or biophysical information. J. Am. Chem. Soc. 2003 125 7 1731 1737 10.1021/ja026939x 12580598
    [Google Scholar]
  198. Liu S. Zhang J. Chu H. Wang M. Xue B. Ni N. Yu J. Xie Y. Chen Z. Chen M. PSP: Million-level protein sequence dataset for protein structure prediction. arXiv 2022
    [Google Scholar]
  199. Wang L. Xie L. Zhang Z. Determination of HER2 binding domain in antigen-antibody complexes based on chemical crosslinking mass spectrometry. J. Proteomics 2023 286 104954 10.1016/j.jprot.2023.104954 37390893
    [Google Scholar]
  200. Di Ianni A. Di Ianni A. Cowan K. Barbero L.M. Sirtori F.R. Leveraging cross-linking mass spectrometry for modeling antibody–antigen complexes. J. Proteome Res. 2024 23 3 1049 1061 10.1021/acs.jproteome.3c00816 38372774
    [Google Scholar]
  201. Wang X. Cimermancic P. Yu C. Schweitzer A. Chopra N. Engel J.L. Greenberg C. Huszagh A.S. Beck F. Sakata E. Yang Y. Novitsky E.J. Leitner A. Nanni P. Kahraman A. Guo X. Dixon J.E. Rychnovsky S.D. Aebersold R. Baumeister W. Sali A. Huang L. Molecular details underlying dynamic structures and regulation of the human 26S proteasome. Mol. Cell. Proteomics 2017 16 5 840 854 10.1074/mcp.M116.065326 28292943
    [Google Scholar]
  202. Kalisman N. Adams C.M. Levitt M. Subunit order of eukaryotic TRiC/CCT chaperonin by cross-linking, mass spectrometry, and combinatorial homology modeling. Proc. Natl. Acad. Sci. USA 2012 109 8 2884 2889 10.1073/pnas.1119472109 22308438
    [Google Scholar]
  203. Dimitsaki S. Gavriilidis G.I. Dimitriadis V.K. Natsiavas P. Benchmarking of machine learning classifiers on plasma proteomic for COVID-19 severity prediction through interpretable artificial intelligence. Artif. Intell. Med. 2023 137 102490 10.1016/j.artmed.2023.102490 36868685
    [Google Scholar]
  204. Abramson, J.; Adler, J.; Dunger, J.; Evans, R.; Green, T.; Pritzel, A.; Ronneberger, O.; Willmore, L.; Ballard, A. J.; Bambrick, J. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature, 2024, 630(8016), 493-500.
/content/journals/cpps/10.2174/0113892037368295250520102121
Loading
Applications and Prospects of Artificial Intelligence in Proteomics Via Mass Spectrometry: A Review
Bentham Science Publishers ; https://doi.org/10.2174/0113892037368295250520102121
/content/journals/cpps/10.2174/0113892037368295250520102121
/content/journals/cpps/10.2174/0113892037368295250520102121
Loading

Data & Media loading...


  • Article Type:     Review Article
Keywords: algorithms ; peptide sequence ; Deep learning ; protein spectra ; datasets ; retention time ; protein structure prediction

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test