Inferring protein-protein interaction networks from mass spectrometry-based proteomic approaches: a mini-review
Open accessAbstract
Studying protein-protein interaction networks provide key evidence for the underlying molecular mechanisms. Mass spectrometry-based proteomic approaches have been playing a pivotal role in deciphering these interaction networks, along with precise quantification for individual interactions. In this mini-review we discuss the available techniques and methods for qualitative and quantitative elucidation of protein-protein interaction networks. We then summarize the down-stream computational strategies for identification and quantification of interactions from those techniques. Finally, we highlight the challenges and limitations of current computational pipelines in eliminating false positive interactors, followed by a summary of the innovative algorithms to address these issues, along with the scope for future improvements.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Limitations of the AP-MS experiment include the potential loss of transient or weakly bound complexes, the inherent noise associated with AP-MS relative to global proteomics analysis, and the short time point of the experiment, which precludes detection of degradation events with slow kinetics.
“Limitations of this experiment include the potential loss of transient or weakly bound complexes in the enrichment and subsequent wash steps, the inherent noise associated with AP-MS relative to global proteomics analysis (Dunham et al., 2012; Yugandhar et al., 2019) and the short time point of the experiment, which precludes detection of degradation events with slow kinetics.”
From Mapping the Degradable Kinome Provides a Resource for Expedited Degrader Development (2020) · cites this paper as Yugandhar et al., 2019
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Mass spectrometry-based proteomics
Aebersold, R., & Mann, M. (2003) · Nature · doi:10.1038/nature01511 · Cited by 6,955 · see citing works
A novel genetic system to detect protein–protein interactions
Fields, S., & Song, O.-K. (1989) · Nature · doi:10.1038/340245a0 · Cited by 6,152 · see citing works
Ong, S.-E., Blagoev, B., Kratchmarova, I., Kristensen, D. B., Steen, H., Pandey, A., & Mann, M. (2002) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m200025-mcp200 · Cited by 5,574 · cited 13× · see citing works
“This has been addressed by the onset of Spike-in SILAC (which uses a stable isotope labelled cell line as a standard for comparison to in vitro post-isolation labelling of the cell line) [58] and Super-SILAC (which is run on a mixture of different cell lines and has the advantage over standard SILAC in not being limit…”
MethodInfluentialCox, J., Hein, M. Y., Luber, C. A., Paron, I., Nagaraj, N., & Mann, M. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.031591 · Cited by 5,180 · cited 3× · see citing works
“MaxQuant [60] is one of the most widely used tools for processing large-scale SILAC datasets [61], along with Census [51,62], pQuant [63], TPP [64], IsoQuant [65] etc. MaxQuant utilizes Andromeda search engine that incorporates length, charge and number of modi fi cations of the peptides for quality peptide spectrum ma…”
MethodA statistical model for identifying proteins by tandem mass spectrometry.
Nesvizhskii, A., Keller, A., Kolker, E., & Aebersold, R. (2003) · Analytical Chemistry · doi:10.1021/ac0341261 · Cited by 4,902 · cited 4× · see citing works
“Algorithms such as ProteinProphet [105] for example, deals with degenerate peptides by retaining only the spectrum associated with the highest PSM scores and uses the remainingspectra to calculate scores for peptides as anap-proximation of their probabilities [106].”
BackgroundMethodInfluentialThe MaxQuant computational platform for mass spectrometry-based shotgun proteomics
Tyanova, S., Temu, T., & Cox, J. (2016) · Nature Protocols · doi:10.1038/nprot.2016.136 · Cited by 4,691 · cited 1× · see citing works
“…tools for processing large-scale SILAC datasets [61], along with Census [51,62], pQuant [63], TPP [64], IsoQuant [65] etc. MaxQuant utilizes Andromeda search engine that incorporates length, charge and number of modi fi cations of the peptides for quality peptide spectrum match (PSM) scoring [66].”
MethodGillet, L. C., Navarro, P., Tate, S., Röst, H., Selevsek, N., Reiter, L., Bonner, R., & Aebersold, R. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o111.016717 · Cited by 2,877 · see citing works
Proteome survey reveals modularity of the yeast cell machinery
Gavin, A., Aloy, P., Grandi, P., Krause, R., Böesche, M., Marzioch, M., Rau, C., Jensen, L. J., Bastuck, S., Dümpelfeld, B., et al. (2006) · Nature · doi:10.1038/nature04532 · Cited by 2,544 · cited 1× · see citing works
“[21] developed a ‘socio-affinity’ score to cluster the identified proteins into potential functional complexes and compared themwith amanually curated set of known interactions to infer accuracy and coverage of the identified interactors.”
BackgroundRoux, K. J., Kim, D. I., Raida, M., & Burke, B. (2012) · Journal of Cell Biology · doi:10.1083/jcb.201112098 · Cited by 2,416 · cited 2× · see citing works
“Both BioID and APEX relies on a reactive biotin derivative that diffuses from an enzyme's active site to label proteins which are in spatial proximity [29].”
BackgroundThompson, A., Schäfer, J., Kuhn, K., Kienle, S., Schwarz, J., Schmidt, G., Neumann, T., & Hamon, C. (2003) · Analytical Chemistry · doi:10.1021/ac0262560 · Cited by 2,386 · cited 11× · see citing works
“TMT belongs to a class of isobaric tags that allow for more accurate multiplexed quanti fi cation of IPs using MS 2 spectra [14,73,74].”
BackgroundMethodInfluentialMass-spectrometric exploration of proteome structure and function
Aebersold, R., & Mann, M. (2016) · Nature · doi:10.1038/nature19949 · Cited by 2,334 · cited 1× · see citing works
“…[2,3], protein-fragment complementation assay (PCA) [4], LUMIER [5], fl uorescence resonance energy transfer (FRET) [6] etc. Mass spectrometry is a powerful tool for studying biomolecules such as proteins by their identi fi cation, quanti fi cation and further functional characterization [7 – 9].”
MethodThe CRAPome: a contaminant repository for affinity purification–mass spectrometry data
Mellacheruvu, D., Wright, Z., Couzens, A. L., Lambert, J.-P., St-Denis, N. A., Li, T., Miteva, Y. V., Hauri, S., Sardiu, M. E., Low, T. Y., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2557 · Cited by 1,686 · cited 4× · see citing works
“Few years ago, Mellacheruvu et al. [103] published a comprehensive list of background proteins, called CRAPome, consistingartifacts of humanor biological errors, aggregated from multiple AP-MS experiments.”
BackgroundInfluentialChien, C., Bartel, P. L., Sternglanz, R., & Fields, S. (1991) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.88.21.9578 · Cited by 1,494 · cited 1× · see citing works
“Some of the most widely used techniques to identify protein-protein interactions include yeast two-hybrid (Y2H) [2,3], protein-fragment complementation assay (PCA) [4], LUMIER [5], fl uorescence resonance energy transfer (FRET) [6] etc. Mass spectrometry is a powerful tool for studying biomolecules…”
MethodHigh-Quality Binary Protein Interaction Map of the Yeast Interactome Network
Yu, H., Braun, P., Yıldırım, M. A., Lemmens, I., Venkatesan, K., Sahalie, J., Hirozane-Kishikawa, T., Gebreab, F., Li, N., Simonis, N., et al. (2008) · Science · doi:10.1126/science.1158684 · Cited by 1,392 · see citing works
Self-citationProteomic Mapping of Mitochondria in Living Cells via Spatially Restricted Enzymatic Tagging
Martell, J. D., Ting, A. Y., Rhee, H., Zou, P., Udeshi, N. D., Mootha, V. K., & Carr, S. A. (2012) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 1,330 · see citing works
Proteomic characterization of the human centrosome by protein correlation profiling
Andersen, J. S., Wilkinson, C. J., Mayor, T., Mortensen, P., Nigg, E. A., & Mann, M. (2003) · Nature · doi:10.1038/nature02166 · Cited by 1,319 · cited 7× · see citing works
“The key component in maintaining high speci fi city in a MS-PCP based approach is its integrated computational analysis pipeline employed for hierarchical clustering and the expression neighborhood analysis [90].”
MethodInfluentialMS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics
Ting, L., Rad, R., Gygi, S. P., & Haas, W. (2011) · Nature Methods · doi:10.1038/nmeth.1714 · Cited by 1,009 · cited 1× · see citing works
“This problem could be addressed by performing an additional step of isolation and fragmentation (MS3 approaches) [82].”
BackgroundA Census of Human Soluble Protein Complexes
Havugimana, P. C., Hart, T., Nepusz, T., Yang, H., Turinsky, A. L., Li, Z., Wang, P. I., Boutz, D. R., Fong, V., Phanse, S., et al. (2012) · Cell · doi:10.1016/j.cell.2012.08.011 · Cited by 843 · cited 1× · see citing works
“Since then, PCP has been demonstrated to be an efficient tool in elucidation and characterization of protein complexes at both organelle [90] and cellular level [91], facilitating comparisons across multiple species [92].”
MethodProtein labeling by iTRAQ: A new tool for quantitative mass spectrometry in proteome research
Wiese, S., Reidegeld, K. A., Meyer, H. E., & Warscheid, B. (2006) · PROTEOMICS · doi:10.1002/pmic.200600422 · Cited by 803 · cited 1× · see citing works
“Some of the most widely used quanti fi cation methods employ stable isotope labeling of amino acids in cell culture (SILAC) [12], isobarictagsforrelativeandabsolutequantitation(iTRAQ)[13],tandem mass tag (TMT) [14] and protein correlation pro fi ling (PCP) [15].”
MethodToward a Comprehensive Atlas of the Physical Interactome of Saccharomyces cerevisiae
Collins, S. R., Kemmeren, P., Zhao, X.-C., Greenblatt, J. F., Spencer, F., Holstege, F. C. P., Weissman, J. S., & Krogan, N. J. (2007) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m600381-mcp200 · Cited by 795 · cited 1× · see citing works
“Motivated by the framework of socio-af fi nity score, Collins et al. [22] designed ‘ puri fi cation enrichment score ’ by incorporating features such as the negative evidence against interactions where one of the two proteins fail to be identi fi ed as a prey when another is used as a bait, and the…”
MethodHigh-Throughput Mapping of a Dynamic Signaling Network in Mammalian Cells
Barrios‐Rodiles, M., Brown, K. R., Ozdamar, B., Bose, R., Liu, Z., Donovan, R. S., Shinjo, F., Liu, Y., Dembowy, J., Taylor, I. W., et al. (2005) · Science · doi:10.1126/science.1105776 · Cited by 726 · cited 2× · see citing works
“Some of the most widely used techniques to identify protein-protein interactions include yeast two-hybrid (Y2H) [2,3], protein-fragment complementation assay (PCA) [4], LUMIER [5], fluorescence resonance energy transfer (FRET) [6] etc.”
MethodAnalysis of protein complexes using mass spectrometry
Gingras, A., Gstaiger, M., Raught, B., & Aebersold, R. H. (2007) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2208 · Cited by 709 · cited 4× · see citing works
“AP-MS facilitates the isolation of protein complexes from cell lysates, which enables studying them at near physiological conditions [10].”
BackgroundMethodInfluentialTemporal analysis of phosphotyrosine-dependent signaling networks by quantitative proteomics
Blagoev, B., Ong, S., Kratchmarova, I., & Mann, M. (2004) · Nature Biotechnology · doi:10.1038/nbt1005 · Cited by 684 · see citing works
Sinz, A. (2006) · Mass Spectrometry Reviews · doi:10.1002/mas.20082 · Cited by 648 · cited 1× · see citing works
“Additionally, XL-MS has been demonstrated tobeanef fi cientmethodtocapturethedistanceconstraints,therebyre-vealingcrucialinformationthat enables theidenti fi cationoftheinterac-tion partners and dynamics of protein-protein interactions [43,44].”
Current Protocols in Bioinformatics
(2018) · Current Protocols in Bioinformatics · doi:10.1002/(issn)1934-340x · Cited by 641 · see citing works
Isobaric Labeling-Based Relative Quantification in Shotgun Proteomics
Rauniyar, N., & Yates, J. R. (2014) · Journal of Proteome Research · doi:10.1021/pr500880b · Cited by 597 · see citing works
A Mammalian Organelle Map by Protein Correlation Profiling
Foster, L. J., de Hoog, C. L., Zhang, Y., Zhang, Y., Xie, X. S., Mootha, V. K., & Mann, M. (2006) · Cell · doi:10.1016/j.cell.2006.03.022 · Cited by 573 · cited 2× · see citing works
“The key component in maintaining high specificity in a MS-PCP based approach is its integrated computational analysis pipeline employed for hierarchical clustering and the expression neighborhood analysis [90].”
MethodScaffold: A bioinformatic tool for validating MS/MS‐based proteomic studies
Searle, B. C. (2010) · PROTEOMICS · doi:10.1002/pmic.200900437 · Cited by 561 · cited 2× · see citing works
“Another tool called Scaffold [107] address this issue using a “ greedy ” approach, wherein protein scores are fi rst calculated using peptides that do not fall under the category of degenerate peptides.”
BackgroundMethod[13] Fluorescence resonance energy transfer
Selvin, P. R. (1995) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/0076-6879(95)46015-2 · Cited by 521 · see citing works
Panorama of ancient metazoan macromolecular complexes
Wan, C., Borgeson, B., Phanse, S., Tu, F., Drew, K., Clark, G. W., Xiong, X., Kagan, O., Kwan, J., Bezginov, A., et al. (2015) · Nature · doi:10.1038/nature14877 · Cited by 521 · cited 2× · see citing works
“Since then, PCP has been demonstrated to be an ef fi - cient tool in elucidation and characterization of protein complexes at both organelle [90] and cellular level [91], facilitating comparisons across multiple species [92].”
BackgroundAddressing Accuracy and Precision Issues in iTRAQ Quantitation
Karp, N. A., Huber, W. K., Sadowski, P. G., Charles, P. D., Hester, S. V., & Lilley, K. S. (2010) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m900628-mcp200 · Cited by 497 · cited 1× · see citing works
“[88] have also been used for handling the problem of underestimation of protein fold change and high variation in protein abundance in samples.”
MethodProteome-wide profiling of protein assemblies by cross-linking mass spectrometry
Liu, F., Rijkers, D. T. S., Post, H., & Heck, A. J. R. (2015) · Nature Methods · doi:10.1038/nmeth.3603 · Cited by 470 · see citing works
Rappsilber, J. (2010) · Journal of Structural Biology · doi:10.1016/j.jsb.2010.10.014 · Cited by 414 · see citing works
Multidimensional proteomics for cell biology
Larance, M., & Lamond, A. I. (2015) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3970 · Cited by 407 · cited 1× · see citing works
“MS-PCP follows a co-fractionation based clustering approach to predict components in a specific protein complex, assuming protein complexes co-purify when separated based on their physicochemical properties such as density, hydrophobicity and size [89].”
MethodKao, A., Chiu, C., Vellucci, D., Yang, Y., Patel, V. R., Guan, S., Randall, A. Z., Baldi, P., Rychnovsky, S. D., & Huang, L. (2010) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m110.002212 · Cited by 405 · cited 2× · see citing works
“Later, the inception of MS-cleavable cross-linkers such as disuccinimidyl sulfoxide (DSSO) [39], disuccinimidyl dibutyric urea (DSBU) [40,41] and 1,1 ′ - carbonyldiimidazole (CDI) [42], which generate a fragment ion signature that provides additional information for database search and reduce the number of iterations…”
BackgroundA quantitative analysis software tool for mass spectrometry–based proteomics
Park, S. K., Venable, J. D., Xu, T., & Yates, J. R. (2008) · Nature Methods · doi:10.1038/nmeth.1195 · Cited by 401 · cited 2× · see citing works
“…spectral counts or integrated peptide ion intensities have been successfully utilized to infer quantitative information about the interactions identi fi ed through AP-MS experiments known as label-free quanti fi cation, using robust tools such as SAINT [49], MaxQuant [50], Census [51] etc.”
MethodProteomic Parsimony through Bipartite Graph Analysis Improves Accuracy and Transparency
Zhang, B., Chambers, M., & Tabb, D. L. (2007) · Journal of Proteome Research · doi:10.1021/pr070230d · Cited by 374 · cited 2× · see citing works
“…from remaining spectra Scaffold [107] “ Greedy ” approach “ One-hit ” wonders ProteinProphet [105] Employs a probability-based model IDPicker [109] Performs better than both “ two-peptide ” and “ one peptide ” rule HIquant [111] Bottom-up approach; requires corresponding ion ratios MRM [115]…”
MethodResultAffinity-purification coupled to mass spectrometry: basic principles and strategies
Dunham, W. H., Mullin, M., & Gingras, A. (2012) · PROTEOMICS · doi:10.1002/pmic.201100523 · Cited by 368 · cited 1× · see citing works
“AP-MS facilitates the identification of interactors for a protein of interest through affinity-based approaches [10,20].”
Multiple reaction monitoring for quantitative biomarker analysis in proteomics and metabolomics☆
Kitteringham, N. R., Jenkins, R. E., Lane, C. S., Elliott, V. L., & Park, B. K. (2008) · Journal of Chromatography B · doi:10.1016/j.jchromb.2008.11.013 · Cited by 343 · cited 1× · see citing works
“…[109] Performs better than both “ two-peptide ” and “ one peptide ” rule HIquant [111] Bottom-up approach; requires corresponding ion ratios MRM [115] Requires prior knowledge of all protein stoichiometries/isoforms SWATH MS [116] Saturation of spectral peaks SignalFinder Targeted MS DeconTools…”
BackgroundO'Reilly, F. J., & Rappsilber, J. (2018) · Nature Structural & Molecular Biology · doi:10.1038/s41594-018-0147-0 · Cited by 334 · cited 1× · see citing works
“Additionally, XL-MS has been demonstrated tobeanef fi cientmethodtocapturethedistanceconstraints,therebyre-vealingcrucialinformationthat enables theidenti fi cationoftheinterac-tion partners and dynamics of protein-protein interactions [43,44].”
False discovery rate estimation for cross-linked peptides identified by mass spectrometry
Walzthoeni, T., Claassen, M., Leitner, A., Herzog, F., Bohn, S., Förster, F. G., Beck, M., & Aebersold, R. H. (2012) · Nature Methods · doi:10.1038/nmeth.2103 · Cited by 301 · see citing works
An insight into iTRAQ: where do we stand now?
Evans, C. A., Noirel, J., Ow, S. Y., Salim, M., Pereira‐Medrano, A. G., Couto, N. A. S., Pandhal, J., Smith, D. L., Pham, T. K., Karunakaran, E., et al. (2012) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-012-5918-6 · Cited by 298 · cited 4× · see citing works
“Another potential issue is the variation in protein abundance, which makes it difficult to detect proteins with low abundance [80].”
BackgroundInfluentialA Novel Proteomic Screen for Peptide-Protein Interactions
Schulze, W. X., & Mann, M. (2004) · Journal of Biological Chemistry · doi:10.1074/jbc.m309909200 · Cited by 298 · cited 1× · see citing works
“This has been addressed by approaches such as dataset normalization [68] and label-swap replication [69,70].”
MethodLiu, F., Lössl, P., Scheltema, R. A., Viner, R., & Heck, A. J. R. (2017) · Nature Communications · doi:10.1038/ncomms15473 · Cited by 285 · see citing works
Protein interaction screening by quantitative immunoprecipitation combined with knockdown (QUICK)
Selbach, M., & Mann, M. (2006) · Nature Methods · doi:10.1038/nmeth972 · Cited by 240 · cited 1× · see citing works
“Multiple experimental strategies have been developed to distinguish speci fi c and non-speci fi c interactions to fi lter-out the background contaminants [100 – 102].”
MethodQuantitative proteomics using SILAC: Principles, applications, and developments
Chen, X., Wei, S., Ji, Y., Guo, X., & Yang, F. (2015) · PROTEOMICS · doi:10.1002/pmic.201500108 · Cited by 239 · cited 2× · see citing works
“A significant limitation of SILAC methodology has been incomplete incorporation of the isotopes [67].”
BackgroundRobust and Sensitive iTRAQ Quantification on an LTQ Orbitrap Mass Spectrometer
Bantscheff, M., Böesche, M., Eberhard, D., Mathieson, T., Sweetman, G. M. A., & Küster, B. (2008) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m800029-mcp200 · Cited by 235 · see citing works
Müller, M. Q., Dreiocker, F., Ihling, C. H., Schäfer, M., & Sinz, A. (2010) · Analytical Chemistry · doi:10.1021/ac101241t · Cited by 231 · cited 2× · see citing works
“Later, the inception of MS-cleavable cross-linkers such as disuccinimidyl sulfoxide (DSSO) [39], disuccinimidyl dibutyric urea (DSBU) [40,41] and 1,1 ′ - carbonyldiimidazole (CDI) [42], which generate a fragment ion signature that provides additional information for database search and reduce the number of iterations…”
BackgroundThe challenge of the proteome dynamic range and its implications for in‐depth proteomics
Zubarev, R. A. (2013) · PROTEOMICS · doi:10.1002/pmic.201200451 · Cited by 230 · see citing works
Jaitly, N., Mayampurath, A., Littlefield, K., Adkins, J. N., Anderson, G. A., & Smith, R. D. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-87 · Cited by 217 · see citing works
Morris, J. H., Knudsen, G. M., Verschueren, E., Johnson, J. R., Cimermančič, P., Greninger, A. L., & Pico, A. R. (2014) · Nature Protocols · doi:10.1038/nprot.2014.164 · Cited by 200 · cited 5× · see citing works
“This might include non-specific binding of proteins to beads, resins or might result from biological factors such as protein misfolding [94].”
BackgroundInfluentialSinz, A. (2018) · Angewandte Chemie International Edition · doi:10.1002/anie.201709559 · Cited by 192 · cited 1× · see citing works
“Cross-linking mass spectrometry (XL-MS) is a more advanced technique to elucidate protein interaction networks at the level of both single complex and proteome-wide studies (extensively reviewed by Sinz [11]).”
BackgroundFalse Discovery Rates of Protein Identifications: A Strike against the Two-Peptide Rule
Gupta, N., & Pevzner, P. A. (2009) · Journal of Proteome Research · doi:10.1021/pr9004794 · Cited by 188 · cited 1× · see citing works
“A general approach that has been adapted to deal with such cases is the ‘two peptide’ rule, where only proteins with two or more number of identified peptides are retained [108].”
BackgroundMatching Cross-linked Peptide Spectra: Only as Good as the Worse Identification
Trnka, M. J., Baker, P. R., Robinson, P. J. J., Burlingame, A. L., & Chalkley, R. J. (2013) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.034009 · Cited by 176 · see citing works
Pichler, P., Köcher, T., Holzmann, J., Mazanek, M., Taus, T., Ammerer, G., & Mechtler, K. (2010) · Analytical Chemistry · doi:10.1021/ac100890k · Cited by 175 · cited 1× · see citing works
“However, some of the previous studies reported that higher order multiplexing such as iTRAQ 8-plex and TMT 6-plex yielded consistently lower number of protein and peptide identifications when compared to that of lower order multiplexing (iTRAQ 4-plex) [78,79].”
ResultRecent advances in proximity-based labeling methods for interactome mapping
Trinkle‐Mulcahy, L. (2019) · F1000Research · doi:10.12688/f1000research.16903.1 · Cited by 166 · see citing works
Analyzing Protein‐Protein Interactions from Affinity Purification‐Mass Spectrometry Data with SAINT
Choi, H., Liu, G., Mellacheruvu, D., Tyers, M., Gingras, A., & Nesvizhskii, A. I. (2012) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi0815s39 · Cited by 164 · see citing works
From pull-down data to protein interaction networks and complexes with biological relevance
Zhang, B., Park, B.-H., Karpinets, T., & Samatova, N. F. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn036 · Cited by 153 · see citing works
Quirks of Error Estimation in Cross-Linking/Mass Spectrometry
Fischer, L., & Rappsilber, J. (2017) · Analytical Chemistry · doi:10.1021/acs.analchem.6b03745 · Cited by 149 · see citing works
[14] Detection of protein-protein interactions by protein fragment complementation strategies
Michnick, S. W., Remy, I., Campbell‐Valois, F., Vallée‐Bélisle, A., & Pelletier, J. N. (2000) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(00)28399-7 · Cited by 139 · see citing works
Quantitative cross-linking/mass spectrometry using isotope-labelled cross-linkers
Fischer, L., Chen, Z. A., & Rappsilber, J. (2013) · Journal of Proteomics · doi:10.1016/j.jprot.2013.03.005 · Cited by 139 · see citing works
Ow, S. Y., Salim, M., Noirel, J., Evans, C. A., & Wright, P. C. (2011) · PROTEOMICS · doi:10.1002/pmic.201000752 · Cited by 132 · cited 1× · see citing works
“Some of the key issues such as interference, where contaminants that elute with the target ion, can potentially skew the reporter ion intensities and contribute to underestimation of protein fold change estimations [71,80], in addition to the problem of ratio compression due to co-eluting peptides [81].”
BackgroundDetermination of carryover and contamination for mass spectrometry-based chromatographic assays
Hughes, N. C., Wong, E. Y. K., Fan, J., & Bajaj, N. (2007) · The AAPS Journal · doi:10.1208/aapsj0903042 · Cited by 132 · cited 3× · see citing works
“The obvious experimental strategies to address such contamination is to perform rigorous washing and sanitization of machines after each experiment [95], apart from regularly monitoring the liquid chromatography system [96].”
BackgroundMethodI-DIRT, A General Method for Distinguishing between Specific and Nonspecific Protein Interactions
Tackett, A. J., DeGrasse, J. A., Sekedat, M. D., Oeffinger, M., Rout, M. P., & Chait, B. T. (2005) · Journal of Proteome Research · doi:10.1021/pr050225e · Cited by 131 · see citing works
Weisbrod, C. R., Chavez, J. D., Eng, J. K., Yang, L., Zheng, C., & Bruce, J. E. (2013) · Journal of Proteome Research · doi:10.1021/pr3011638 · Cited by 131 · see citing works
Chemical cross‐linking and native mass spectrometry: A fruitful combination for structural biology
Sinz, A., Arlt, C., Chorev, D. S., & Sharon, M. (2015) · Protein Science · doi:10.1002/pro.2696 · Cited by 130 · see citing works
General Statistical Modeling of Data from Protein Relative Expression Isobaric Tags
Breitwieser, F. P., Müller, A. C., Dayon, L., Köcher, T., Hainard, A., Pichler, P., Schmidt-Erfurth, U. M., Superti‐Furga, G., Sanchez, J., Mechtler, K., et al. (2011) · Journal of Proteome Research · doi:10.1021/pr1012784 · Cited by 127 · cited 1× · see citing works
“Data modeling with ‘ boosted median ’ by Breitwieser et al. [87] and simple linear correction proposed by Karp et al. [88]have alsobeen used for handlingtheproblem of underestimation of protein fold change and high variation in protein abundance in samples.”
MethodQuality control in mass spectrometry‐based proteomics
Bittremieux, W., Tabb, D. L., Impens, F., Staes, A., Timmerman, E., Martens, L., & Laukens, K. (2017) · Mass Spectrometry Reviews · doi:10.1002/mas.21544 · Cited by 127 · cited 2× · see citing works
“The obvious experimental strategies to address such contamination is to perform rigorous washing and sanitization of machines after each experiment [95], apart from regularly monitoring the liquid chromatography system [96].”
BackgroundMethodIsobaric protein and peptide quantification: perspectives and issues
Treumann, A., & Thiede, B. (2010) · Expert Review of Proteomics · doi:10.1586/epr.10.29 · Cited by 116 · cited 3× · see citing works
“A potential disadvantage with amino-group isotopic-labelling strategies is the increased sample complexity due to the double peaks in MS spectra [71,72].”
BackgroundMethodHuang, T. C., Wang, J., Yu, W., & He, Z. (2012) · Briefings in Bioinformatics · doi:10.1093/bib/bbs004 · Cited by 112 · cited 2× · see citing works
“Another challenge for computational pipelines in distinguishing true interactors from false positives is the case where multiple potential interactor proteins have only one peptide (unique or shared) identified from the PSM search [104].”
BackgroundPrecise protein quantification based on peptide quantification using iTRAQ™
Boehm, A., Pütz, S. M., Altenhöfer, D., Sickmann, A., & Falk, M. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-214 · Cited by 110 · see citing works
Comparing SILAC- and Stable Isotope Dimethyl-Labeling Approaches for Quantitative Proteomics
Lau, H.-T., Suh, H. W., Golkowski, M. G., & Ong, S. (2014) · Journal of Proteome Research · doi:10.1021/pr500630a · Cited by 110 · cited 1× · see citing works
“TMT belongs to a class of isobaric tags that allow for more accurate multiplexed quantification of IPs using MS(2) spectra [14,73,74].”
Choe, L. H., Aggarwal, K., Franck, Z., & Lee, K. H. (2005) · Electrophoresis · doi:10.1002/elps.200410336 · Cited by 106 · see citing works
Multi-Q: A Fully Automated Tool for Multiplexed Protein Quantitation
Lin, W.-T., Hung, W.-N., Yian, Y.-H., Wu, K., Han, C., Chen, Y., Chen, Y., Sung, T.-Y., & Hsu, W.-L. (2006) · Journal of Proteome Research · doi:10.1021/pr060132c · Cited by 101 · see citing works
Review of software tools for design and analysis of large scale MRM proteomic datasets
Colangelo, C. M., Chung, L. M., Bruce, C., & Cheung, K. (2013) · Methods · doi:10.1016/j.ymeth.2013.05.004 · Cited by 97 · cited 1× · see citing works
“On the other hand, tools used for non-targeted MS runs generally involve usage of adjacent ion peaks that are not saturated to correct the saturated peaks [113,114].”
BackgroundOvercoming the dynamic range problem in mass spectrometry-based shotgun proteomics
Wu, L., & Han, D. K. M. (2006) · Expert Review of Proteomics · doi:10.1586/14789450.3.6.611 · Cited by 97 · see citing works
Mass spectrometry‐based immuno‐precipitation proteomics – The user's guide
ten Have, S., Boulon, S., Ahmad, Y., & Lamond, A. I. (2011) · PROTEOMICS · doi:10.1002/pmic.201000548 · Cited by 93 · see citing works
Liu, C., Song, C., Yuan, Z., Fu, Y., Chi, H., Wang, L., Fan, S., Zhang, K., Zeng, W., He, S., et al. (2014) · Analytical Chemistry · doi:10.1021/ac404246w · Cited by 85 · cited 1× · see citing works
“MaxQuant [60] is one of the most widely used tools for processing large-scale SILAC datasets [61], along with Census [51,62], pQuant [63], TPP [64], IsoQuant [65] etc. MaxQuant utilizes Andromeda search engine that incorporates length, charge and number of modi fi cations of the peptides for quality…”
MethodProteomic Mapping of Mitochondria in Living Cells via Spatially Restricted Enzymatic Tagging
Rhee, H.-W., Zou, P., Udeshi, N. D., Martell, J. D., Mootha, V. K., Carr, S. A., & Ting, A. Y. (2013) · Science · doi:10.1126/science.1230593 · Cited by 83 · cited 2× · see citing works
“Both BioID and APEX relies on a reactive biotin derivative that diffuses from an enzyme's active site to label proteins which are in spatial proximity [29].”
BackgroundNavare, A., Chavez, J. D., Zheng, C., Weisbrod, C. R., Eng, J. K., Siehnel, R., Singh, P. K., Manoil, C., & Bruce, J. E. (2015) · Structure · doi:10.1016/j.str.2015.01.022 · Cited by 82 · see citing works
Analysis of protein isoforms: Can we do it better?
Št́astná, M., & Eyk, J. E. V. (2012) · PROTEOMICS · doi:10.1002/pmic.201200161 · Cited by 79 · cited 1× · see citing works
“Additionally, tools such as “multiple reaction monitoring” (MRM) and “sequential window acquisition of all theoretical fragment-ion spectra” (SWATH MS) have also been proposed as effective strategies for determining proteoforms from a complex mixture [112].”
MethodChang, I. (2006) · PROTEOMICS · doi:10.1002/pmic.200600225 · Cited by 76 · cited 1× · see citing works
“from non-specific binding during the affinity purification step [99].”
BackgroundChen, Z. A., & Rappsilber, J. (2018) · Nature Protocols · doi:10.1038/s41596-018-0089-3 · Cited by 75 · cited 2× · see citing works
“Recent reports suggest that QXL-MS shows promising performance in studying the dynamics of human complement protein C3 [17 – 19].”
BackgroundScoring Large‐Scale Affinity Purification Mass Spectrometry Datasets with MiST
Verschueren, E., Dollen, J. V., Cimermančič, P., Gulbahce, N., Šali, A., & Krogan, N. J. (2015) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi0819s49 · Cited by 69 · cited 1× · see citing works
“Furthermore, it could potentially be addressed using computational approach by sorting the MS runs in the order they were run on the machine and delineate the signal intensities, number of spectra and number of identified unique peptides from proteins over consecutiveMS runs and utilize that information as a predictiv…”
BackgroundTriple SILAC to Determine Stimulus Specific Interactions in the Wnt Pathway
Hilger, M., & Mann, M. (2011) · Journal of Proteome Research · doi:10.1021/pr200740a · Cited by 68 · cited 1× · see citing works
“This has been shown to be useful in identifying protein interactions that are stimulus-dependent [53].”
BackgroundA review of statistical methods for protein identification using tandem mass spectrometry
Noble, W. S., & Serang, O. (2012) · Statistics and Its Interface · doi:10.4310/sii.2012.v5.n1.a2 · Cited by 67 · cited 3× · see citing works
“IDPicker is another pipeline that has been reported to show better specificity compared to the “one peptide” and “two peptide” rules, however showing lower sensitivity than both those approaches [106,109].”
BackgroundResultInfluentialTo Be or Not to Be? Five Guidelines to Avoid Misassignments in Cross-Linking/Mass Spectrometry
Iacobucci, C., & Sinz, A. (2017) · Analytical Chemistry · doi:10.1021/acs.analchem.7b02316 · Cited by 62 · cited 1× · see citing works
“However, there is a great scope for improvement in the computational algorithms that are used to identify the cross-linked peptides especially byimplementing novel and highly stringent error estimates[38,45 – 48].”
MethodThe First Zero‐Length Mass Spectrometry‐Cleavable Cross‐Linker for Protein Structure Analysis
Hage, C., Iacobucci, C., Rehkamp, A., Arlt, C., & Sinz, A. (2017) · Angewandte Chemie International Edition · doi:10.1002/anie.201708273 · Cited by 62 · cited 2× · see citing works
“Later, the inception of MS-cleavable cross-linkers such as disuccinimidyl sulfoxide (DSSO) [39], disuccinimidyl dibutyric urea (DSBU) [40,41] and 1,1 ′ - carbonyldiimidazole (CDI) [42], which generate a fragment ion signature that provides additional information for database search and reduce the number of iterations…”
BackgroundLabel-free quantitative proteomics trends for protein–protein interactions
Tate, S. A., Larsen, B. G., Bonner, R. F., & Gingras, A. (2012) · Journal of Proteomics · doi:10.1016/j.jprot.2012.10.027 · Cited by 59 · cited 1× · see citing works
“A potential disadvantage with amino-group isotopic-labelling strategies is the increased sample complexity due to the double peaks in MS spectra [71,72].”
BackgroundPark, S. S., Wu, W. W., Zhou, Y., Shen, R., Martin, B., & Maudsley, S. R. (2012) · Journal of Proteomics · doi:10.1016/j.jprot.2012.04.035 · Cited by 58 · cited 1× · see citing works
“This has been addressed by approaches such as dataset normalization [68] and labelswap replication [69,70].”
MethodSuper-SILAC: current trends and future perspectives
Shenoy, A., & Geiger, T. (2014) · Expert Review of Proteomics · doi:10.1586/14789450.2015.982538 · Cited by 58 · cited 1× · see citing works
“…cell line as a standard for comparison to in vitro post-isolation labelling of the cell line) [58] and Super-SILAC (which is run on a mixture of different cell lines and has the advantage over standard SILAC in not being limited by the number of samples that can be analyzed simultaneously) [57,59].”
BackgroundMakowski, M. M., Willems, E., Jansen, P. W. T. C., & Vermeulen, M. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m115.053082 · Cited by 57 · cited 1× · see citing works
“Efforts have been made to utilize cross-linking either as an additional step in the original protocol to covalently cross-link all the interactors to the bait protein [33] or as an independentmethod to identify proteome-wide interaction networks [34–38].”
MethodProtein Dynamics in Solution by Quantitative Crosslinking/Mass Spectrometry
Chen, Z. A., & Rappsilber, J. (2018) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2018.09.003 · Cited by 54 · see citing works
Trinkle‐Mulcahy, L. (2012) · PROTEOMICS · doi:10.1002/pmic.201100438 · Cited by 53 · see citing works
A researcher's guide to mass spectrometry‐based proteomics
Savaryn, J. P., Toby, T. K., & Kelleher, N. L. (2016) · PROTEOMICS · doi:10.1002/pmic.201600113 · Cited by 53 · see citing works
Quantitative Comparison of Proteomes Using SILAC
Deng, J., Erdjument‐Bromage, H., & Neubert, T. A. (2018) · Current Protocols in Protein Science · doi:10.1002/cpps.74 · Cited by 47 · cited 1× · see citing works
“MaxQuant [60] is one of the most widely used tools for processing large-scale SILAC datasets [61], along with Census [51,62], pQuant [63], TPP [64], IsoQuant [65] etc. MaxQuant utilizes Andromeda search engine that incorporates length, charge and number of modi fi cations of the peptides for quality…”
MethodMaXLinker: Proteome-wide Cross-link Identifications with High Specificity and Sensitivity
Yugandhar, K., Wang, T., Leung, A. K.-Y., Lanz, M. C., Motorykin, I., Liang, J. H., Shayhidin, E. E., Smolka, M. B., Zhang, S., & Yu, H. (2019) · Molecular & Cellular Proteomics · doi:10.1074/mcp.tir119.001847 · Cited by 47 · see citing works
Proteomics : methods and protocols
Reinders, J., & Sickmann, A. (2009) · Cited by 43 · see citing works
Census for Proteome Quantification
Park, S. K., & Yates, J. R. (2010) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi1312s29 · Cited by 40 · cited 4× · see citing works
“MaxQuant [60] is one of the most widely used tools for processing large-scale SILAC datasets [61], along with Census [51,62], pQuant [63], TPP [64], IsoQuant [65] etc. MaxQuant utilizes Andromeda search engine that incorporates length, charge and number of modi fi cations of the peptides for quality peptide spectrum ma…”
MethodInfluentialQuantitative cross-linking/mass spectrometry reveals subtle protein conformational changes
Chen, Z. A., Fischer, L., Tahir, S., Wills, J. C., Barlow, P. N., & Rappsilber, J. (2016) · Wellcome Open Research · doi:10.12688/wellcomeopenres.9896.1 · Cited by 39 · cited 1× · see citing works
“Recent reports suggest that QXL-MS shows promising performance in studying the dynamics of human complement protein C3 [17 – 19].”
BackgroundDevelopment of Large-scale Cross-linking Mass Spectrometry
Barysz, H. M., & Malmström, J. A. (2017) · Molecular & Cellular Proteomics · doi:10.1074/mcp.r116.061663 · Cited by 37 · see citing works
SILAC–based quantitative MS approach for real-time recording protein-mediated cell-cell interactions
Wang, X., He, Y., Ye, Y., Zhao, X., Deng, S., He, G., Zhu, H., Xu, N., & Liang, S. (2018) · Scientific Reports · doi:10.1038/s41598-018-26262-2 · Cited by 37 · cited 1× · see citing works
“It can also be used in investigating temporal proteomic changes, where the third cellular state is the time point of the treatment under study [54 – 56].”
BackgroundCasey, T. M., Khan, J. M., Bringans, S., Koudelka, T., Takle, P. S., Downs, R. A., Livk, A., Syme, R. A., Tan, K., & LIPSCOMBE, R. J. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b01154 · Cited by 36 · cited 1× · see citing works
“However, some of the previous studies reported that higher order multiplexing such as iTRAQ 8-plex and TMT 6-plex yielded consistently lower number of protein and peptide identi fi cations when compared to that of lower order multiplexing (iTRAQ 4-plex) [78,79].”
ResultA Novel Scoring Approach for Protein Co-Purification Data Reveals High Interaction Specificity
Yu, X., Ivanic, J., Wallqvist, A., & Jaques Reifman, A. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000515 · Cited by 30 · cited 1× · see citing works
“On the other hand, constrained randomized simulations have also been demonstrated to be effective in generating co-occurrence distributions for each protein pair in a reference-set independent manner [23].”
BackgroundPena, A. B., Gibbons, B. C., Slysz, G., Crowell, K. L., Monroe, M. E., Ibrahim, Y., Smith, R. D., Payne, S., & Baker, E. S. (2017) · International Journal of Mass Spectrometry · doi:10.1016/j.ijms.2017.11.003 · Cited by 30 · cited 3× · see citing works
“On the other hand, tools used for non-targeted MS runs generally involve usage of adjacent ion peaks that are not saturated to correct the saturated peaks [113,114].”
BackgroundMethodStudying macromolecular complex stoichiometries by peptide‐based mass spectrometry
Wohlgemuth, I., Lenz, C., & Urlaub, H. (2014) · PROTEOMICS · doi:10.1002/pmic.201400466 · Cited by 25 · cited 1× · see citing works
“Complex stoichiometry is a quantitative measure that refers to the relative amount of the constituents of a complex protein mixture [110].”
BackgroundMüller, M. Q., Zeiser, J. J., Dreiocker, F., Pich, A., Schäfer, M., & Sinz, A. (2010) · Rapid Communications in Mass Spectrometry · doi:10.1002/rcm.4812 · Cited by 24 · cited 2× · see citing works
“Later, the inception of MS-cleavable cross-linkers such as disuccinimidyl sulfoxide (DSSO) [39], disuccinimidyl dibutyric urea (DSBU) [40,41] and 1,1 ′ - carbonyldiimidazole (CDI) [42], which generate a fragment ion signature that provides additional information for database search and reduce the number of iterations…”
BackgroundMass Spectrometric-Based Quantitative Proteomics Using SILAC
Lanucara, F., & Eyers, C. E. (2011) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-385118-5.00008-6 · Cited by 23 · see citing works
Bioinformatics for Comparative Proteomics
(2010) · Methods in molecular biology · doi:10.1007/978-1-60761-977-2 · Cited by 23 · cited 3× · see citing works
“Furthermore, TPP supports both ETD (electron transfer dissociation) and CID (collision-induced dissociation) type of tandem MS data [67].”
MethodLiao, Z., Wan, Y., Thomas, S. N., & Yang, A. J. (2012) · Analytical Chemistry · doi:10.1021/ac300510t · Cited by 21 · cited 1× · see citing works
“…is one of the most widely used tools for processing large-scale SILAC datasets [61], along with Census [51,62], pQuant [63], TPP [64], IsoQuant [65] etc. MaxQuant utilizes Andromeda search engine that incorporates length, charge and number of modi fi cations of the peptides for quality peptide…”
MethodQuantifying Homologous Proteins and Proteoforms
Malioutov, D., Chen, T., Airoldi, E., Jaffe, J., Budnik, B., & Slavov, N. (2019) · Molecular & Cellular Proteomics · doi:10.1074/mcp.tir118.000947 · Cited by 21 · cited 4× · see citing works
“This issue is especially hard to deal in a bottom-up approach, where peptides generated due to enzymatic digestion could be shared between multiple proteoforms [111].”
BackgroundMethodInfluentialIdentifying binary protein-protein interactions from affinity purification mass spectrometry data
Zhang, X.-F., Ou-Yang, L., Hu, X. T., & Dai, D. (2015) · BMC Genomics · doi:10.1186/s12864-015-1944-z · Cited by 17 · cited 1× · see citing works
“Furthermore, features such as the topological relationships between direct physical interactions along with observed co-complex interactions [24] and gene ontology enrichment [25] have also been employed to prioritize potential true positive interactors.”
Neuroproteomics Methods and Protocols
Jellinger, K. A. (2009) · European Journal of Neurology · doi:10.1111/j.1468-1331.2009.02899.x · Cited by 10 · cited 1× · see citing works
“This has been addressed by the onset of Spike-in SILAC (which uses a stable isotope labelled cell line as a standard for comparison to in vitro post-isolation labelling of the cell line) [58] and Super-SILAC (which is run on a mixture of different cell lines and has the advantage over standard SILAC in not being limit…”
MethodCoreFlow: A computational platform for integration, analysis and modeling of complex biological data
Pasculescu, A., Schoof, E. M., Creixell, P., Zheng, Y., Olhovsky, M., Tian, R., So, J., Vanderlaan, R. D., Pawson, T. J., Linding, R., et al. (2014) · Journal of Proteomics · doi:10.1016/j.jprot.2014.01.023 · Cited by 10 · cited 1× · see citing works
“This has been addressed by approaches such as dataset normalization [68] and label-swap replication [69,70].”
MethodA two-step framework for inferring direct protein-protein interaction network from AP-MS data
Tian, B., Zhao, C., Gu, F., & He, Z. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0452-y · Cited by 10 · cited 1× · see citing works
“Morerecently,anensembleap-proach has been reported that utilizes multiple existing scoring methods to generate an initial network and then re fi ne it further by applying indirect association removal methods [26].”
Method
Flagged first, then by citations
Research works printed but not linked: 4
- [57]Pozniak Y. Geiger T. Warscheid B. Stable isotope labeling by amino acids in cell culture (SILAC): methods and protocols 2014 Springer New York New York, NY 281 291
- [60]Tyanova S. Mann M. Cox J. Warscheid B. Stable isotope labeling by amino acids in cell culture (SILAC): methods and protocols 2014 Springer New York New York, NY 351 364
- [93]Kristensen A.R. Foster L.J. Warscheid B. Stable isotope labeling by amino acids in cell culture (SILAC): methods and protocols 2014 Springer New York New York, NY 263 270
- [98]Min S.C. Kim E.J. El-Shourbagy T.A. Managing bioanalytical cross-contamination vol. 10 2007
Cited by
Sorted by citations
Advances and Trends in Omics Technology Development
Dai, X., & Shen, L. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.911861 · Cited by 567 · see citing works
Mapping the Degradable Kinome Provides a Resource for Expedited Degrader Development
Donovan, K. A., Ferguson, F. M., Bushman, J. W., Eleuteri, N. A., Bhunia, D., Ryu, S., Tan, L., Shi, K., Yue, H., Liu, X., et al. (2020) · Cell · doi:10.1016/j.cell.2020.10.038 · Cited by 359 · see citing works
Quantitative Mass Spectrometry-Based Proteomics: An Overview
Rozanova, S., Barkovits, K., Nikolov, M., Schmidt, C., Urlaub, H., & Marcus, K. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1024-4_8 · Cited by 279 · see citing works
Protein–protein interaction prediction with deep learning: A comprehensive review
Soleymani, F., Paquet, E., Viktor, H. L., Michalowski, W. J., & Spinello, D. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.08.070 · Cited by 195 · see citing works
Quantitative Mass Spectrometry-Based Proteomics: An Overview
Nikolov, M., Schmidt, C., & Urlaub, H. (2012) · Methods in molecular biology · doi:10.1007/978-1-61779-885-6_7 · Cited by 166 · see citing works
SAAMBE-3D: Predicting Effect of Mutations on Protein–Protein Interactions
Pahari, S., Li, G., Murthy, A. K., Liang, S., Fragoza, R., Yu, H., & Alexov, E. G. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21072563 · Cited by 109 · see citing works
“In the first case, one categorizes the mutations as disruptive (complex is not formed) and non-disruptive (complex is formed) [20,21].”
BackgroundOmics in Systems Biology: Current Progress and Future Outlook
Veenstra, T. D. (2020) · PROTEOMICS · doi:10.1002/pmic.202000235 · Cited by 104 · see citing works
Protein‐protein interaction networks as miners of biological discovery
Wang, S., Wu, R., Lü, J., Jiang, Y., Huang, T., & Cai, Y. (2022) · PROTEOMICS · doi:10.1002/pmic.202100190 · Cited by 84 · see citing works
“Yugandhar et al. broadly classified MS-based PPIs based on qualitative and quantitative techniques [18, 19].”
MethodChen, Z. A., & Rappsilber, J. (2018) · Nature Protocols · doi:10.1038/s41596-018-0089-3 · Cited by 75 · see citing works
Protein-protein interactions: Methods, databases, and applications in virus-host study
Farooq, Q. U. A., Shaukat, Z., Aiman, S., & Li, C. (2021) · World Journal of Virology · doi:10.5501/wjv.v10.i6.288 · Cited by 68 · see citing works
Salas, D., Stacey, R. G., Akinlaja, M. O., & Foster, L. J. (2019) · Molecular & Cellular Proteomics · doi:10.1074/mcp.r119.001803 · Cited by 66 · see citing works
Community Detection in Protein-Protein Interaction Networks and Applications
Manipur, I., Giordano, M., Piccirillo, M., Parashuraman, S., & Maddalena, L. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3138142 · Cited by 56 · see citing works
“However, experimental approaches have their own shortcomings, often resulting time-consuming, expensive, and inefficient [11], [12], [13].”
BackgroundMaXLinker: Proteome-wide Cross-link Identifications with High Specificity and Sensitivity
Yugandhar, K., Wang, T., Leung, A. K.-Y., Lanz, M. C., Motorykin, I., Liang, J. H., Shayhidin, E. E., Smolka, M. B., Zhang, S., & Yu, H. (2019) · Molecular & Cellular Proteomics · doi:10.1074/mcp.tir119.001847 · Cited by 47 · see citing works
Exploring the complexity of MECP2 function in Rett syndrome
Liu, Y., Whitfield, T. W., Bell, G. W., Guo, R., Flamier, A., Young, R. A., & Jaenisch, R. (2025) · Nature reviews. Neuroscience · doi:10.1038/s41583-025-00926-1 · Cited by 37 · see citing works
“However, the extent of the IDRs of MECP2 raises concerns about the suitability of AP–MS to study its interactome, because this technique may not effectively capture these IDR-mediated dynamic interactions 240,241 .”
Proteomics and plant biology: contributions to date and a look towards the next decade
Jorrín-Novo, J. V. (2021) · Expert Review of Proteomics · doi:10.1080/14789450.2021.1910028 · Cited by 35 · see citing works
Nitika, N., Zheng, B., Ruan, L., Kline, J. T., Omkar, S., Sikora, J. W., Torres, M. T., Wang, Y., Takakuwa, J. E., Huguet, R., et al. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001839 · Cited by 33 · see citing works
Peck Justice, S. A., Barron, M. P., Qi, G. D., Wijeratne, H. R. S., Victorino, J. F., Simpson, E. R., Vilseck, J. Z., Wijeratne, A. B., & Mosley, A. L. (2020) · Journal of Biological Chemistry · doi:10.1074/jbc.ra120.014576 · Cited by 28 · see citing works
“These methods have greatly increased our knowledge of PPIs but often require large amounts of starting material and involve other manipulations to the cell, such as introducing an epitope tag, or are reliant on antibodies that can differ widely in affinity and specificity (33, 34).”
Peck Justice, S. A., Qi, G., Wijeratne, H. R. S., Victorino, J. F., Simpson, E. R., Wijeratne, A. B., & Mosley, A. L. (2019) · doi:10.1101/2019.12.30.891267 · Cited by 28 · see citing works
Computing the Human InteractomePreprint
Zhang, J., Humphreys, I. R., Pei, J., Kim, J., Choi, C., Yuan, R., Durham, J., Liu, S., Choi, H., Baek, M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.01.615885 · Cited by 27 · see citing works
Proteomics in fresh and preserved pig semen: Recent achievements and future challenges
Roca, J., Pérez‐Patiño, C., Barranco, I., Padilla, L., Martinez, E. A., Rodríguez‐Martínez, H., & Parrilla, I. (2020) · Theriogenology · doi:10.1016/j.theriogenology.2020.01.066 · Cited by 26 · see citing works
Proteomics: A groundbreaking development in cancer biology
Mir, M. A., Qayoom, H., Sofi, S., & Jan, N. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-95072-5.00004-3 · Cited by 21 · see citing works
Kühn, L., Vincent, T., Hammann, P., & Zuber, H. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1967-4_11 · Cited by 20 · see citing works
Sun, J., Kulandaisamy, A., Liu, J., Hu, K., Gromiha, M. M., & Zhang, Y. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.01.036 · Cited by 19 · see citing works
“Alternatively, AP-MS involves purifying protein complexes formed by a tag-fused bait protein (e.g., antibody) and its interaction partners, which then undergo mass spectrometry for refined characterization [184–186].”
BackgroundApplications of CRISPR-Cas Technologies to Proteomics
Dolgalev, G. V., & Poverennaya, E. V. (2021) · Genes · doi:10.3390/genes12111790 · Cited by 17 · see citing works
“While TAP-MS is a powerful strategy to discover protein interactions, it lacks an ability to capture transient or weak interactions, for example the ones that often happen between enzyme and substrate [113].”
BackgroundApproaches to Investigating the Protein Interactome of PTEN
Smith, S. L., Pitt, A. R., & Spickett, C. M. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00570 · Cited by 16 · see citing works
Sharma, A., Singh, D., Gupta, P., Bhardwaj, S. K., Kaur, I., & Kumar, V. (2021) · Journal of Experimental Biology · doi:10.1242/jeb.242153 · Cited by 14 · see citing works
Allai, L., Li, C., & Quan, G. (2024) · Animal Reproduction Science · doi:10.1016/j.anireprosci.2024.107441 · Cited by 13 · see citing works
Microfluidic devices for protein analysis using intact and top‐down mass spectrometry
Shao, X., Huang, Y., & Wang, G. (2022) · Open Research (University of Surrey) · doi:10.1002/viw.20220032 · Cited by 13 · see citing works
Hevler, J. F., & Heck, A. J. R. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100657 · Cited by 11 · see citing works
“…interactions, occurring during the cell lysis step in sample preparation and the inability to identify weaker and transient interactions (42, 43) can be partially overcome by proximity labeling approaches, such as proximity-dependent biotin identi fi cation (BioID) and ascorbic acid…”
BackgroundTopological analysis as a tool for detection of abnormalities in protein–protein interaction data
Nowakowska, A. W., & Kotulska, M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac440 · Cited by 12 · see citing works
Prediction of Protein Solubility Based on Sequence Feature Fusion and DDcCNN
Wang, X., Liu, Y., Du, Z., Zhu, M., Kaushik, A. C., Jiang, X., & Wei, D. (2021) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-021-00456-1 · Cited by 11 · see citing works
Nature-inspired solutions: A comprehensive review of biomimetic nanoparticles in nanomedicine
Issaka, E., Tornyeava, B., Agyekum, E. A., Enyan, M., Amu‐Darko, J. N. O., & Chimuza, H. T. (2024) · Journal of Thermoplastic Composite Materials · doi:10.1177/08927057241264464 · Cited by 9 · see citing works
“93,147 BioM NPs can be loaded with targeted ligands that preferentially bind to receptors or antigens found on the surfaces of neurons or glial cells.”
Progress in methodologies and quality‐control strategies in protein cross‐linking mass spectrometry
Yugandhar, K., Zhao, Q., Gupta, S., Xiong, D., & Yu, H. (2021) · PROTEOMICS · doi:10.1002/pmic.202100145 · Cited by 8 · see citing works
“pivotal role in identificationandquantificationof protein-protein interactions [2].”
Evolution of biophysical tools for quantitative protein interactions and drug discovery
Harish, M., & Venkatraman, P. (2021) · Emerging Topics in Life Sciences · doi:10.1042/etls20200258 · Cited by 7 · see citing works
González‐Avendaño, M., Zúñiga-Almonacid, S., Silva, I., Lavanderos, B. J., Robinson, F., Rosales‐Rojas, R., Durán-Verdugo, F., González, W., Cáceres, M., Cerda, O., et al. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.701477 · Cited by 6 · see citing works
“MS-based proteomics methods have gained great relevance, offering high sensitivity and specificity to decipher PPI networks and to study their interaction dynamics (Yakubu et al., 2019; Yugandhar et al., 2019).”
BackgroundVelasquez, E. F., Garcia, Y. A., Ramírez, I., Gholkar, A. A., & Torres, J. Z. (2021) · Molecular Biology of the Cell · doi:10.1091/mbc.e21-05-0257 · Cited by 6 · see citing works
“PPI/As can inform on how a POI is compartmentalized within a cell, how it forms higher-order complexes, how it is regulated, and how it coordinates with other proteins in a spatial and temporal manner to carry out specific cellular processes (Yugandhar et al., 2019; Lu et al., 2020).”
BackgroundInfluentialRole of Genomics and Proteomics in Drug Discovery
Sundarasekar, J., & Sahgal, G. (2024) · Interdisciplinary biotechnological advances · doi:10.1007/978-981-97-1148-2_11 · Cited by 6 · see citing works
Mass spectrometry for mitochondrial multi-omics
Wang, Z., Liu, B., Lin, L., & Qiao, L. (2023) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2023.117063 · Cited by 7 · see citing works
Therapeutic potential of protein–protein interaction modulators in antiviral research
Kaur, G., Thapa, K., Sood, P., Rani, L., Rani, I., Gautam, R. K., Goyal, K., Ali, H., Mishra, R., Hussain, M. S., et al. (2025) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2025.109346 · Cited by 4 · see citing works
Zhang, K., Xu, M., Hu, X., Tan, P., Su, J., Abbas, M. N., & Cui, H. (2023) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2023.128515 · Cited by 4 · see citing works
Mapping histone modification-dependent protein interactions with chemical proteomics
Chen, Y., & Wang, Y. (2021) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2021.11.002 · Cited by 4 · see citing works
Quantitative proteomics reveals CLR interactome in primary human cells
Manolis, D., Hasan, S. R., Ettelaie, C., Maraveyas, A., O'Brien, D. P., Kessler, B. M., Kramer, H., & Nikitenko, L. L. (2024) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2024.107399 · Cited by 3 · see citing works
Emmanuel, J., Isewon, I., Olasehinde, G. I., & Oyelade, J. O. (2024) · Current Bioinformatics · doi:10.2174/0115748936286848240108074303 · Cited by 3 · see citing works
Protein phylogenetics with mass spectrometry. A comparison of methods
Downard, K. M. (2021) · Analytical Methods · doi:10.1039/d1ay00153a · Cited by 3 · see citing works
Nitika, N., Zheng, B., Ruan, L., Kline, J. T., Sikora, J. W., Torres, M. T., Wang, Y., Takakuwa, J. E., Huguet, R., Klemm, C., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.25.457671 · Cited by 3 · see citing works
“While large-scale interactome studies of chaperones have been attempted previously, the methods used in these attempts have associated drawbacks that prevent a comprehensive analysis of the system (Koegl and Uetz, 2007; Yugandhar et al., 2019).”
MethodMolecular Imaging of Protein–Protein Interactions and Protein Folding
Massoud, T. F., & Paulmurugan, R. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-816386-3.00071-5 · Cited by 2 · see citing works
Cost-Effective Mapping of Genetic Interactions in Mammalian Cells
Khan, A. H., & Smith, D. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.703738 · Cited by 2 · see citing works
NRBO-AGP: A novel feature selection approach for accurate protein solubility prediction
Elmi, Z., Elmi, S., & Danishvar, S. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129194 · Cited by 1 · see citing works
Data mining in mass spectrometry-based proteomics studies
Le, V. A., Phan, C. Q. T., & Nguyen, T. H. (2020) · Science & Technology Development Journal - Engineering and Technology · doi:10.32508/stdjet.v2i4.483
Screening of CCDC43 molecular partners by BioID2-based proximity labeling
Öztürk, M. T. (2025) · TURKISH JOURNAL OF BIOLOGY · doi:10.55730/1300-0152.2744
“…atomic force microscopy, surface plasmon resonance, Förster resonance energy transfer (FRET), bioluminescence resonance energy transfer (BRET), bimolecular fluorescence complementation (BIFC), and superresolution microscopy (Yugandhar et al., 2019; Benz et al., 2020; Richards et al., 2021).”
Deciphering Protein Interaction Dynamics Using the Yeast Three-Hybrid System
Liu, Q., & Ponnu, J. (2026) · Methods in molecular biology · doi:10.1007/978-1-0716-5214-5_16
- Authors
- Yugandhar, K., Gupta, S., & Yu, H.
- Affiliations
- Cornell University (US)
- Corresponding author
- Yu, Haiyuan
- Venue
- Computational and Structural Biotechnology Journal
- Published
- 2019-01
- Volume
- 17
- Pages
- 805-811
- ISSN
- 2001-0370 (ISSN-L)
- PMC
- PMC6611912
- OpenAlex
- W2949571777
- Cited by
- 51
- References
- 114
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 6 · 2023: 5 · 2022: 6 · 2021: 17 · 2020: 8 · 2019: 2 · 2018: 1 · 2012: 1
- FWCI
- 0.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computational and Structural Biotechnology Journal (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institutes of Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Biotin and Related Studies
- Keywords
- protein-protein interaction network, mass spectrometry-based proteomics, interaction quantification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
