A critical aspect of any drug development effort is the ability to define compound mechanism of action. Most small molecule therapeutics function by engaging a protein (target) through a direct physical interaction, thereby inhibiting or activating a specific target in order to modulate enzymatic activity, cell signaling pathways, or gene expression. Because of its unique ability to probe cell function on a proteome-wide scale, mass spectrometry (MS) has emerged as a powerful tool for interrogating compound mechanism of action by aiding in the validation of known targets and the identification of new ones (Moellering and Cravatt, 2012; Daub, 2015; Schürmann et al., 2016; Frantzi et al., 2019; Robers et al., 2020; Kuljanin et al., 2021; Meissner et al., 2022). In addition to traditional proteomic approaches that profile protein expression and post-translational modifications (PTMs), the mass spectrometer has also become indispensable for unbiased assessments of compound target engagement and, by extension, mechanism of action. This has led to the development of numerous MS-based methods to screen the proteome for evidence of ligand binding (Lomenick et al., 2009; Molina et al., 2013; Jafari et al., 2014; Savitski et al., 2014; Liu et al., 2017; Gaetani et al., 2019; Ball et al., 2020; Van Vranken et al., 2021; Beusch et al., 2022).

All proteins have a characteristic melting temperature, and this physical property can be influenced by numerous factors (ligand-binding, protein-protein interactions, and PTMs) that make a given protein more or less resistant to thermal denaturation (Koshland, 1958). Classically, the thermal shift assay was used to study a single protein of interest, however, by coupling the traditional thermal shift assay with modern quantitative proteomics and sample multiplexing, it is possible to assess changes in protein thermal stability on a proteome-wide scale (Molina et al., 2013; Jafari et al., 2014; Savitski et al., 2014). As such, MS-coupled thermal shift assays have emerged as a powerful tool for studying compound target engagement and mechanism of action in lysates, cells, and even tissues (Molina et al., 2013; Savitski et al., 2014; Jafari et al., 2014; Franken et al., 2015; Huber et al., 2015; Reinhard et al., 2015; Becher et al., 2016; Mateus et al., 2016; Sridharan et al., 2019a; Mateus et al., 2020; Perrin et al., 2020).

The cellular thermal shift assay (CETSA) and thermal proteome profiling (TPP) use tandem mass tag (TMT)-based quantitative proteomics to quantify complete melting curves and assign melting temperatures (T_M) to thousands of proteins, simultaneously, making it possible to screen the proteome for ligand-induced changes in protein thermal stability (Molina et al., 2013; Savitski et al., 2014; Jafari et al., 2014; Perrin et al., 2020; Zinn et al., 2021). More recently, the proteome integral solubility alteration (PISA) assay was proposed to improve the efficiency of the proteome-wide thermal shift assay (Gaetani et al., 2019). Rather than building complete melting curves, PISA uses TMT-based quantitative proteomics to estimate, or integrate, the area under a protein melting curve (Figure 1—figure supplement 1A). Instead of TMT labelling the soluble fraction from each individual temperature—as in CETSA and TPP—PISA pools the soluble fractions of multiple samples heated across a thermal gradient such that a single TMT reporter represents an entire integrated melting curve. So, while CETSA and TPP measure a change in melting temperature (∆T_M), PISA measures a change in solubility (∆S_M). Critically, there is a strong correlation between ∆T_M and ∆S_M, which makes PISA a reliable, if still imperfect, surrogate for measuring direct changes in protein thermal stability (Gaetani et al., 2019; Li et al., 2020). Thus, in the context of PISA, a change in protein thermal stability (or a thermal shift) can be defined as a fold change in the abundance of soluble protein in a compound-treated sample vs. a vehicle-treated control after thermal denaturation and high-speed centrifugation. Therefore, an increase in melting temperature, which one could determine using CETSA or TPP, will lead to an increase in the area under the curve and an increase in the soluble protein abundance relative to controls (positive log₂ fold change). Conversely, a decrease in melting temperature will result in a decrease in the area under the curve and a decrease in the soluble protein abundance relative to controls (negative log₂ fold change). In the end, the ability to compress entire melting curves into a single TMT channel enables the synchronized interrogation of multiple compounds at multiple concentrations with multiple replicates in one experiment (Gaetani et al., 2019).

Owing to improved scalability, PISA results in an eightfold theoretical improvement in throughput compared to TPP and CETSA. Therefore, PISA makes it feasible to readily approach large-scale chemical perturbation experiments, such as chemical library screening. With this in mind, we sought to establish an efficient platform for screening chemical libraries for drug-induced changes in protein thermal stability and test the ability of this workflow to provide meaningful compound-specific data when performed at scale. We selected 96 compounds with known mechanisms of action for PISA screening in live K562 cells and further assayed 70 of the compounds in native lysates. Using these data, we identified evidence of on-target binding, as well as putative examples of off-target engagement. In addition to direct drug binding, we were able to identify secondary changes in protein thermal stability that did not result from direct engagement and, instead, likely stemmed from other factors such as PTMs or changes in macromolecular interactions. Overall, this study serves as a roadmap for the scalable implementation of protein thermal stability quantitation to interrogate compound mechanism of action.

The determination of protein-compound engagement and mechanism of action is an essential aspect of the drug development process. Herein we present approachable methods for the implementation and interpretation of proteome-wide thermal shifts assays on at 96-well plate scale. Taking advantage of the eightfold increase in efficiency afforded by PISA, we catalogued compound-dependent changes in protein thermal stability in response to 96 compounds in living K562 cells and 70 compounds in native extracts. This study resulted in a total of 871,120 differential solubility (thermal stability) measurements in living cells and an additional 627,176 measurements in lysates. Exploiting this wealth of data, we were able to find clear evidence of on-target engagement for the majority of compounds tested in both cell- and lysate-based experiments. Furthermore, there were numerous potential examples of off-target engagement. In addition to direct target engagement, there were many secondary changes in protein thermal stability, as evidenced by changes in solubility that occurred independent of direct binding. Finally, using an all-by-all analysis of compounds and proteins, we demonstrated how structurally related proteins and protein complexes engage consistently with our library of compounds. Our study serves as an unbiased assessment of the current technology and serves as a comprehensive guide for ongoing work to enable library-scale study of protein-compound and protein-ligand interactions.

One of the most valuable features of the proteome-wide thermal shift assay, including PISA, is the ability to screen compounds in both living cells and native lysates (Molina et al., 2013; Savitski et al., 2014; Franken et al., 2015; Huber et al., 2015; Becher et al., 2016; Becher et al., 2018; Dai et al., 2018; Gaetani et al., 2019; Sridharan et al., 2019b; Sabatier et al., 2022; Sabatier et al., 2021). In this study, we further demonstrate that these two approaches are not redundant, but, rather, provide complimentary data that can be employed to help establish compound mechanism of action. First and foremost, each approach was similarly proficient in identifying compound-dependent changes of known targets (Figures 1 and 3). In both cell- and lysate-based PISA experiments, about 70% of all compounds (whose target was quantified) induced a significant change in the solubility of its well-established target(s). Furthermore, when the two datasets were combined, that figure increased to over 80%. This is because certain targets, like BRAF, for example were more prone to undergo a change in solubility in one experimental setting rather than the other (Figure 4D).

In general, a compound-dependent thermal shift that occurs in a lysate-based experiment is almost certain to stem from direct target engagement (Savitski et al., 2014; Becher et al., 2016). This is because cell signaling pathways and cellular structures are disrupted and diluted. Cell-based studies, on the other hand, have the added potential to identify the targets of pro-drugs that must be metabolized in the cell to become active and secondary changes that occur independent of direct engagement (Savitski et al., 2014; Franken et al., 2015; Almqvist et al., 2016; Becher et al., 2016; Liang et al., 2022). Consistent with this dichotomy, we found that, on average, compounds caused a greater number of thermal shifts (as evidenced by changes in solubility) in cells than lysates. This is further supported by the PF-3758309-dependent thermal destabilization of spliceosome subunits. Finally, we documented a number of examples of phosphorylation-dependent thermal shifts. For example, compounds targeting CDKs tended to impact the thermal stability of RB1. Likewise, compounds targeting tyrosine kinases induced a thermal destabilization of downstream phosphorylation targets like CRKL and CRK. In these examples, an inhibitor engages and inhibits a direct target, which, in turn, prevents the phosphorylation of a downstream target thereby inducing a change in thermal stability (Savitski et al., 2014).

The idea of area under the curve analysis (PISA) remains a relatively recent modification of the traditional proteome-wide thermal shift assay (Gaetani et al., 2019). Using this dataset, we were able to benchmark many aspects of this advance and highlight many important considerations when designing and interpreting these experiments. First, we highlight the importance of selecting an appropriate thermal window (Li et al., 2020). In this study we selected a temperature range of 48–58°C, however, it is possible that deploying multiple thermal windows could be a viable strategy for focused interrogation of a small number of high-value compounds. In utilizing thermal windows, the goal is to maximize the potential magnitudes of the final fold change measurements. Still, we observed a large range in the magnitudes of the log₂ fold changes that were quantified for known compound targets. This is despite the fact, that we utilized a high treatment dose (10 µM) to maximize the magnitude of the fold change of the primary target. Ultimately, the data seemed to be consistent with previous studies that indicate the maximal change in thermal stability in protein specific (Savitski et al., 2014; Becher et al., 2016; Sabatier et al., 2022). Therefore, a log₂ fold change of 2 for one compound-protein pair could be just as meaningful as a log₂ fold change of 0.2 for another. In either case though we find that it is essential to optimize the thermal denaturation to ensure maximally useful effect sizes when comparing compounds.

While the primary screen was carried out at fixed dose, the increased throughput of PISA allowed for certain compounds to be assayed at multiple doses in a single experiment. In these instances, there was a clear dose-dependent change in thermal stability of primary targets, off-targets, and secondary targets. This not only helped corroborate observations from the primary screen, but also seemed to provide a qualitative assessment of relative compound potency in agreement with previous studies (Savitski et al., 2014; Becher et al., 2016; Mateus et al., 2016). Specifically, the compounds that most strongly impacted the thermal stability of targets, also acted as the most potent inhibitors. In order to be a candidate for this type of study, a target must have a large maximal thermal shift (magnitude of log₂ fold change in solubility) because there must be a large enough dynamic range to clearly resolve different doses. From this study, PIK3CB (maximal log₂ fold change ~0.2) would make a bad candidate, while p38α (maximal log₂ fold change ~2.0) would be a good candidate. Importantly, however, it should be possible to tune the thermal window in order to specifically increase the maximal log₂ fold change of PI3KCB for dose-dependency studies.

Each of the compounds used in this study have a well-defined target and known mechanism of action. Furthermore, many of the compounds have been used extensively as tool compounds in basic research and as therapeutics in the treatment of multiple cancers. Despite the frequency with which many of these compounds have been used, the data generated in this study suggests that the current mechanistic understanding of many (if not all) of these compounds remains incomplete. For example, we found that the clinically deployed CDK4/6 inhibitor palbociclib has a dramatic impact on PLK1 thermal stability in live cells, is capable of inhibiting PLK1 activity in cell-based assays, and can be modelled into the PLK1 active site. Importantly, this activity could contribute to the mechanism of action of palbociclib and further supports the conclusions of a previous study that implicated palbociclib as a potential inhibitor of PLK1 (Hafner et al., 2019). Similarly, PISA helped reveal that the CHEK1 inhibitor AZD-7762 is, in fact, a highly promiscuous kinase inhibitor that, amongst many other kinases, appears to engage a number of tyrosine kinases and inhibit downstream signaling. While this compound has not been approved for clinical use, it has been used frequently as a tool compound to study the biological impact of CHEK1 inhibition. Additionally, the PLK1 inhibitor BI 2536 engages non-protein kinases such as PIP4K2A/B/C. As inhibition of PIP4K2s has been shown un hematological cells, this potentially suggests the mechanism of dose-limiting hematologic events upon patient treatment with BI 2536 (Frost et al., 2012; Lima et al., 2022). Overall, these examples, and the dataset as a whole, suggest that proteome-wide thermal shift assays and PISA, specifically, are valuable tools to help resolve the full range of compound activities. Despite this utility, however, it is important to note that these assays, alone, are unlikely to paint a full picture of mechanism of action. Instead, they represent a valuable tool that can be coupled with other MS-based and genetic approaches.

In the end, this study highlights the immense power of thermal stability assays in screening large collections of molecules to help define target engagement and mechanism of action on a proteome-wide scale. While this type of study is ideal for finding the targets of compounds that have been identified in high-throughput screening, they can provide valuable information in almost any context. This includes compounds that seemingly already have a well-defined target. Despite finding many ways to utilize this data, we expect that this dataset contains many interesting observations that can not only be used to better understand specific drugs, but also has the potential to reveal new biology. Moving forward, we hope that this data will serve as a valuable resource for those interested in drug development and the research community at large.

The mass spectrometry data have been deposited to the ProteomeXchange Consortium with the data set identifier PXD048009 (initial submission) and PXD053138 (revised submission). Information regarding TMT channel assignments and plex layout can be found in Supplementary file 2.

1. 1. Van Vranken JG
   2. Li J
   3. Mintseris J
   4. Wei T
   5. Sniezek CM
   6. Gadzuk-Shea M
   7. Gygi SP
   8. Schweppe DK

   (2024) PRIDE

   ID PDX048009. Large-scale characterization of drug mechanism of action using proteome-wide thermal shift assays (Initial submission).

   https://www.ebi.ac.uk/pride/archive/projects/PDX048009
2. 1. Van Vranken JG
   2. Li J
   3. Mintseris J
   4. Wei T
   5. Sniezek CM
   6. Gadzuk-Shea M
   7. Gygi SP
   8. Schweppe DK

   (2024) PRIDE

   ID PDX053138. Large-scale characterization of drug mechanism of action using proteome-wide thermal shift assays (Revised submission).

   https://www.ebi.ac.uk/pride/archive/projects/PDX053138

1. 1. Almqvist H
   2. Axelsson H
   3. Jafari R
   4. Dan C
   5. Mateus A
   6. Haraldsson M
   7. Larsson A
   8. Martinez Molina D
   9. Artursson P
   10. Lundbäck T
   11. Nordlund P

   (2016) CETSA screening identifies known and novel thymidylate synthase inhibitors and slow intracellular activation of 5-fluorouracil

   Nature Communications 7:11040.

   https://doi.org/10.1038/ncomms11040

   • PubMed
   • Google Scholar
2. 1. Amiran MR
   2. Taghdir M
   3. Abasi Joozdani F

   (2023) Molecular insights into the behavior of the allosteric and ATP-competitive inhibitors in interaction with AKT1 protein: A molecular dynamics study

   International Journal of Biological Macromolecules 242:124853.

   https://doi.org/10.1016/j.ijbiomac.2023.124853

   • PubMed
   • Google Scholar
3. 1. Ball KA
   2. Webb KJ
   3. Coleman SJ
   4. Cozzolino KA
   5. Jacobsen J
   6. Jones KR
   7. Stowell MHB
   8. Old WM

   (2020) An isothermal shift assay for proteome scale drug-target identification

   Communications Biology 3:75.

   https://doi.org/10.1038/s42003-020-0795-6

   • PubMed
   • Google Scholar
4. 1. Becher I
   2. Werner T
   3. Doce C
   4. Zaal EA
   5. Tögel I
   6. Khan CA
   7. Rueger A
   8. Muelbaier M
   9. Salzer E
   10. Berkers CR
   11. Fitzpatrick PF
   12. Bantscheff M
   13. Savitski MM

   (2016) Thermal profiling reveals phenylalanine hydroxylase as an off-target of panobinostat

   Nature Chemical Biology 12:908–910.

   https://doi.org/10.1038/nchembio.2185

   • PubMed
   • Google Scholar
5. 1. Becher I
   2. Andrés-Pons A
   3. Romanov N
   4. Stein F
   5. Schramm M
   6. Baudin F
   7. Helm D
   8. Kurzawa N
   9. Mateus A
   10. Mackmull MT
   11. Typas A
   12. Müller CW
   13. Bork P
   14. Beck M
   15. Savitski MM

   (2018) Pervasive protein thermal stability variation during the cell cycle

   Cell 173:1495–1507.

   https://doi.org/10.1016/j.cell.2018.03.053

   • PubMed
   • Google Scholar
6. 1. Beusch CM
   2. Sabatier P
   3. Zubarev RA

   (2022) Ion-based proteome-integrated solubility alteration assays for systemwide profiling of protein-molecule interactions

   Analytical Chemistry 94:7066–7074.

   https://doi.org/10.1021/acs.analchem.2c00391

   • PubMed
   • Google Scholar
7. 1. Byth KF
   2. Thomas A
   3. Hughes G
   4. Forder C
   5. McGregor A
   6. Geh C
   7. Oakes S
   8. Green C
   9. Walker M
   10. Newcombe N
   11. Green S
   12. Growcott J
   13. Barker A
   14. Wilkinson RW

   (2009) AZD5438, a potent oral inhibitor of cyclin-dependent kinases 1, 2, and 9, leads to pharmacodynamic changes and potent antitumor effects in human tumor xenografts

   Molecular Cancer Therapeutics 8:1856–1866.

   https://doi.org/10.1158/1535-7163.MCT-08-0836

   • PubMed
   • Google Scholar
8. 1. Chen P
   2. Lee NV
   3. Hu W
   4. Xu M
   5. Ferre RA
   6. Lam H
   7. Bergqvist S
   8. Solowiej J
   9. Diehl W
   10. He YA
   11. Yu X
   12. Nagata A
   13. VanArsdale T
   14. Murray BW

   (2016) Spectrum and degree of cdk drug interactions predicts clinical performance

   Molecular Cancer Therapeutics 15:2273–2281.

   https://doi.org/10.1158/1535-7163.MCT-16-0300

   • PubMed
   • Google Scholar
9. 1. Cucchi U
   2. Gianellini LM
   3. De Ponti A
   4. Sola F
   5. Alzani R
   6. Patton V
   7. Pezzoni A
   8. Troiani S
   9. Saccardo MB
   10. Rizzi S
   11. Giorgini ML
   12. Cappella P
   13. Beria I
   14. Valsasina B

   (2010)

   Phosphorylation of TCTP as a marker for polo-like kinase-1 activity in vivo

   Anticancer Research 30:4973–4985.

   • PubMed
   • Google Scholar
10. 1. Dai L
    2. Zhao T
    3. Bisteau X
    4. Sun W
    5. Prabhu N
    6. Lim YT
    7. Sobota RM
    8. Kaldis P
    9. Nordlund P

    (2018) Modulation of protein-interaction states through the cell cycle

    Cell 173:1481–1494.

    https://doi.org/10.1016/j.cell.2018.03.065

    • PubMed
    • Google Scholar
11. 1. Daub H

    (2015) Quantitative proteomics of kinase inhibitor targets and mechanisms

    ACS Chemical Biology 10:201–212.

    https://doi.org/10.1021/cb5008794

    • PubMed
    • Google Scholar
12. 1. Eid S
    2. Turk S
    3. Volkamer A
    4. Rippmann F
    5. Fulle S

    (2017) KinMap: a web-based tool for interactive navigation through human kinome data

    BMC Bioinformatics 18:16.

    https://doi.org/10.1186/s12859-016-1433-7

    • PubMed
    • Google Scholar
13. 1. Elias JE
    2. Gygi SP

    (2007) Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry

    Nature Methods 4:207–214.

    https://doi.org/10.1038/nmeth1019

    • PubMed
    • Google Scholar
14. 1. Eng JK
    2. Jahan TA
    3. Hoopmann MR

    (2013) Comet: an open-source MS/MS sequence database search tool

    PROTEOMICS 13:22–24.

    https://doi.org/10.1002/pmic.201200439

    • PubMed
    • Google Scholar
15. 1. Fischer PM

    (2003) CDK versus gsk-3 inhibition: a purple haze no longer

    Chemistry & Biology 10:1144–1146.

    https://doi.org/10.1016/j.chembiol.2003.12.009

    • Google Scholar
16. 1. Franken H
    2. Mathieson T
    3. Childs D
    4. Sweetman GMA
    5. Werner T
    6. Tögel I
    7. Doce C
    8. Gade S
    9. Bantscheff M
    10. Drewes G
    11. Reinhard FBM
    12. Huber W
    13. Savitski MM

    (2015) Thermal proteome profiling for unbiased identification of direct and indirect drug targets using multiplexed quantitative mass spectrometry

    Nature Protocols 10:1567–1593.

    https://doi.org/10.1038/nprot.2015.101

    • PubMed
    • Google Scholar
17. 1. Frantzi M
    2. Latosinska A
    3. Mischak H

    (2019) PROTEOMICS in drug development: the dawn of a new era?

    Proteomics. Clinical Applications 13:1800087.

    https://doi.org/10.1002/prca.201800087

    • PubMed
    • Google Scholar
18. 1. Frost A
    2. Mross K
    3. Steinbild S
    4. Hedbom S
    5. Unger C
    6. Kaiser R
    7. Trommeshauser D
    8. Munzert G

    (2012) Phase I study of the Plk1 inhibitor BI 2536 administered intravenously on three consecutive days in advanced solid tumours

    Current Oncology 19:e28–e35.

    https://doi.org/10.3747/co.19.866

    • PubMed
    • Google Scholar
19. 1. Gaetani M
    2. Sabatier P
    3. Saei AA
    4. Beusch CM
    5. Yang Z
    6. Lundström SL
    7. Zubarev RA

    (2019) Proteome integral solubility alteration: a high-throughput proteomics assay for target deconvolution

    Journal of Proteome Research 18:4027–4037.

    https://doi.org/10.1021/acs.jproteome.9b00500

    • PubMed
    • Google Scholar
20. 1. Gassaway BM
    2. Li J
    3. Rad R
    4. Mintseris J
    5. Mohler K
    6. Levy T
    7. Aguiar M
    8. Beausoleil SA
    9. Paulo JA
    10. Rinehart J
    11. Huttlin EL
    12. Gygi SP

    (2022) A multi-purpose, regenerable, proteome-scale, human phosphoserine resource for phosphoproteomics

    Nature Methods 19:1371–1375.

    https://doi.org/10.1038/s41592-022-01638-5

    • PubMed
    • Google Scholar
21. 1. Giuliano CJ
    2. Lin A
    3. Smith JC
    4. Palladino AC
    5. Sheltzer JM

    (2018) MELK expression correlates with tumor mitotic activity but is not required for cancer growth

    eLife 7:e32838.

    https://doi.org/10.7554/eLife.32838

    • PubMed
    • Google Scholar
22. 1. Hafner M
    2. Mills CE
    3. Subramanian K
    4. Chen C
    5. Chung M
    6. Boswell SA
    7. Everley RA
    8. Liu C
    9. Walmsley CS
    10. Juric D
    11. Sorger PK

    (2019) Multiomics profiling establishes the polypharmacology of fda-approved cdk4/6 inhibitors and the potential for differential clinical activity

    Cell Chemical Biology 26:1067–1080.

    https://doi.org/10.1016/j.chembiol.2019.05.005

    • PubMed
    • Google Scholar
23. 1. Herneisen AL
    2. Lourido S

    (2021) Thermal proteome profiling to identify protein-ligand interactions in the apicomplexan parasite Toxoplasma gondii

    Bio-Protocol 11:e4207.

    https://doi.org/10.21769/BioProtoc.4207

    • PubMed
    • Google Scholar
24. 1. Hirai H
    2. Sootome H
    3. Nakatsuru Y
    4. Miyama K
    5. Taguchi S
    6. Tsujioka K
    7. Ueno Y
    8. Hatch H
    9. Majumder PK
    10. Pan BS
    11. Kotani H

    (2010) MK-2206, an allosteric Akt inhibitor, enhances antitumor efficacy by standard chemotherapeutic agents or molecular targeted drugs in vitro and in vivo

    Molecular Cancer Therapeutics 9:1956–1967.

    https://doi.org/10.1158/1535-7163.MCT-09-1012

    • PubMed
    • Google Scholar
25. 1. Huber KVM
    2. Olek KM
    3. Müller AC
    4. Tan CSH
    5. Bennett KL
    6. Colinge J
    7. Superti-Furga G

    (2015) Proteome-wide drug and metabolite interaction mapping by thermal-stability profiling

    Nature Methods 12:1055–1057.

    https://doi.org/10.1038/nmeth.3590

    • PubMed
    • Google Scholar
26. 1. Hughes CS
    2. Foehr S
    3. Garfield DA
    4. Furlong EE
    5. Steinmetz LM
    6. Krijgsveld J

    (2014) Ultrasensitive proteome analysis using paramagnetic bead technology

    Molecular Systems Biology 10:757.

    https://doi.org/10.15252/msb.20145625

    • PubMed
    • Google Scholar
27. 1. Hughes CS
    2. Moggridge S
    3. Müller T
    4. Sorensen PH
    5. Morin GB
    6. Krijgsveld J

    (2019) Single-pot, solid-phase-enhanced sample preparation for proteomics experiments

    Nature Protocols 14:68–85.

    https://doi.org/10.1038/s41596-018-0082-x

    • PubMed
    • Google Scholar
28. 1. Huttlin EL
    2. Jedrychowski MP
    3. Elias JE
    4. Goswami T
    5. Rad R
    6. Beausoleil SA
    7. Villén J
    8. Haas W
    9. Sowa ME
    10. Gygi SP

    (2010) A tissue-specific atlas of mouse protein phosphorylation and expression

    Cell 143:1174–1189.

    https://doi.org/10.1016/j.cell.2010.12.001

    • PubMed
    • Google Scholar
29. 1. Huttlin EL
    2. Bruckner RJ
    3. Paulo JA
    4. Cannon JR
    5. Ting L
    6. Baltier K
    7. Colby G
    8. Gebreab F
    9. Gygi MP
    10. Parzen H
    11. Szpyt J
    12. Tam S
    13. Zarraga G
    14. Pontano-Vaites L
    15. Swarup S
    16. White AE
    17. Schweppe DK
    18. Rad R
    19. Erickson BK
    20. Obar RA
    21. Guruharsha KG
    22. Li K
    23. Artavanis-Tsakonas S
    24. Gygi SP
    25. Harper JW

    (2017) Architecture of the human interactome defines protein communities and disease networks

    Nature 545:505–509.

    https://doi.org/10.1038/nature22366

    • PubMed
    • Google Scholar
30. 1. Huttlin EL
    2. Bruckner RJ
    3. Navarrete-Perea J
    4. Cannon JR
    5. Baltier K
    6. Gebreab F
    7. Gygi MP
    8. Thornock A
    9. Zarraga G
    10. Tam S
    11. Szpyt J
    12. Gassaway BM
    13. Panov A
    14. Parzen H
    15. Fu S
    16. Golbazi A
    17. Maenpaa E
    18. Stricker K
    19. Guha Thakurta S
    20. Zhang T
    21. Rad R
    22. Pan J
    23. Nusinow DP
    24. Paulo JA
    25. Schweppe DK
    26. Vaites LP
    27. Harper JW
    28. Gygi SP

    (2021) Dual proteome-scale networks reveal cell-specific remodeling of the human interactome

    Cell 184:3022–3040.

    https://doi.org/10.1016/j.cell.2021.04.011

    • PubMed
    • Google Scholar
31. 1. Jafari R
    2. Almqvist H
    3. Axelsson H
    4. Ignatushchenko M
    5. Lundbäck T
    6. Nordlund P
    7. Martinez Molina D

    (2014) The cellular thermal shift assay for evaluating drug target interactions in cells

    Nature Protocols 9:2100–2122.

    https://doi.org/10.1038/nprot.2014.138

    • PubMed
    • Google Scholar
32. 1. Jarzab A
    2. Kurzawa N
    3. Hopf T
    4. Moerch M
    5. Zecha J
    6. Leijten N
    7. Bian Y
    8. Musiol E
    9. Maschberger M
    10. Stoehr G
    11. Becher I
    12. Daly C
    13. Samaras P
    14. Mergner J
    15. Spanier B
    16. Angelov A
    17. Werner T
    18. Bantscheff M
    19. Wilhelm M
    20. Klingenspor M
    21. Lemeer S
    22. Liebl W
    23. Hahne H
    24. Savitski MM
    25. Kuster B

    (2020) Meltome atlas-thermal proteome stability across the tree of life

    Nature Methods 17:495–503.

    https://doi.org/10.1038/s41592-020-0801-4

    • PubMed
    • Google Scholar
33. 1. Johnson FD
    2. Hughes CS
    3. Liu A
    4. Lockwood WW
    5. Morin GB

    (2023) Tandem mass tag-based thermal proteome profiling for the discovery of drug-protein interactions in cancer cells

    STAR Protocols 4:102012.

    https://doi.org/10.1016/j.xpro.2022.102012

    • PubMed
    • Google Scholar
34. 1. Koshland DE

    (1958) Application of a theory of enzyme specificity to protein synthesis

    PNAS 44:98–104.

    https://doi.org/10.1073/pnas.44.2.98

    • PubMed
    • Google Scholar
35. 1. Kothe M
    2. Kohls D
    3. Low S
    4. Coli R
    5. Rennie GR
    6. Feru F
    7. Kuhn C
    8. Ding Y

    (2007) Research article: selectivity‐determining residues in Plk1

    Chemical Biology & Drug Design 70:540–546.

    https://doi.org/10.1111/j.1747-0285.2007.00594.x

    • Google Scholar
36. 1. Kuljanin M
    2. Mitchell DC
    3. Schweppe DK
    4. Gikandi AS
    5. Nusinow DP
    6. Bulloch NJ
    7. Vinogradova EV
    8. Wilson DL
    9. Kool ET
    10. Mancias JD
    11. Cravatt BF
    12. Gygi SP

    (2021) Reimagining high-throughput profiling of reactive cysteines for cell-based screening of large electrophile libraries

    Nature Biotechnology 39:630–641.

    https://doi.org/10.1038/s41587-020-00778-3

    • PubMed
    • Google Scholar
37. 1. Laurien L
    2. Nagata M
    3. Schünke H
    4. Delanghe T
    5. Wiederstein JL
    6. Kumari S
    7. Schwarzer R
    8. Corona T
    9. Krüger M
    10. Bertrand MJM
    11. Kondylis V
    12. Pasparakis M

    (2020) Autophosphorylation at serine 166 regulates RIP kinase 1-mediated cell death and inflammation

    Nature Communications 11:1747.

    https://doi.org/10.1038/s41467-020-15466-8

    • PubMed
    • Google Scholar
38. 1. Li J
    2. Van Vranken JG
    3. Paulo JA
    4. Huttlin EL
    5. Gygi SP

    (2020) Selection of heating temperatures improves the sensitivity of the proteome integral solubility alteration assay

    Journal of Proteome Research 19:2159–2166.

    https://doi.org/10.1021/acs.jproteome.0c00063

    • PubMed
    • Google Scholar
39. 1. Liang YY
    2. Bacanu S
    3. Sreekumar L
    4. Ramos AD
    5. Dai L
    6. Michaelis M
    7. Cinatl J
    8. Seki T
    9. Cao Y
    10. Coffill CR
    11. Lane DP
    12. Prabhu N
    13. Nordlund P

    (2022) CETSA interaction proteomics define specific RNA-modification pathways as key components of fluorouracil-based cancer drug cytotoxicity

    Cell Chemical Biology 29:572–585.

    https://doi.org/10.1016/j.chembiol.2021.06.007

    • PubMed
    • Google Scholar
40. 1. Lima K
    2. Pereira-Martins DA
    3. de Miranda LBL
    4. Coelho-Silva JL
    5. Leandro G
    6. Weinhäuser I
    7. Cavaglieri R
    8. Leal A
    9. da Silva WF
    10. Lange A
    11. Velloso E
    12. Griessinger E
    13. Hilberink JR
    14. Ammatuna E
    15. Huls G
    16. Schuringa JJ
    17. Rego EM
    18. Machado-Neto JA

    (2022) The PIP4K2 inhibitor THZ-P1-2 exhibits antileukemia activity by disruption of mitochondrial homeostasis and autophagy

    Blood Cancer Journal 12:151.

    https://doi.org/10.1038/s41408-022-00747-w

    • PubMed
    • Google Scholar
41. 1. Liu F
    2. Meng H
    3. Fitzgerald MC

    (2017) Large-scale analysis of breast cancer-related conformational changes in proteins using silac-sprox

    Journal of Proteome Research 16:3277–3286.

    https://doi.org/10.1021/acs.jproteome.7b00283

    • PubMed
    • Google Scholar
42. 1. Lomenick B
    2. Hao R
    3. Jonai N
    4. Chin RM
    5. Aghajan M
    6. Warburton S
    7. Wang J
    8. Wu RP
    9. Gomez F
    10. Loo JA
    11. Wohlschlegel JA
    12. Vondriska TM
    13. Pelletier J
    14. Herschman HR
    15. Clardy J
    16. Clarke CF
    17. Huang J

    (2009) Target identification using drug affinity responsive target stability (DARTS)

    PNAS 106:21984–21989.

    https://doi.org/10.1073/pnas.0910040106

    • PubMed
    • Google Scholar
43. 1. Martens S
    2. Goossens V
    3. Devisscher L
    4. Hofmans S
    5. Claeys P
    6. Vuylsteke M
    7. Takahashi N
    8. Augustyns K
    9. Vandenabeele P

    (2018) RIPK1-dependent cell death: a novel target of the aurora kinase inhibitor tozasertib (VX-680)

    Cell Death & Disease 9:211.

    https://doi.org/10.1038/s41419-017-0245-7

    • PubMed
    • Google Scholar
44. 1. Mateus A
    2. Määttä TA
    3. Savitski MM

    (2016) Thermal proteome profiling: unbiased assessment of protein state through heat-induced stability changes

    Proteome Science 15:13.

    https://doi.org/10.1186/s12953-017-0122-4

    • PubMed
    • Google Scholar
45. 1. Mateus A
    2. Kurzawa N
    3. Becher I
    4. Sridharan S
    5. Helm D
    6. Stein F
    7. Typas A
    8. Savitski MM

    (2020) Thermal proteome profiling for interrogating protein interactions

    Molecular Systems Biology 16:e9232.

    https://doi.org/10.15252/msb.20199232

    • PubMed
    • Google Scholar
46. 1. Meijer L
    2. Skaltsounis AL
    3. Magiatis P
    4. Polychronopoulos P
    5. Knockaert M
    6. Leost M
    7. Ryan XP
    8. Vonica CA
    9. Brivanlou A
    10. Dajani R
    11. Crovace C
    12. Tarricone C
    13. Musacchio A
    14. Roe SM
    15. Pearl L
    16. Greengard P

    (2003) GSK-3-selective inhibitors derived from tyrian purple indirubins

    Chemistry & Biology 10:1255–1266.

    https://doi.org/10.1016/j.chembiol.2003.11.010

    • Google Scholar
47. 1. Meissner F
    2. Geddes-McAlister J
    3. Mann M
    4. Bantscheff M

    (2022) The emerging role of mass spectrometry-based proteomics in drug discovery

    Nature Reviews. Drug Discovery 21:637–654.

    https://doi.org/10.1038/s41573-022-00409-3

    • PubMed
    • Google Scholar
48. 1. Mitchell DC
    2. Kuljanin M
    3. Li J
    4. Van Vranken JG
    5. Bulloch N
    6. Schweppe DK
    7. Huttlin EL
    8. Gygi SP

    (2023) A proteome-wide atlas of drug mechanism of action

    Nature Biotechnology 41:845–857.

    https://doi.org/10.1038/s41587-022-01539-0

    • PubMed
    • Google Scholar
49. 1. Moellering RE
    2. Cravatt BF

    (2012) How chemoproteomics can enable drug discovery and development

    Chemistry & Biology 19:11–22.

    https://doi.org/10.1016/j.chembiol.2012.01.001

    • PubMed
    • Google Scholar
50. 1. Molina DM
    2. Jafari R
    3. Ignatushchenko M
    4. Seki T
    5. Larsson EA
    6. Dan C
    7. Sreekumar L
    8. Cao Y
    9. Nordlund P

    (2013) Monitoring drug target engagement in cells and tissues using the cellular thermal shift assay

    Science 341:84–87.

    https://doi.org/10.1126/science.1233606

    • Google Scholar
51. 1. Narasimha AM
    2. Kaulich M
    3. Shapiro GS
    4. Choi YJ
    5. Sicinski P
    6. Dowdy SF

    (2014) Cyclin D activates the Rb tumor suppressor by mono-phosphorylation

    eLife 3:e02872.

    https://doi.org/10.7554/eLife.02872

    • PubMed
    • Google Scholar
52. 1. Perrin J
    2. Werner T
    3. Kurzawa N
    4. Rutkowska A
    5. Childs DD
    6. Kalxdorf M
    7. Poeckel D
    8. Stonehouse E
    9. Strohmer K
    10. Heller B
    11. Thomson DW
    12. Krause J
    13. Becher I
    14. Eberl HC
    15. Vappiani J
    16. Sevin DC
    17. Rau CE
    18. Franken H
    19. Huber W
    20. Faelth-Savitski M
    21. Savitski MM
    22. Bantscheff M
    23. Bergamini G

    (2020) Identifying drug targets in tissues and whole blood with thermal-shift profiling

    Nature Biotechnology 38:303–308.

    https://doi.org/10.1038/s41587-019-0388-4

    • PubMed
    • Google Scholar
53. 1. Rad R
    2. Li J
    3. Mintseris J
    4. O’Connell J
    5. Gygi SP
    6. Schweppe DK

    (2021) Improved monoisotopic mass estimation for deeper proteome coverage

    Journal of Proteome Research 20:591–598.

    https://doi.org/10.1021/acs.jproteome.0c00563

    • PubMed
    • Google Scholar
54. 1. Reinhard FBM
    2. Eberhard D
    3. Werner T
    4. Franken H
    5. Childs D
    6. Doce C
    7. Savitski MF
    8. Huber W
    9. Bantscheff M
    10. Savitski MM
    11. Drewes G

    (2015) Thermal proteome profiling monitors ligand interactions with cellular membrane proteins

    Nature Methods 12:1129–1131.

    https://doi.org/10.1038/nmeth.3652

    • PubMed
    • Google Scholar
55. 1. Robers MB
    2. Friedman-Ohana R
    3. Huber KVM
    4. Kilpatrick L
    5. Vasta JD
    6. Berger BT
    7. Chaudhry C
    8. Hill S
    9. Müller S
    10. Knapp S
    11. Wood KV

    (2020) Quantifying Target Occupancy of Small Molecules Within Living Cells

    Annual Review of Biochemistry 89:557–581.

    https://doi.org/10.1146/annurev-biochem-011420-092302

    • PubMed
    • Google Scholar
56. 1. Rojas-Rivera D
    2. Delvaeye T
    3. Roelandt R
    4. Nerinckx W
    5. Augustyns K
    6. Vandenabeele P
    7. Bertrand MJM

    (2017) When PERK inhibitors turn out to be new potent RIPK1 inhibitors: critical issues on the specificity and use of GSK2606414 and GSK2656157

    Cell Death and Differentiation 24:1100–1110.

    https://doi.org/10.1038/cdd.2017.58

    • PubMed
    • Google Scholar
57. 1. Sabatier P
    2. Beusch CM
    3. Saei AA
    4. Aoun M
    5. Moruzzi N
    6. Coelho A
    7. Leijten N
    8. Nordenskjöld M
    9. Micke P
    10. Maltseva D
    11. Tonevitsky AG
    12. Millischer V
    13. Carlos Villaescusa J
    14. Kadekar S
    15. Gaetani M
    16. Altynbekova K
    17. Kel A
    18. Berggren PO
    19. Simonson O
    20. Grinnemo KH
    21. Holmdahl R
    22. Rodin S
    23. Zubarev RA

    (2021) An integrative proteomics method identifies a regulator of translation during stem cell maintenance and differentiation

    Nature Communications 12:6558.

    https://doi.org/10.1038/s41467-021-26879-4

    • PubMed
    • Google Scholar
58. 1. Sabatier P
    2. Beusch CM
    3. Meng Z
    4. Zubarev RA

    (2022) System-wide profiling by proteome integral solubility alteration assay of drug residence times for target characterization

    Analytical Chemistry 94:15772–15780.

    https://doi.org/10.1021/acs.analchem.2c03506

    • PubMed
    • Google Scholar
59. 1. Savitski MM
    2. Reinhard FBM
    3. Franken H
    4. Werner T
    5. Savitski MF
    6. Eberhard D
    7. Martinez Molina D
    8. Jafari R
    9. Dovega RB
    10. Klaeger S
    11. Kuster B
    12. Nordlund P
    13. Bantscheff M
    14. Drewes G

    (2014) Tracking cancer drugs in living cells by thermal profiling of the proteome

    Science 346:1255784.

    https://doi.org/10.1126/science.1255784

    • PubMed
    • Google Scholar
60. 1. Savitski MM
    2. Wilhelm M
    3. Hahne H
    4. Kuster B
    5. Bantscheff M

    (2015) A scalable approach for protein false discovery rate estimation in large proteomic data sets

    Molecular & Cellular Proteomics 14:2394–2404.

    https://doi.org/10.1074/mcp.M114.046995

    • PubMed
    • Google Scholar
61. 1. Savitski MM
    2. Zinn N
    3. Faelth-Savitski M
    4. Poeckel D
    5. Gade S
    6. Becher I
    7. Muelbaier M
    8. Wagner AJ
    9. Strohmer K
    10. Werner T
    11. Melchert S
    12. Petretich M
    13. Rutkowska A
    14. Vappiani J
    15. Franken H
    16. Steidel M
    17. Sweetman GM
    18. Gilan O
    19. Lam EYN
    20. Dawson MA
    21. Prinjha RK
    22. Grandi P
    23. Bergamini G
    24. Bantscheff M

    (2018) Multiplexed proteome dynamics profiling reveals mechanisms controlling protein homeostasis

    Cell 173:260–274.

    https://doi.org/10.1016/j.cell.2018.02.030

    • PubMed
    • Google Scholar
62. 1. Schürmann M
    2. Janning P
    3. Ziegler S
    4. Waldmann H

    (2016) Small-molecule target engagement in cells

    Cell Chemical Biology 23:435–441.

    https://doi.org/10.1016/j.chembiol.2016.03.008

    • PubMed
    • Google Scholar
63. 1. Schweppe DK
    2. Prasad S
    3. Belford MW
    4. Navarrete-Perea J
    5. Bailey DJ
    6. Huguet R
    7. Jedrychowski MP
    8. Rad R
    9. McAlister G
    10. Abbatiello SE
    11. Woulters ER
    12. Zabrouskov V
    13. Dunyach JJ
    14. Paulo JA
    15. Gygi SP

    (2019) Characterization and optimization of multiplexed quantitative analyses using high-field asymmetric-waveform ion mobility mass spectrometry

    Analytical Chemistry 91:4010–4016.

    https://doi.org/10.1021/acs.analchem.8b05399

    • PubMed
    • Google Scholar
64. 1. Shi Y
    2. Bray W
    3. Smith AJ
    4. Zhou W
    5. Calaoagan J
    6. Lagisetti C
    7. Sambucetti L
    8. Crews P
    9. Lokey RS
    10. Webb TR

    (2020) An exon skipping screen identifies antitumor drugs that are potent modulators of pre-mRNA splicing, suggesting new therapeutic applications

    PLOS ONE 15:e0233672.

    https://doi.org/10.1371/journal.pone.0233672

    • PubMed
    • Google Scholar
65. Book

    1. Sridharan S
    2. Günthner I
    3. Becher I
    4. Savitski M
    5. Bantscheff M

    (2019a) Target discovery using thermal proteome profiling

    In: Tao WA, Zhang Y, editors. Mass Spectrometry‐Based Chemical Proteomics. PMC. pp. 267–291.

    https://doi.org/10.1002/9781118970195

    • Google Scholar
66. 1. Sridharan S
    2. Kurzawa N
    3. Werner T
    4. Günthner I
    5. Helm D
    6. Huber W
    7. Bantscheff M
    8. Savitski MM

    (2019b) Proteome-wide solubility and thermal stability profiling reveals distinct regulatory roles for ATP

    Nature Communications 10:1155.

    https://doi.org/10.1038/s41467-019-09107-y

    • PubMed
    • Google Scholar
67. 1. Tyanova S
    2. Temu T
    3. Sinitcyn P
    4. Carlson A
    5. Hein MY
    6. Geiger T
    7. Mann M
    8. Cox J

    (2016) The Perseus computational platform for comprehensive analysis of (prote)omics data

    Nature Methods 13:731–740.

    https://doi.org/10.1038/nmeth.3901

    • PubMed
    • Google Scholar
68. 1. Van Vranken JG
    2. Li J
    3. Mitchell DC
    4. Navarrete-Perea J
    5. Gygi SP

    (2021) Assessing target engagement using proteome-wide solvent shift assays

    eLife 10:e70784.

    https://doi.org/10.7554/eLife.70784

    • PubMed
    • Google Scholar
69. 1. Yap TA
    2. Walton MI
    3. Hunter L-JK
    4. Valenti M
    5. de Haven Brandon A
    6. Eve PD
    7. Ruddle R
    8. Heaton SP
    9. Henley A
    10. Pickard L
    11. Vijayaraghavan G
    12. Caldwell JJ
    13. Thompson NT
    14. Aherne W
    15. Raynaud FI
    16. Eccles SA
    17. Workman P
    18. Collins I
    19. Garrett MD

    (2011) Preclinical pharmacology, antitumor activity, and development of pharmacodynamic markers for the novel, potent AKT inhibitor CCT128930

    Molecular Cancer Therapeutics 10:360–371.

    https://doi.org/10.1158/1535-7163.MCT-10-0760

    • PubMed
    • Google Scholar
70. 1. Zinn N
    2. Werner T
    3. Doce C
    4. Mathieson T
    5. Boecker C
    6. Sweetman G
    7. Fufezan C
    8. Bantscheff M

    (2021) Improved proteomics-based drug mechanism-of-action studies using 16-plex isobaric mass tags

    Journal of Proteome Research 20:1792–1801.

    https://doi.org/10.1021/acs.jproteome.0c00900

    • PubMed
    • Google Scholar

Author details

1. Jonathan G Van Vranken

   Department of Cell Biology, Harvard Medical School, Boston, United States

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8931-852X

2. Jiaming Li

   Department of Cell Biology, Harvard Medical School, Boston, United States

   Contribution

   Data curation, Software, Formal analysis, Methodology

   Competing interests

   No competing interests declared

3. Julian Mintseris

   Department of Cell Biology, Harvard Medical School, Boston, United States

   Contribution

   Data curation, Formal analysis

   Competing interests

   No competing interests declared

4. Ting-Yu Wei

   Department of Cell Biology, Harvard Medical School, Boston, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

5. Catherine M Sniezek

   Department of Genome Sciences, University of Washington, Seattle, United States

   Contribution

   Validation, Investigation, Visualization

   Competing interests

   No competing interests declared

6. Meagan Gadzuk-Shea

   Department of Genome Sciences, University of Washington, Seattle, United States

   Contribution

   Conceptualization, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

7. Steven P Gygi

   Department of Cell Biology, Harvard Medical School, Boston, United States

   Contribution

   Conceptualization, Supervision, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   steven_gygi@hms.harvard.edu

   Competing interests

   SPG is on the advisory board for ThermoFisher Scientific, Cedilla Therapeutics, Casma Therapeutics, Cell Signaling Technology and Frontier Medicines

   "This ORCID iD identifies the author of this article:" 0000-0001-7626-0034

8. Devin K Schweppe

   Department of Genome Sciences, University of Washington, Seattle, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   dkschwep@uw.edu

   Competing interests

   DKS is a consultant and/or collaborator with ThermoFisher Scientific, AI Proteins, Genentech, and Matchpoint Therapeutics

   "This ORCID iD identifies the author of this article:" 0000-0002-3241-6276

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