Assessing target engagement using proteome-wide solvent shift assays
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Curated by eLife
Evaluation Summary:
This manuscript will be of broad interest to readers in the field of proteomics and drug discovery. It describes a potentially robust method for the identification of biological targets of small molecules, a substantial hurdle in drug discovery. The experiments described are rigorous and this manuscript provides a useful template for the broad implementation of this method. One conclusion that needs further support is the one of the complementarity of CPP and TPP (as in "these two approaches share much in common, they remain distinct and likely serve to complement one another").
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
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Abstract
Recent advances in mass spectrometry (MS) have enabled quantitative proteomics to become a powerful tool in the field of drug discovery, especially when applied toward proteome-wide target engagement studies. Similar to temperature gradients, increasing concentrations of organic solvents stimulate unfolding and precipitation of the cellular proteome. This property can be influenced by physical association with ligands and other molecules, making individual proteins more or less susceptible to solvent-induced denaturation. Herein, we report the development of proteome-wide solvent shift assays by combining the principles of solvent-induced precipitation (Zhang et al., 2020) with modern quantitative proteomics. Using this approach, we developed solvent proteome profiling (SPP), which is capable of establishing target engagement through analysis of SPP denaturation curves. We readily identified the specific targets of compounds with known mechanisms of action. As a further efficiency boost, we applied the concept of area under the curve analysis to develop solvent proteome integral solubility alteration (solvent-PISA) and demonstrate that this approach can serve as a reliable surrogate for SPP. We propose that by combining SPP with alternative methods, like thermal proteome profiling, it will be possible to increase the absolute number of high-quality melting curves that are attainable by either approach individually, thereby increasing the fraction of the proteome that can be screened for evidence of ligand binding.
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Author Response:
Reviewer #2 (Public Review):
The authors benchmark their workflow and analyses using fairly well characterized compounds that are relatively potent against established targets. However, the authors appear to use significantly higher concentrations than the reported activity for these inhibitors and observe relatively few stabilized targets. Similarly, the corresponding measured induced-stabilization fold change at these concentrations often appear to be 1.5-2 fold. For example, SCIO-469 has reported in vitro potencies of ~10nM against MAPK14, ~100nM against MAPK11, with ~1000-fold selectivity over other kinases (including other MAPKs), and cell-based IC50s of ~300nM. However, the authors use 100 micromolar of SCIO-469 in their solvent-PISA profiling experiments, where they observe ~2-fold change for MAPK14 and ~1.5 …
Author Response:
Reviewer #2 (Public Review):
The authors benchmark their workflow and analyses using fairly well characterized compounds that are relatively potent against established targets. However, the authors appear to use significantly higher concentrations than the reported activity for these inhibitors and observe relatively few stabilized targets. Similarly, the corresponding measured induced-stabilization fold change at these concentrations often appear to be 1.5-2 fold. For example, SCIO-469 has reported in vitro potencies of ~10nM against MAPK14, ~100nM against MAPK11, with ~1000-fold selectivity over other kinases (including other MAPKs), and cell-based IC50s of ~300nM. However, the authors use 100 micromolar of SCIO-469 in their solvent-PISA profiling experiments, where they observe ~2-fold change for MAPK14 and ~1.5 fold changes for MAPK12 and MAPK9, and MAPK11 does not appear to be detected. This might suggest that solvent-PISA might not be sensitive to detecting stabilization to less-well developed compounds, decreasing its utility to identify targets of bioactive compounds that are less characterized/developed. It would be informative if the authors provided context for the concentrations of the small molecules that they use and provided some assessment of the sensitivity of this approach in regard to required compound potencies/target affinities.
We thank the reviewer for the rigorous assessment of our manuscript. We agree that the concentrations of compounds used in our experiments are much higher than the respective IC50s. Because any thermal or chemical stability measurement represents the average denaturation point of every copy of an individual protein in a proteome, the ability to detect a meaningful curve shift depends on saturating the target with the ligand. This means that the small molecule concentrations generally need to be high. This is even more important in lysate-based experiments, in which the cellular architecture is lost and the proteome is massively diluted. Moreover, the concentrations we employ are in line with similar studies (Zhang et al., Anal. Chem., 2020, 92, 1363-1371, Gaetani et al., J. Proteome Res., 2019, 18, 4027-4037, Savitski et al., 2014, Science, 346). We also agree that overall the fold changes of the PISA assay are relatively small (1.5-2 fold). In solvent-PISA experiments, the magnitude of the fold changes are not only affected by the ligand concentration and the shift in denaturation point, but also the denaturation point of the protein and the concentrations selected. We have investigated the relationship between the fold changes and these factors in the PISA assay in a former paper (J. Proteome Res. 2020, 19, 5, 2159–2166). We showed that the fold changes are inherently small in the classic PISA assay, which is determined by the nature of sigmoidal curves. We also showed that optimization of the selected temperatures ameliorated the issue (J. Proteome Res. 2020, 19, 5, 2159–2166). In this manuscript, we also showed in Figure 4C-E that a careful selection of the concentrations improved the magnitude of the fold changes in the solvent-PISA assay. Importantly, although the fold changes are relatively small (1.5-2 fold), TMT-based multiplexed quantitative proteomics is capable of analyzing both control and treated samples in multiple replicates in one experiment, which minimizes the variance and has robust sensitivity in detecting these fold changes.
Reviewer #3 (Public Review):
This is a highly interesting work providing an alternative method for drug target deconvolution for thermal proteome profiling. The experiments are thoroughly performed, and the conclusions are mostly supported by the obtained data. The only conclusion that needs further support is the one of the complementarity of SPP and TPP (as in "these two approaches share much in common, they remain distinct and likely serve to complement one another").
We appreciate that reviewer raised this point and would be happy to clarify this statement. Commonality – Both approaches rely of protein denaturation to determine target engagement. Furthermore, either approach could be used to screen ~70% of the proteome for ligand-induced changes in thermal- or chemical-stability. Lastly, both approaches follow a similar workflow, take approximately the same amount of time to prep and measure, and in the end generate similar data (Figure 3). Complementarity – We note that there are certain proteins that denature well in only a single condition and that combining the two approaches allows one to cover a greater fraction of the proteome than either approach, individually (Figure 5). Furthermore, the two approaches can also be used to corroborate one another. If one observes a ligand-induced thermal shift, for example, then SPP could be used to provide greater confidence in this hit and vice versa. It seems that our original statement was far too general and made with knowledge of data that had not yet been presented in the manuscript (mainly figure 5). We modified our conclusion in the main text to more accurately reflect the data presented specifically in Figure 3. The new conclusion is stated below and can be found on pages 10 and 11 of the revised manuscript: “Overall these data suggest that while these two approaches are capable of generating a similar set of putative targets, these lists are not completely identical. Therefore, SPP and TPP appear to be complimentary, not only because they can provide independent corroboration but because one method could potentially identify a target that the other might miss.”
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Evaluation Summary:
This manuscript will be of broad interest to readers in the field of proteomics and drug discovery. It describes a potentially robust method for the identification of biological targets of small molecules, a substantial hurdle in drug discovery. The experiments described are rigorous and this manuscript provides a useful template for the broad implementation of this method. One conclusion that needs further support is the one of the complementarity of CPP and TPP (as in "these two approaches share much in common, they remain distinct and likely serve to complement one another").
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
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Reviewer #1 (Public Review):
The manuscript by Van Vranken JG et al describes the development and application of solvent proteome profiling (SPP) and solvent proteome integral solubility alteration (solvent-PISA) to characterize potential protein targets of small molecule drugs. The study was well executed and described, with excellent quantitative analysis of proteomic data, which were pioneered and well-established in the Giyi lab. The application of these methods to drug target identification will have tremendous impact on academic research and pharmaceutical development.
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Reviewer #2 (Public Review):
In the article titled "Assessing target engagement using proteome-wide solvent shift assays," the authors describe a substantial expansion of solvent-induced protein precipitation-coupled MS, as recently reported (SIP, Zhang et al., Anal. Chem., 2020, 92, 1363-1371), to identify protein stabilization events induced by small molecule binding. Specifically, the authors integrate SIP with multiplexed tandem mass tagging to determine full protein melting curves in the presence/absence of small molecule ligands, in a single mass spectrometric experiment, which they term solvent proteome profiling (SPP). Although a simpler for of this SIP had been previously reported, the authors perform rigorous optimization to maximize proteomic coverage of solvent induced precipitated proteins, which includes exploration of …
Reviewer #2 (Public Review):
In the article titled "Assessing target engagement using proteome-wide solvent shift assays," the authors describe a substantial expansion of solvent-induced protein precipitation-coupled MS, as recently reported (SIP, Zhang et al., Anal. Chem., 2020, 92, 1363-1371), to identify protein stabilization events induced by small molecule binding. Specifically, the authors integrate SIP with multiplexed tandem mass tagging to determine full protein melting curves in the presence/absence of small molecule ligands, in a single mass spectrometric experiment, which they term solvent proteome profiling (SPP). Although a simpler for of this SIP had been previously reported, the authors perform rigorous optimization to maximize proteomic coverage of solvent induced precipitated proteins, which includes exploration of various experimental conditions. The authors further extended this approach via incorporation of PISA (Proteome Integral Solubility Alteration) to empirically determine the area under the protein melting curves, significantly increasing sample throughput. The authors benched marked their methods using several characterized inhibitors with well-established targets (e.g. kinase inhibitors, HDAC inhibitors, etc). As the authors recognize, the application of this technique is limited to cell lysates, in contrast to thermal proteome profiling (TPP) which can be performed on intact live cells. However, the authors demonstrate that known small molecule targets that are not identified through TPP can be identified through solvent proteome profiling, and vice versa. Further, they show that a combination of both strategies yields the broadest coverage of detectable ligand-stabilization events. The authors are extremely diligent in their analyses and establish a clear template for broader adoption of this new technique for target identification pursuits.
Overall, the manuscript is well-written and experiments and data analyses are quite rigorous. The conclusions of this paper are, in general, well-supported by the data. However, considering the relative "newness" of this technique, some points need to be further clarified, particularly around the overall sensitivity of this technique to detect ligand-induced stabilization.
The authors benchmark their workflow and analyses using fairly well characterized compounds that are relatively potent against established targets. However, the authors appear to use significantly higher concentrations than the reported activity for these inhibitors and observe relatively few stabilized targets. Similarly, the corresponding measured induced-stabilization fold change at these concentrations often appear to be 1.5-2 fold. For example, SCIO-469 has reported in vitro potencies of ~10nM against MAPK14, ~100nM against MAPK11, with ~1000-fold selectivity over other kinases (including other MAPKs), and cell-based IC50s of ~300nM. However, the authors use 100 micromolar of SCIO-469 in their solvent-PISA profiling experiments, where they observe ~2-fold change for MAPK14 and ~1.5 fold changes for MAPK12 and MAPK9, and MAPK11 does not appear to be detected. This might suggest that solvent-PISA might not be sensitive to detecting stabilization to less-well developed compounds, decreasing its utility to identify targets of bioactive compounds that are less characterized/developed. It would be informative if the authors provided context for the concentrations of the small molecules that they use and provided some assessment of the sensitivity of this approach in regard to required compound potencies/target affinities.
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Reviewer #3 (Public Review):
This is a highly interesting work providing an alternative method for drug target deconvolution for thermal proteome profiling. The experiments are thoroughly performed, and the conclusions are mostly supported by the obtained data. The only conclusion that needs further support is the one of the complementarity of CPP and TPP (as in "these two approaches share much in common, they remain distinct and likely serve to complement one another").
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