Identifying the proteins to which small-molecule probes and drugs bind in cells

Abstract

Most small-molecule probes and drugs alter cell circuitry by interacting with 1 or more proteins. A complete understanding of the interacting proteins and their associated protein complexes, whether the compounds are discovered by cell-based phenotypic or target-based screens, is extremely rare. Such a capability is expected to be highly illuminating--providing strong clues to the mechanisms used by small-molecules to achieve their recognized actions and suggesting potential unrecognized actions. We describe a powerful method combining quantitative proteomics (SILAC) with affinity enrichment to provide unbiased, robust and comprehensive identification of the proteins that bind to small-molecule probes and drugs. The method is scalable and general, requiring little optimization across different compound classes, and has already had a transformative effect on our studies of small-molecule probes. Here, we describe in full detail the application of the method to identify targets of kinase inhibitors and immunophilin binders.

Fig. 1.

Identifying specific SM-protein interactions with quantitative proteomics. (A) SILAC identifies specific protein interactions with SM baits. Cell populations are fully labeled with light (black) and heavy amino acids (red) and lysates incubated either with SM-loaded beads (SM-Beads) and soluble SM competitor or SM-Beads alone. Proteins interacting directly with the SM or via secondary and/or higher order interactions (marked “S” for specific) will be enriched in the heavy state over the light and will be identified with differential ratios. Nonspecific (NS) interactions of proteins will be enriched equally in both states and have ratios close to 1. (B) Experimental mass spectra showing specific protein interactions with the immunophilin ligand, AP1497. (Left) A peptide from FKBP4, a known binding partner to FK506, is observed with a highly differential ratio. (Right) In contrast, a histone H1.3 peptide is identified with a ratio close to 1, indicating no specific binding to the soluble SM competitor.

Fig. 2.

Classifying SM-protein interactions with quantitative ratios. Using Ro-31-7549 BC (A) and SC (B) datasets as examples, shown are modeled ratio data distributions with mixture modeling (Upper) and plots of log₂ SILAC ratios against proteins sorted by their ratios in descending order (Lower). (Upper) Histograms of log₂ SILAC ratios for the BC and SC experiments, using mixtures of t-distributions (each modeled component is drawn in dotted lines) to highlight differences between the 2 experimental designs. Although the distribution of log₂ ratios for SC experiments is tightly centered around zero, log₂ ratio distributions for BC experiments vary considerably and are affected by compound loading levels and wash stringency (Figs. S2). (Lower) Targets of bisindolylmaleimide-type PKC inhibitors identified in other proteomic datasets are highlighted in red (17) and blue (27), and those common to both datasets are in purple. (A) BC experiments compare proteins enriched between SM matrices (heavy) and ethanol loaded beads (light). Ratios help classify proteins into categories of “SM binders,” “control bead binders,” or “undetermined/nonspecific.” (B) SC experiments use SM matrices in pull-downs with both light and heavy lysates. Differential ratios arise through reduction of bead-bound protein in one state by competition with the soluble compound.

Fig. 3.

Identifying significant targets of K252a in soluble competition data. Scatter plot of 2 replicate experiments of K252a 100× SC. Each data point is a single protein with kinases represented as triangles and circles denoting nonkinases. The color scale indicates the number of identified peptides per protein. The contour line demarcates a local FDR of 0.01 and all data points to the top right corner of the plot are inferred targets. Identified targets span a wide range of abundance. (Inset) Expanded view of the null distribution centered about log₂ SILAC ratio of 0.

Fig. 4.

Comparing K252a SC and BC experiments. Box plot of log₂ SILAC ratios for K252a SC (0.025×, 0.25×, 2.5×, 5×, 10×, 50×, and 100×) and BC experiments (two replicates per condition). Protein kinases (26) identified in each experiment are plotted in red, and the total number of kinases are provided in the Table 2.

Fig. 5.

Immunophilin ligand series. (A) Structures of immunophilin ligands and measured K_D values for FKBP1A. (B) Specificity of FKBP proteins for IPL ligands determined by their SILAC ratios. (C) Sequence coverage for FKBP proteins and validation by Western blot analysis. (D) Validation of HSP90-FKBP4 interaction by coimmunoprecipitation and Western blot analysis. Two FKBP5 antibodies failed to coprecipitate any significant amounts of HSP90.

Fig. 6.

MTAP is a protein target for the IPL ligand Pro-AP1780. (A) Validation with Western blot analysis of MTAP in IPL pull-downs and cell lysate. (B) In vitro MTAP activity assay with 3 dose levels of each IPL ligand. All IPL ligands show slight inhibition of MTAP at the highest levels of compound dose (850 μM). Pro-AP1780 shows the largest inhibitory effect on MTAP in comparison with DMSO treated controls.