Fluorogenic sensor platform for the histone code using receptors from dynamic combinatorial libraries

Abstract

Post-translational modifications (PTMs) on histone tails act in diverse combinations in the 'histone code' to control gene expression, with dysregulation observed in a variety of diseases. However, detection and sensing methods are limited, expensive, and/or low-throughput, including MS and antibody based detection. We found that by combining four synthetic receptors developed by dynamic combinatorial chemistry (DCC) in an indicator displacement system, we are able to create a pattern-based sensor platform that can discriminate single PTMs such as methylation and acetylation on a representative histone peptide with 100% accuracy as well as peptides bearing both dimethyl and trimethyl lysine in the presence of arginine methylation, which has not previously been demonstrated, and can even correctly distinguish the position of lysine methylation individually or in the presence of other PTMs. To extend this approach, a full panel of thirteen analytes containing different combinations of PTMs were classified with 96 ± 1% overall accuracy in a 50% left-out analysis, demonstrating the robustness and versatility of the sensor array. Finally, the sensor platform was also used to demonstrate proof of concept for enzymatic assays by analysing the mock reaction of a threonine kinase, successfully identifying analytes representative of substrate conversion both with and without neighboring PTMs. This work provides a rapid platform for the analysis of peptides bearing complex modifications and highlights the utility of receptors discovered though DCC that display variations in binding affinity and selectivity.

Fig. 1. PTMs on the side chains of Lys, Arg, and Thr.

Scheme 1. Schematic representations of (a) indicator displacement assay, (b) sensor array showing a different pattern for each analyte, and (c) statistical output.

Fig. 2. Receptors used in the sensor array for histone PTMs. Each receptor displays a different pattern of inter-action for the methylated forms of lysine and arginine.

Fig. 3. Fluorescence response for each sensor to the given peptide analyte (15 μM). Each sensor utilized a fixed concentration of LCG (1 μM) and receptor (A₂B – 10 μM; A₂D – 5 μM; A₂N – 15 μM; A₂G – 10 μM). Each sensor array was run in 50 mM glycine buffer, pH 9.15 at room temperature. Error bars represent 20 replicates. Fluorescence is normalized to a control sample of LCG (F/F _∞).

Fig. 4. LDA response of the sensor array to five singly modified analytes (20 replicates). Classification accuracy at 100%, jackknife accuracy at 100%.

Fig. 5. LDA response of the sensor array to four peptides bearing multiple methylation PTMs at varying sites. Confidence ellipses at 85%, classification accuracy of 99%, jack-knife test at 98%.

Fig. 6. LDA response of the sensor array to four peptides bearing methylation and phosphorylation. Confidence ellipses at 85%, classification accuracy of 100%, jackknife test at 100%.

Fig. 7. LDA response of the sensor array to all thirteen analytes. Twenty replicates were performed for each analyte, but only the first five are plotted here to simplify the resulting visual output. Observed classification accuracy of 96%, jackknife classification of 95%.

Fig. 8. LDA of the mock enzymatic kinase reaction monitoring conversion of H3 1–12 (Ac-ARTKQTARKSTGY-NH2) to H3 1–12 T11ph (Ac-ARTKQTARKSTphGY-NH2). The substrate and product were both at 15 μM, with 33% conversion at 5 : 10 μM substrate : product and 66% at 10 : 5 μM. Arrows were added to represent the path of phosphorylation, confidence ellipses at 90%.

Fig. 9. LDA of the mock enzymatic kinase reaction monitoring conversion of H3 1–12 K9me3 (Ac-ARTKQTARKme3STGY-NH2) to H3 1–12 K9me3T11ph (Ac-ARTKQTARKme3STphGY-NH2). Arrows were added to represent the path of phosphorylation, confidence ellipses at 90%.