Abstract
Targeted covalent inhibitors are powerful entities in drug discovery, but their application has so far mainly been limited to addressing cysteine residues. The development of cysteine-directed covalent inhibitors has largely profited from determining their proteome-wide selectivity using competitive residue-specific proteomics. Several probes have recently been described to monitor other amino acids using this technology and many more electrophiles exist to modify proteins. Nevertheless, a direct, proteome-wide comparison of the selectivity of diverse probes is still entirely missing. Here, we developed a completely unbiased workflow to analyse electrophile selectivity proteome-wide and applied it to directly compare 54 alkyne probes containing diverse reactive groups. In this way, we verified and newly identified probes to monitor a total of nine different amino acids as well as the N-terminus proteome-wide. This selection includes the first probes to globally monitor tryptophans, histidines and arginines as well as novel tailored probes for methionines, aspartates and glutamates.
Supplementary materials
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Supplementary Information Zanon et al.
Description
Supplementary Figures, Supplementary Tables 5-7, Experimental Procedures and NMR Spectra
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Zanon et al. Supplementary Table 1
Description
Supplementary Table 1: Overview of electrophile reactivity. A summary of the key information on masses of modification, amino acid selectivity and quantification for all probes.
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Zanon et al. Supplementary Table 2
Description
Supplementary Table 2: Mass of modification data for all probes. Masses of modification were determined using
an Open Search in MSFragger-based FragPipe.
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Zanon et al. Supplementary Table 3
Description
Supplementary Table 3: Amino acid selectivity data for all probes. Amino acid selectivity was determined using
an Offset Search in MSFragger-based FragPipe.
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Zanon et al. Supplementary Table 4
Description
Supplementary Table 4 | Quantification using all probes. Quantification was mainly performed using MSFragger Closed Search and IonQuant labelling based quantification. Individual data sets are also included that were quantified using MSFragger Offset Search and IonQuant labelling based quantification or using MaxQuant or pFind 3.
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