Quantitative Chemoproteomic Profiling with Data-Independent Acquisition-Based Mass Spectrometry

Abstract

Activity-based protein profiling (ABPP) has emerged as a powerful and versatile tool to enable annotation of protein functions and discovery of targets of bioactive ligands in complex biological systems. It utilizes chemical probes to covalently label functional sites in proteins so that they can be enriched for mass spectrometry (MS)-based quantitative proteomics analysis. However, the semistochastic nature of datadependent acquisition and high cost associated with isotopically encoded quantification reagents compromise the power of ABPP in multidimensional analysis and high-throughput screening, when a large number of samples need to be quantified in parallel. Here, we combine the data-independent acquisition (DIA) MS with ABPP to develop an efficient label-free quantitative chemical proteomic method, DIA-ABPP, with good reproducibility and high accuracy for high-throughput quantification. We demonstrated the power of DIA-ABPP for comprehensive profiling of functional cysteineome in three distinct applications, including dosedependent quantification of cysteines' sensitivity toward a reactive metabolite, screening of ligandable cysteines with a covalent fragment library, and profiling of cysteinome fluctuation in circadian clock cycles. DIA-ABPP will open new opportunities for indepth and multidimensional profiling of functional proteomes and interactions with bioactive small molecules in complex biological systems.

CysDB: A Human Cysteine Database based on Experimental Quantitative Chemoproteomics

“…We prioritized studies that reported high coverage datasets that measured one or more of the following parameters: (1) total number of cysteines identifiable by the pan-cysteine reactive probes, (2) measurement of cysteine intrinsic reactivity towards iodoacetamide alkyne (IAA, 1) and (3) assaying cysteine ligandability (Figure 1A). We collected a total of nine datasets that fulfilled our criteria (Figure 1B for all datasets used) 2,[4][5][6][7][8][9][10][11] .…”

“…With a complete, curated dataset in hand, our next step was to construct the CysDB database and web user interface outlined in Figure 2A. Raw data from prioritized studies 2,[4][5][6][7][8][9][10][11] were pre-processed into a standardized input format for SQL integration (See Table S1 for example data format and required information for future data integration to CysDB). Processed data from these selected sources was ingested and transformed into a database hosted in Google Cloud using MySQL v.8.0.…”

“…Reasons for this gap include protein abundance and restricted expression profiles, location of cysteines in very long or very short tryptic peptides, which are not detected in standard trypsin digests, and unreactive cysteines, such as those buried in the protein core or located in structural disulfides. Despite these technical limitations, the cysteinome continues to grow, with the addition of multiple high coverage new studies in this year alone 6,10,14 .…”

“…(1) We consider identification studies as those aiming to increase coverage of cysteine containing peptides using label free quantification [4][5][6] . (2) Hyperreactivity experiments measure the intrinsic reactivity of cysteines towards highly electrophilic probes [7][8][9][10] , while (3) ligandability experiments measure the intrinsic ligandability or potential 'druggability' of cysteines using libraries of drug-like electrophilic molecules, natural products, and lipid derived electrophiles 2,11,[15][16][17][18][19] . (4) Finally, redox protocols are tailored to identify redox sensitive cysteines 1,[20][21][22][23] .…”

“…Prior analyses have revealed that comparable competition ratios can be calculated using either MS1 or MS2 7 level quantification 2,[4][5]8,[10][11] . Therefore, we opted not to differentiate between samples analyzed using different quantification methods, including isotopic labeling strategy (TMT or isotopically enriched biotinylation reagents) 2,6 , label free quantification and data independent acquisition (DIA) based MS2 level quantification (Figure S2 for general workflow) 4,8,10 . The vast majority (99.3%) of all compounds screened were found to be functionalized with either a chloroacetamide or acrylamide moieties (Figure S3).…”