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Abstract Imidazole‐1‐sulfonyl and ‐sulfonate (imidazylate) are widely used in synthetic chemistry as nucleofuges for diazotransfer, nucleophilic substitution, and cross‐coupling reactions. The utility of these reagents for protein bioconjugation, in contrast, have not been comprehensively explored and important considering the prevalence of imidazoles in biomolecules and drugs. Here, we synthesized a series of alkyne‐modified sulfonyl‐ and sulfonate‐imidazole probes to investigate the utility of this electrophile for protein binding. Alkylation of the distal nitrogen activated the nucleofuge capability of the imidazole to produce sulfonyl‐imidazolium electrophiles that were highly reactive but unstable for biological applications. In contrast, arylsulfonyl imidazoles functioned as a tempered electrophile for assessing ligandability of select tyrosine and lysine sites in cell proteomes and when mated to a recognition element could produce targeted covalent inhibitors with reduced off‐target activity. In summary, imidazole nucleofuges show balanced stability and tunability to produce sulfone‐based electrophiles that bind functional tyrosine and lysine sites in the proteome. 

Attention-based models have achieved many remarkable breakthroughs in numerous applications. However, the quadratic complexity of Attention makes the vanilla Attentionbased models hard to apply to long sequence tasks. Various improved Attention structures are proposed to reduce the computation cost by inducing low rankness and approximating the whole sequence by sub-sequences. The most challenging part of those approaches is maintaining the proper balance between information preservation and computation reduction: the longer sub-sequences used, the better information is preserved, but at the price of introducing more noise and computational costs. In this paper, we propose a smoothed skeleton sketching based Attention structure, coined S3Attention, which significantly improves upon the previous attempts to negotiate this trade-off. S3Attention has two mechanisms to effectively minimize the impact of noise while keeping the linear complexity to the sequence length: a smoothing block to mix information over long sequences and a matrix sketching method that simultaneously selects columns and rows from the input matrix. We verify the effectiveness of S3Attention both theoretically and empirically. Extensive studies over Long Range Arena (LRA) datasets and six time-series forecasting show that S3Attention significantly outperforms both vanilla Attention and other state-of-the-art variants of Attention structures. 

Abstract Activity‐based protein profiling (ABPP) is a chemical proteomic method for investigating functional states of proteins in native biological settings. By quantifying changes in probe binding states of active and regulatory protein sites, ABPP reveals functional information on protein regulation and can be configured in competitive settings to determine global selectivity profiles of tool compounds and drugs in lysates, cells, and animals. Chemical probes used for ABPP analyses can target protein families with conserved enzymatic or structural features or can broadly profile the proteome using electrophiles with reactivity towards functional groups on amino acid side chains. The latter approach has provided insights to protein sites involved in allosteric regulation and non‐enzymatic functions. This review introduces quantitative ABPP workflows and discusses electrophilic groups used for ABPP profiling of functional sites in the proteome with an emphasis on tyrosine residues. 

We provide a family-wide assessment of accessible sites for covalent targeting that combined with AlphaFold revealed predicted small molecule binding pockets for guiding future inhibitor development of the DGK superfamily.