Complex Proteomes Research Articles

Chemical Labeling of Protein 4'-Phosphopantetheinylation in Surfactin-Producing Nonribosomal Peptide Synthetases.

4'-Phosphopantetheinylation is an essential posttranslational modification of the primary and secondary metabolic pathways in prokaryotes and eukaryotes. Several peptide-based natural products are biosynthesized by large, multifunctional enzymes known as nonribosomal peptide synthetases (NRPSs), responsible for producing virulence factors and many pharmaceuticals. The thiolation (T) domain serves as a covalent tether for substrates and intermediates in nonribosomal peptide biosynthesis and must be posttranslationally modified with a 4'-phosphopantetheinyl group. To detect 4'-phosphopantetheinylation of NRPS in bacterial proteomes, we developed a 5'-(vinylsulfonylaminodeoxy)adenosine scaffold with a clickable functionality, enabling effective chemical labeling of 4'-phosphopantethylated NRPSs. In this chapter, we describe the design and synthesis of an activity-based protein profiling probe and summarize our work toward developing a series of protocols for the labeling and visualization of 4'-phosphopantetheinylation of endogenous NRPSs in complex proteomes.

Protein ubiquitination in plant peroxisomes.

Protein ubiquitination regulates diverse cellular processes in eukaryotic organisms, from growth and development to stress response. Proteins subjected to ubiquitination can be found in virtually all subcellular locations and organelles, including peroxisomes, single-membrane and highly dynamic organelles ubiquitous in eukaryotes. Peroxisomes contain metabolic functions essential to plants and animals such as lipid catabolism, detoxification of reactive oxygen species (ROS), biosynthesis of vital hormones and cofactors, and photorespiration. Plant peroxisomes possess a complex proteome with functions varying among different tissue types and developmental stages, and during plant response to distinct environmental cues. However, how these diverse functions are regulated at the post-translational level is poorly understood, especially in plants. In this review, we summarized current knowledge of the involvement of protein ubiquitination in peroxisome protein import, remodeling, pexophagy, and metabolism, focusing on plants, and referencing discoveries from other eukaryotic systems when relevant. Based on previous ubiquitinomics studies, we compiled a list of 56 ubiquitinated Arabidopsis peroxisomal proteins whose functions are associated with all the major plant peroxisomal metabolic pathways. This discovery suggests a broad impact of protein ubiquitination on plant peroxisome functions, therefore substantiating the need to investigate this significant regulatory mechanism in peroxisomes at more depths.

Strategies to Enrich, Identify, and Characterize Glycoproteome in Blood Plasma Using Liquid Chromatography High-Resolution Mass Spectrometry.

Blood and blood-derived components such as plasma and serum are considered as excellent resources that can be utilized to understand the biology of higher eukaryotic organisms including human beings. In research and clinical studies, blood plasma and serum are used to monitor health conditions, progression, and severity of diseases. Many of the disease-related studies utilize plasma and serum due to their disease relevance and accessibility as they can be collected from patients and healthy donors through minimally invasive approaches. Despite its significance, the unique proteome composition, complexity, wide dynamic range, and heterogeneity of the plasma proteins highlight critical factors that challenge the existing analytical technologies. Depletion of high abundant proteins is one among the accepted methods that can simplify the plasma proteome complexity; however, collateral loss of critical proteins should be anticipated. Selective protein enrichment seems to be a better alternative to depletion. Glycosylation of proteins is a dominant posttranslational modification known for its biological as well as diagnostic and therapeutic potential. Most of the reported therapeutic targets for diagnosis and monitoring are found to be glycosylated. In this chapter, a protocol for selective and reproducible enrichment of glycoproteins from blood plasma followed by identification through liquid chromatography high-resolution mass spectrometry has been documented.

Peptide- and Protein-Level Combined Strategy for Analyzing Newly Synthesized Proteins by Integrating Tandem Orthogonal Proteolysis with Cleavable Bioorthogonal Tagging.

A newly synthesized proteome reflects perturbations sensitively and maintains homeostasis in cells. To investigate the low abundant newly synthesized proteins (NSPs) from a complex background proteome, an enrichment process with high selectivity and reliability is essential. Here, we have developed a strategy to realize comprehensive analysis of NSPs by integrating tandem orthogonal proteolysis (TOP) with cleavable bioorthogonal tagging (CBOT) called TOP-CBOT. A solid-phase-conjugated probe with a clickable moiety and a protease-cleavable site was designed, which allowed NSPs to be covalently captured along with tandem release by trypsin and orthogonal tobacco etch virus (TEV) protease. Our method has integrated the advantages of protein-level and peptide-level enrichment. Trypsin digests larger number of peptides from the recovered proteins for NSPs identification and quantification, while the specific tag-contained peptides from TEV data set enabled further NSPs confirmation. Integrating information from two complementary data sets, the reliability in NSPs identification and quantitation were remarkably enhanced. A total of 3699 proteins were recovered in the trypsin data set. Additionally, 1931 proteins were confirmed as NSPs with 5019 identified peptides in the TEV data set, over 90% of which were overlapped with the tryptic data set. Our strategy was further applied to profile NSP degradation kinetics during rapamycin-induced macroautophagy. The newly synthesized proteome displayed varied alteration of degradation rates among stimulation and more than half of NSPs showed decreased half-lives during autophagy.

An Optimized Proteomics Approach Reveals Novel Alternative Proteins in Mouse Liver Development

Alternative ORFs (AltORFs) are unannotated sequences in genome that encode novel peptides or proteins named alternative proteins (AltProts). Although ribosome profiling and bioinformatics predict a large number of AltProts, mass spectrometry as the only direct way of identification is hampered by the short lengths and relative low abundance of AltProts. There is an urgent need for improvement of mass spectrometry methodologies for AltProt identification. Here, we report an approach based on size-exclusion chromatography for simultaneous enrichment and fractionation of AltProts from complex proteome. This method greatly simplifies the variance of AltProts discovery by enriching small proteins smaller than 40 kDa. In a systematic comparison between 10 methods, the approach we reported enabled the discovery of more AltProts with overall higher intensities, with less cost of time and effort compared to other workflows. We applied this approach to identify 89 novel AltProts from mouse liver, 39 of which were differentially expressed between embryonic and adult mice. During embryonic development, the upregulated AltProts were mainly involved in biological pathways on RNA splicing and processing, whereas the AltProts involved in metabolisms were more active in adult livers. Our study not only provides an effective approach for identifying AltProts but also novel AltProts that are potentially important in developmental biology.

Deep Learning Based MS2 Feature Detection for Data-Independent Shotgun Proteomics.

Accuracy of peptide identification in LC-MS analysis is crucial for information regarding the aspects of proteins that aid in biomarker discovery and the profiling of complex proteomes. The detection of peptide fragment ions in tandem mass spectrometry is still challenging given that current tools were not created or tested for the low-abundance, low-peak fragments of peptides found in MS2 data. Feature detection, a crucial pre-processing step in the LC-MS analysis pipeline that quantifies peptides by their mass-to-charge ratio, retention time, and intensity, is particularly challenging due to the overlapping nature of peptides and weak signals that are often indistinguishable from noises, thus creating a reliance on rigid mathematical structures and heuristics. In this study, we developed a deep-learning-based model with an innovative sliding window process that enables high-resolution processing of quantitative MS/MS data to conduct MS2 feature detection. Experimental results show that our model can produce more accurate values and identifications than existing feature detection tools, as well as a high rate of true positive features quantified. Therefore, we believe that our model illustrates the advantages of deep learning techniques applied towards computational proteomics.

Application of Machine Learning in Spatial Proteomics.

Spatial proteomics is an interdisciplinary field that investigates the localization and dynamics of proteins, and it has gained extensive attention in recent years, especially the subcellular proteomics. Numerous evidence indicate that the subcellular localization of proteins is associated with various cellular processes and disease progression. Mass spectrometry (MS)-based and imaging-based experimental approaches have been developed to acquire large-scale spatial proteomic data. To allow the reliable analysis of increasingly complex spatial proteomics data, machine learning (ML) methods have been widely used in both MS-based and imaging-based spatial proteomic data analysis pipelines. Here, we comprehensively survey the applications of ML in spatial proteomics from following aspects: (1) data resources for spatial proteome are comprehensively introduced; (2) the roles of different ML algorithms in data analysis pipelines are elaborated; (3) successful applications of spatial proteomics and several analytical tools integrating ML methods are presented; (4) challenges existing in modern ML-based spatial proteomics studies are discussed. This review provides guidelines for researchers seeking to apply ML methods to analyze spatial proteomic data and can facilitate insightful understanding of cell biology as well as the future research in medical and drug discovery communities.

SRSF10 is essential for progenitor spermatogonia expansion by regulating alternative splicing.

Alternative splicing expands the transcriptome and proteome complexity and plays essential roles in tissue development and human diseases. However, how alternative splicing regulates spermatogenesis remains largely unknown. Here, using a germ cell-specific knockout mouse model, we demonstrated that the splicing factor Srsf10 is essential for spermatogenesis and male fertility. In the absence of SRSF10, spermatogonial stem cells can be formed, but the expansion of Promyelocytic Leukemia Zinc Finger (PLZF)-positive undifferentiated progenitors was impaired, followed by the failure of spermatogonia differentiation (marked by KIT expression) and meiosis initiation. This was further evidenced by the decreased expression of progenitor cell markers in bulk RNA-seq, and much less progenitor and differentiating spermatogonia in single-cell RNA-seq data. Notably, SRSF10 directly binds thousands of genes in isolated THY+ spermatogonia, and Srsf10 depletion disturbed the alternative splicing of genes that are preferentially associated with germ cell development, cell cycle, and chromosome segregation, including Nasp, Bclaf1, Rif1, Dazl, Kit, Ret, and Sycp1. These data suggest that SRSF10 is critical for the expansion of undifferentiated progenitors by regulating alternative splicing, expanding our understanding of the mechanism underlying spermatogenesis.

Differential alternative splicing genes and isoform co-expression networks of Brassica napus under multiple abiotic stresses.

Alternative splicing (AS) is an important regulatory process that affects plant development and stress responses by greatly increasing the complexity of transcriptome and proteome. To understand how the AS landscape of B. napus changes in response to abiotic stresses, we investigated 26 RNA-seq libraries, including control and treatments with cold, dehydration, salt, and abscisic acid (ABA) at two different time points, to perform comparative alternative splicing analysis. Apparently, AS events increased under all stresses except dehydration for 1 h, and intron retention was the most common AS mode. In addition, a total of 357 differential alternative splicing (DAS) genes were identified under four abiotic stresses, among which 81 DAS genes existed in at least two stresses, and 276 DAS genes were presented under only one stress. A weighted gene co-expression network analysis (WGCNA) based on the splicing isoforms, rather than the genes, pinpointed out 23 co-expression modules associated with different abiotic stresses. Among them, a number of significant hub genes were also found to be DAS genes, which encode key isoforms involved in responses to single stress or multiple stresses, including RNA-binding proteins, transcription factors, and other important genes, such as RBP45C, LHY, MYB59, SCL30A, RS40, MAJ23.10, and DWF4. The splicing isoforms of candidate genes identified in this study could be a valuable resource for improving tolerance of B. napus against multiple abiotic stresses.

323 (PB103) - LiP-MS, a machine learning-based chemoproteomic approach to identify drug targets in complex proteomes

Background: Target identification is a critical step in elucidating the mechanism of action (MoA) for bioactive compounds. For target-based and phenotypic drug discovery pipelines, extensive potential target knowledge for a lead compound provides essential insights that enable potency and selectivity optimization. The tedious process of target deconvolution for a compound often necessitates a plethora of biochemical and biophysical techniques. To expand the toolbox of unbiased target ID approaches, we developed a drug target deconvolution workflow combining limited proteolysis with quantitative mass spectrometry (LiP-MS) that exploits the drug binding phenomena of protein structural alterations and steric hindrance.

Maximizing Cumulative Trypsin Activity with Calcium at Elevated Temperature for Enhanced Bottom-Up Proteome Analysis.

Simple SummaryTrypsin is frequently employed to cleave proteins ahead of mass spectrometry characterization. Traditionally, enzyme digestion involves overnight incubation of proteins at 37 °C, which is time consuming though still may yield poor digestion efficiency. While raising the temperature should theoretically accelerate the digestion, it also destabilizes the enzyme and promotes trypsin de-activation. We therefore questioned whether elevated temperature is beneficial for improving tryptic digestion. Here, we quantify protein digestion kinetics at elevated temperatures for calcium-stabilized trypsin and enforce the critical importance of calcium ions to preserve the enzyme. We quantitatively demonstrate that 1 h at 47 °C provides a superior digest when compared to conventional (overnight, 37 °C) processing of the proteome. The practical impact of our enhanced digestion protocol is shown through bottom-up mass spectrometry analysis of a complex proteome mixture.Bottom-up proteomics relies on efficient trypsin digestion ahead of MS analysis. Prior studies have suggested digestion at elevated temperature to accelerate proteolysis, showing an increase in the number of MS-identified peptides. However, improved sequence coverage may be a consequence of partial digestion, as higher temperatures destabilize and degrade the enzyme, causing enhanced activity to be short-lived. Here, we use a spectroscopic (BAEE) assay to quantify calcium-stabilized trypsin activity over the complete time course of a digestion. At 47 °C, the addition of calcium contributes a 25-fold enhancement in trypsin stability. Higher temperatures show a net decrease in cumulative trypsin activity. Through bottom-up MS analysis of a yeast proteome extract, we demonstrate that a 1 h digestion at 47 °C with 10 mM Ca2+ provides a 29% increase in the total number of peptide identifications. Simultaneously, the quantitative proportion of peptides with 1 or more missed cleavage sites was diminished in the 47 °C digestion, supporting enhanced digestion efficiency with the 1 h protocol. Trypsin specificity also improves, as seen by a drop in the quantitative abundance of semi-tryptic peptides. Our enhanced digestion protocol improves throughput for bottom-up sample preparation and validates the approach as a robust, low-cost alternative to maximized protein digestion efficiency.

Cell-Type-Specific Profiling of the Arabidopsis thaliana Membrane Protein-Encoding Genes.

Membrane proteins work in large complexes to perceive and transduce external signals and to trigger a cellular response leading to the adaptation of the cells to their environment. Biochemical assays have been extensively used to reveal the interaction between membrane proteins. However, such analyses do not reveal the unique and complex composition of the membrane proteins of the different plant cell types. Here, we conducted a comprehensive analysis of the expression of Arabidopsis membrane proteins in the different cell types composing the root. Specifically, we analyzed the expression of genes encoding membrane proteins interacting in large complexes. We found that the transcriptional profiles of membrane protein-encoding genes differ between Arabidopsis root cell types. This result suggests that different cell types are characterized by specific sets of plasma membrane proteins, which are likely a reflection of their unique biological functions and interactions. To further explore the complexity of the Arabidopsis root cell membrane proteomes, we conducted a co-expression analysis of genes encoding interacting membrane proteins. This study confirmed previously reported interactions between membrane proteins, suggesting that the co-expression of genes at the single cell-type level can be used to support protein network predictions.

Two Tags in One Probe: Combining Fluorescence- and Biotin-based Detection of the Trypanosomal Cysteine Protease Rhodesain.

Rhodesain is the major cysteine protease of the protozoan parasite Trypanosoma brucei and a therapeutic target for sleeping sickness, a fatal neglected tropical disease. We designed, synthesized and characterized a bimodal activity-based probe that binds to and inactivates rhodesain. This probe exhibited an irreversible mode of action and extraordinary potency for the target protease with a kinac /Ki value of 37,000 M-1 s-1 . Two reporter tags, a fluorescent coumarin moiety and a biotin affinity label, were incorporated into the probe and enabled highly sensitive detection of rhodesain in a complex proteome by in-gel fluorescence and on-blot chemiluminescence. Furthermore, the probe was employed for microseparation and quantification of rhodesain and for inhibitor screening using a competition assay. The developed bimodal rhodesain probe represents a new proteomic tool for studying Trypanosoma pathobiochemistry and antitrypanosomal drug discovery.

Roles of minor spliceosome in intron recognition and the convergence with the better understood major spliceosome.

Catalyzed by spliceosomes in the nucleus, RNA splicing removes intronic sequences from precursor RNAs in eukaryotes to generate mature RNA, which also significantly increases proteome complexity and fine-tunes gene expression. Most metazoans have two coexisting spliceosomes; the major spliceosome, which removes >99.5% of introns, and the minor spliceosome, which removes far fewer introns (only 770 at present have been predicted in the human genome). Both spliceosomes are large and dynamic machineries, each consisting of five small nuclear RNAs (snRNAs) and more than 100 proteins. However, the dynamic assembly, catalysis, and protein composition of the minor spliceosome are still poorly understood. With different splicing signals, minor introns are rare and usually distributed alone and flanked by major introns in genes, raising questions of how they are recognized by the minor spliceosome and how their processing deals with the splicing of neighboring major introns. Due to large numbers of introns and close similarities between the two machinery, cooperative, and competitive recognition by the two spliceosomes has been investigated. Functionally, many minor-intron-containing genes are evolutionarily conserved and essential. Mutations in the minor spliceosome exhibit a variety of developmental defects in plants and animals and are linked to numerous human diseases. Here, we review recent progress in the understanding of minor splicing, compare currently known components of the two spliceosomes, survey minor introns in a wide range of organisms, discuss cooperation and competition of the two spliceosomes in splicing of minor-intron-containing genes, and contributions of minor splicing mutations in development and diseases. This article is categorized under: RNA Processing > Processing of Small RNAs RNA Processing > Splicing Mechanisms RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry.

A brief introduction to chemical proteomics for target deconvolution.

Drug-target relationships provide the basis for network-based polypharmacology, and target deconvolution is a key step in phenotypic-screening based drug discovery. Due to the complexity of the mammalian proteomics and the often-limited affinity of the lead compound, it is challenging to identify the drug targets, especially when the goal is to identify all targets. This paper attempts to provide a brief and comprehensive introduction to the various methods in chemical proteomics for target deconvolution by categorizing them into two groups: the biochemical enrichment and the proteomics-screening methods. Moreover, a brief introduction of related Mass Spectrometry techniques is also provided, together with recent progress. The data for this review were queried from Web of Science and PubMed, the keywords used were Drug targets, Target deconvolution, and Chemical Proteomics. A total of over 500 relevant articles, with a time limit from 1953 to 2022, were identified according to search strategy. Duplicate records and review articles were excluded by their titles and abstracts. Finally, we found about 120 articles matching our inclusion criteria, which covered representative research and reviews of various target discovery methods. Existing target discovery methods can be grouped into either biochemical enrichment or the proteomics-screening methods, with the recent emergence of a hybrid method combining these two such as lysine reactivity profiling. The advantage of the biochemical enrichment method is the ease of operation and the comprehensive target coverage. However, most biochemical enrichment methods require a high-affinity binding of the drug to the target proteins and cannot differentiate direct/indirect targets. The proteomics-screening methods do not require drug modification but have limited protein coverage, and most of them cannot differentiate direct/indirect targets. Although existing target discovery methods have greatly facilitated pharmacological research, each of these methods has advantages and disadvantages. New strategies/methods are needed to further improve both the coverage of the proteosome and the specificity.

Deep Learning for MS2 Feature Detection in Liquid Chromatography Mass Spectrometry

Accuracy of peptide identification is crucial for LC-MS analysis to reveal information regarding many different aspects of proteins that aid in the discovery of biomarkers and profiling of complex proteomes. Preprocessing steps such as feature detection are crucial yet challenging; current feature detection tools are not robust enough to detect low-abundance, low-peak fragments of peptides found in MS2 data from tandem mass spectrometry. In this study, we developed a deep learning-based model with an innovative sliding window process that enables high-resolution processing of quantitative MS/MS data to conduct accurate feature detection on MS2 data. Experimental results show that our model is able to produce more accurate values and identifications than existing feature detection tools. Therefore, we believe that our model can realize the full potential of neural networks in the field of bioinformatics and yields long-term benefits in the advancement of proteomic inquiry.

Alternative splicing: An efficient regulatory approach towards plant developmental plasticity.

Alternative splicing (AS) is a gene regulatory mechanism that plants adapt to modulate gene expression (GE) in multiple ways. AS generates alternative isoforms of the same gene following various development and environmental stimuli, increasing transcriptome plasticity and proteome complexity. AS controls the expression levels of certain genes and regulates GE networks that shape plant adaptations through nonsense-mediated decay (NMD). This review intends to discuss AS modulation, from interaction with noncoding RNAs to the established roles of splicing factors (SFs) in response to endogenous and exogenous cues. We aim to gather such studies that highlight the magnitude and impact of AS, which are not always clear from individual articles, when AS is increasing in individual genes and at a global level. This work also anticipates making plant researchers know that AS is likely to occur in their investigations and that dynamic changes in AS and their effects must be frequently considered. We also review our understanding of AS-mediated posttranscriptional modulation of plant stress tolerance and discuss its potential application in crop improvement in the future. This article is categorized under: RNA Processing > Splicing Regulation/Alternative Splicing RNA Processing > Splicing Mechanisms RNA-Based Catalysis > RNA Catalysis in Splicing and Translation.

Histone Marks-Dependent Effect on Alternative Splicing: New Perspectives for Targeted Splicing Modulation in Cancer?

Alternative splicing (AS) is a tightly regulated mechanism that generates the complex human proteome from a small number of genes. Cis-regulatory RNA motifs in exons and introns control AS, recruiting positive and negative trans-acting splicing regulators. At a higher level, chromatin affects splicing events. Growing evidence indicates that the popular histone code hypothesis can be extended to RNA-level processes, such as AS. In addition to nucleosome positioning, which can generate transcriptional barriers to shape the final splicing outcome, histone post-translational modifications can contribute to the detailed regulation of single exon inclusion/exclusion. A histone-based system can identify alternatively spliced chromatin stretches, affecting RNAPII elongation locally or recruiting splicing components via adaptor complexes. In tumor cells, several mechanisms trigger misregulated AS events and produce cancer-associated transcripts. On a genome-wide level, aberrant AS can be the consequence of dysfunctional epigenetic splicing code, including altered enrichment in histone post-translational modifications. This review describes the main findings related to the effect of histone modifications and variants on splicing outcome and how a dysfunctional epigenetic splicing code triggers aberrant AS in cancer. In addition, it highlights recent advances in programmable DNA-targeting technologies and their possible application for AS targeted epigenetic modulation.

Alternative transcription start sites contribute to acute-stress-induced transcriptome response in human skeletal muscle

BackgroundMore than half of human protein-coding genes have an alternative transcription start site (TSS). We aimed to investigate the contribution of alternative TSSs to the acute-stress-induced transcriptome response in human tissue (skeletal muscle) using the cap analysis of gene expression approach. TSSs were examined at baseline and during recovery after acute stress (a cycling exercise).ResultsWe identified 44,680 CAGE TSS clusters (including 3764 first defined) belonging to 12,268 genes and annotated for the first time 290 TSSs belonging to 163 genes. The transcriptome dynamically changes during the first hours after acute stress; the change in the expression of 10% of genes was associated with the activation of alternative TSSs, indicating differential TSSs usage. The majority of the alternative TSSs do not increase proteome complexity suggesting that the function of thousands of alternative TSSs is associated with the fine regulation of mRNA isoform expression from a gene due to the transcription factor-specific activation of various alternative TSSs. We identified individual muscle promoter regions for each TSS using muscle open chromatin data (ATAC-seq and DNase-seq). Then, using the positional weight matrix approach we predicted time course activation of “classic” transcription factors involved in response of skeletal muscle to contractile activity, as well as diversity of less/un-investigated factors.ConclusionsTranscriptome response induced by acute stress related to activation of the alternative TSSs indicates that differential TSSs usage is an essential mechanism of fine regulation of gene response to stress stimulus. A comprehensive resource of accurate TSSs and individual promoter regions for each TSS in muscle was created. This resource together with the positional weight matrix approach can be used to accurate prediction of TFs in any gene(s) of interest involved in the response to various stimuli, interventions or pathological conditions in human skeletal muscle.

A modification-centric assessment tool for the performance of chemoproteomic probes.

Chemoproteomics has emerged as a key technology to expand the functional space in complex proteomes for probing fundamental biology and for discovering new small-molecule-based therapies. Here we report a modification-centric computational tool termed pChem to provide a streamlined pipeline for unbiased performance assessment of chemoproteomic probes. The pipeline starts with an experimental setting for isotopically coding probe-derived modifications that can be automatically recognized by pChem, with masses accurately calculated and sites precisely localized. pChem exports on-demand reports by scoring the profiling efficiency, modification homogeneity and proteome-wide residue selectivity of a tested probe. The performance and robustness of pChem were benchmarked by applying it to eighteen bioorthogonal probes. These analyses reveal that the formation of unexpected probe-derived modifications can be driven by endogenous reactive metabolites (for example, bioactive aldehydes and glutathione). pChem is a powerful and user-friendly tool that aims to facilitate the development of probes for the ever-growing field of chemoproteomics.