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Current drugs to treat African sleeping sickness are inadequate and new therapies are urgently required. As part of a medicinal chemistry programme based upon the simplification of acetogenin-type ether scaffolds, we previously reported the promising trypanocidal activity of compound 1, a bis-tetrahydropyran 1,4-triazole (B-THP-T) inhibitor. This study aims to identify the protein target(s) of this class of compound in Trypanosoma brucei to understand its mode of action and aid further structural optimisation. We used compound 3, a diazirine- and alkyne-containing bi-functional photo-affinity probe analogue of our lead B-THP-T, compound 1, to identify potential targets of our lead compound in the procyclic form T. brucei. Bi-functional compound 3 was UV cross-linked to its target(s) in vivo and biotin affinity or Cy5.5 reporter tags were subsequently appended by Cu(II)-catalysed azide-alkyne cycloaddition. The biotinylated protein adducts were isolated with streptavidin affinity beads and subsequent LC-MSMS identified the FoF1-ATP synthase (mitochondrial complex V) as a potential target. This target identification was confirmed using various different approaches. We show that (i) compound 1 decreases cellular ATP levels (ii) by inhibiting oxidative phosphorylation (iii) at the FoF1-ATP synthase. Furthermore, the use of GFP-PTP-tagged subunits of the FoF1-ATP synthase, shows that our compounds bind specifically to both the α- and β-subunits of the ATP synthase. The FoF1-ATP synthase is a target of our simplified acetogenin-type analogues. This mitochondrial complex is essential in both procyclic and bloodstream forms of T. brucei and its identification as our target will enable further inhibitor optimisation towards future drug discovery. Furthermore, the photo-affinity labeling technique described here can be readily applied to other drugs of unknown targets to identify their modes of action and facilitate more broadly therapeutic drug design in any pathogen or disease model.

A proton-coupled organic cation (H+/OC) antiporter working at the blood–brain barrier (BBB) in humans and rodents is thought to be a promising candidate for the efficient delivery of cationic drugs to the brain. Therefore, it is important to identify the molecular entity that exhibits this activity. Here, for this purpose, we established the Proteomics-based Identification of transporter by Crosslinking substrate in Keyhole (PICK) method, which combines photo-affinity labeling with comprehensive proteomics analysis using SWATH-MS. Using preselected criteria, the PICK method generated sixteen candidate proteins. From these, knockdown screening in hCMEC/D3 cells, an in vitro BBB model, identified two proteins, TM7SF3 and LHFPL6, as candidates for the H+/OC antiporter. We synthesized a novel H+/OC antiporter substrate for functional analysis of TM7SF3 and LHFPL6 in hCMEC/D3 cells and HEK293 cells. The results suggested that both TM7SF3 and LHFPL6 are components of the H+/OC antiporter.

Sclerosteosis is a rare high bone mass disease that is caused by inactivating mutations in the SOST gene. Its gene product, Sclerostin, is a key negative regulator of bone formation and might therefore serve as a target for the anabolic treatment of osteoporosis. The exact molecular mechanism by which Sclerostin exerts its antagonistic effects on Wnt signaling in bone forming osteoblasts remains unclear. Here we show that Wnt3a-induced transcriptional responses and induction of alkaline phosphatase activity, an early marker of osteoblast differentiation, require the Wnt co-receptors LRP5 and LRP6. Unlike Dickkopf1 (DKK1), Sclerostin does not inhibit Wnt-3a-induced phosphorylation of LRP5 at serine 1503 or LRP6 at serine 1490. Affinity labeling of cell surface proteins with [(125)I]Sclerostin identified LRP6 as the main specific Sclerostin receptor in multiple mesenchymal cell lines. When cells were challenged with Sclerostin fused to recombinant green fluorescent protein (GFP) this was internalized, likely via a Clathrin-dependent process, and subsequently degraded in a temperature and proteasome-dependent manner. Ectopic expression of LRP6 greatly enhanced binding and cellular uptake of Sclerostin-GFP, which was reduced by the addition of an excess of non-GFP-fused Sclerostin. Finally, an anti-Sclerostin antibody inhibited the internalization of Sclerostin-GFP and binding of Sclerostin to LRP6. Moreover, this antibody attenuated the antagonistic activity of Sclerostin on canonical Wnt-induced responses.

P2X receptors are ATP-gated ion channels involved in many physiological functions, and determination of ATP-recognition (AR) of P2X receptors will promote the development of new therapeutic agents for pain, inflammation, bladder dysfunction and osteoporosis. Recent crystal structures of the zebrafish P2X4 (zfP2X4) receptor reveal a large ATP-binding pocket (ABP) located at the subunit interface of zfP2X4 receptors, which is occupied by a conspicuous cluster of basic residues to recognize triphosphate moiety of ATP. Using the engineered affinity labeling and molecular modeling, at least three sites (S1, S2 and S3) within ABP have been identified that are able to recognize the adenine ring of ATP, implying the existence of at least three distinct AR modes in ABP. The open crystal structure of zfP2X4 confirms one of three AR modes (named AR1), in which the adenine ring of ATP is buried into site S1 while the triphosphate moiety interacts with clustered basic residues. Why architecture of ABP favors AR1 not the other two AR modes still remains unexplored. Here, we examine the potential role of inherent dynamics of head domain, a domain involved in ABP formation, in AR determinant of P2X4 receptors. In silico docking and binding free energy calculation revealed comparable characters of three distinct AR modes. Inherent dynamics of head domain, especially the downward motion favors the preference of ABP for AR1 rather than AR2 and AR3. Along with the downward motion of head domain, the closing movement of loop139-146 and loop169-183, and structural rearrangements of K70, K72, R298 and R143 enabled ABP to discriminate AR1 from other AR modes. Our observations suggest the essential role of head domain dynamics in determining AR of P2X4 receptors, allowing evaluation of new strategies aimed at developing specific blockers/allosteric modulators by preventing the dynamics of head domain associated with both AR and channel activation of P2X4 receptors.

Isobaric labeling empowers proteome-wide expression measurements simultaneously across multiple samples. Here an expanded set of 16 isobaric reagents based on an isobutyl-proline immonium ion reporter structure (TMTpro) is presented. These reagents have similar characteristics to existing tandem mass tag reagents but with increased fragmentation efficiency and signal. In a proteome-scale example dataset, we compared eight common cell lines with and without Torin1 treatment with three replicates, quantifying more than 8,800 proteins (mean of 7.5 peptides per protein) per replicate with an analysis time of only 1.1 h per proteome. Finally, we modified the thermal stability assay to examine proteome-wide melting shifts after treatment with DMSO, 1 or 20 µM staurosporine with five replicates. This assay identified and dose-stratified staurosporine binding to 228 cellular kinases in just one, 18-h experiment. TMTpro reagents allow complex experimental designs—all with essentially no missing values across the 16 samples and no loss in quantitative integrity. A set of isobaric labeling reagents called TMTpro enables deep quantitative comparisons of proteome measurements across 16 samples.

This protocol describes the use of TurboID and split-TurboID in proximity labeling applications for mapping protein–protein interactions and subcellular proteomes in live mammalian cells. TurboID is an engineered biotin ligase that uses ATP to convert biotin into biotin–AMP, a reactive intermediate that covalently labels proximal proteins. Optimized using directed evolution, TurboID has substantially higher activity than previously described biotin ligase–related proximity labeling methods, such as BioID, enabling higher temporal resolution and broader application in vivo. Split-TurboID consists of two inactive fragments of TurboID that can be reconstituted through protein–protein interactions or organelle–organelle interactions, which can facilitate greater targeting specificity than full-length enzymes alone. Proteins biotinylated by TurboID or split-TurboID are then enriched with streptavidin beads and identified by mass spectrometry. Here, we describe fusion construct design and characterization (variable timing), proteomic sample preparation (5–7 d), mass spectrometric data acquisition (2 d), and proteomic data analysis (1 week). This protocol describes proximity labeling approaches using TurboID and split-TurboID, which can be used for mapping protein–protein interactions and organelle proteomes in live mammalian cells with nanometer spatial resolution.

Protein–protein interactions in cells are rapidly identified with improved proximity labeling methods. Protein interaction networks and protein compartmentalization underlie all signaling and regulatory processes in cells. Enzyme-catalyzed proximity labeling (PL) has emerged as a new approach to study the spatial and interaction characteristics of proteins in living cells. However, current PL methods require over 18 h of labeling time or utilize chemicals with limited cell permeability or high toxicity. We used yeast display-based directed evolution to engineer two promiscuous mutants of biotin ligase, TurboID and miniTurbo, which catalyze PL with much greater efficiency than BioID or BioID2, and enable 10-min PL in cells with non-toxic and easily deliverable biotin. Furthermore, TurboID extends biotin-based PL to flies and worms.

Many biological processes are executed and regulated through the molecular interactions of proteins and nucleic acids. Proximity labeling (PL) is a technology for tagging the endogenous interaction partners of specific protein ‘baits’, via genetic fusion to promiscuous enzymes that catalyze the generation of diffusible reactive species in living cells. Tagged molecules that interact with baits can then be enriched and identified by mass spectrometry or nucleic acid sequencing. Here we review the development of PL technologies and highlight studies that have applied PL to the discovery and analysis of molecular interactions. In particular, we focus on the use of PL for mapping protein–protein, protein–RNA and protein–DNA interactions in living cells and organisms.This Review describes proximity labeling methods that make use of peroxidases (APEX) or biotin ligases (TurboID, BioID), and their applications to studying protein–protein and protein–nucleic acid interactions in living systems.

MaxQuant is a platform for mass spectrometry-based proteomics data analysis. It includes a peptide database search engine, called Andromeda, and expanding capability to handle data from most quantitative proteomics experiments. MaxQuant is one of the most frequently used platforms for mass-spectrometry (MS)-based proteomics data analysis. Since its first release in 2008, it has grown substantially in functionality and can be used in conjunction with more MS platforms. Here we present an updated protocol covering the most important basic computational workflows, including those designed for quantitative label-free proteomics, MS1-level labeling and isobaric labeling techniques. This protocol presents a complete description of the parameters used in MaxQuant, as well as of the configuration options of its integrated search engine, Andromeda. This protocol update describes an adaptation of an existing protocol that substantially modifies the technique. Important concepts of shotgun proteomics and their implementation in MaxQuant are briefly reviewed, including different quantification strategies and the control of false-discovery rates (FDRs), as well as the analysis of post-translational modifications (PTMs). The MaxQuant output tables, which contain information about quantification of proteins and PTMs, are explained in detail. Furthermore, we provide a short version of the workflow that is applicable to data sets with simple and standard experimental designs. The MaxQuant algorithms are efficiently parallelized on multiple processors and scale well from desktop computers to servers with many cores. The software is written in C# and is freely available at http://www.maxquant.org .

Affinity purification coupled with mass spectrometry (AP–MS) and proximity-dependent biotinylation identification (BioID) methods have made substantial contributions to interaction proteomics studies. Whereas AP−MS results in the identification of proteins that are in a stable complex, BioID labels and identifies proteins that are in close proximity to the bait, resulting in overlapping yet distinct protein identifications. Integration of AP–MS and BioID data has been shown to comprehensively characterize a protein’s molecular context, but interactome analysis using both methods in parallel is still labor and resource intense with respect to cell line generation and protein purification. Therefore, we developed the Multiple Approaches Combined (MAC)-tag workflow, which allows for both AP–MS and BioID analysis with a single construct and with almost identical protein purification and mass spectrometry (MS) identification procedures. We have applied the MAC-tag workflow to a selection of subcellular markers to provide a global view of the cellular protein interactome landscape. This localization database is accessible via our online platform ( http://proteomics.fi ) to predict the cellular localization of a protein of interest (POI) depending on its identified interactors. In this protocol, we present the detailed three-stage procedure for the MAC-tag workflow: (1) cell line generation for the MAC-tagged POI; (2) parallel AP–MS and BioID protein purification followed by MS analysis; and (3) protein interaction data analysis, data filtration and visualization with our localization visualization platform. The entire procedure can be completed within 25 d. This protocol describes the MAC-tag approach, which combines affinity purification and biotinylation identification proximity labeling in a single tag. Binding proteins are identified by liquid chromatography–mass spectrometry, followed by visualization of protein localization using an online platform.