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Mechanism-Based Strategy for Optimizing HaloTag Protein Labeling.

ABSTRACT: HaloTag labeling technology has introduced unrivaled potential in protein chemistry and molecular and cellular biology. A wide variety of ligands have been developed to meet the specific needs of diverse applications, but only a single protein tag, DhaAHT, is routinely used for their incorporation. Following a systematic kinetic and computational analysis of different reporters, a tetramethylrhodamine- and three 4-stilbazolium-based fluorescent ligands, we showed that the mechanism of incorporating different ligands depends both on the binding step and the efficiency of the chemical reaction. By studying the different haloalkane dehalogenases DhaA, LinB, and DmmA, we found that the architecture of the access tunnels is critical for the kinetics of both steps and the ligand specificity. We showed that highly efficient labeling with specific ligands is achievable with natural dehalogenases. We propose a simple protocol for selecting the optimal protein tag for a specific ligand from the wide pool of available enzymes with diverse access tunnel architectures. The application of this protocol eliminates the need for expensive and laborious protein engineering.

SUBMITTER: Marques SM

PROVIDER: S-EPMC9241015 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Clostridioides difficile (formerly Clostridium difficile) is a Gram-positive, anaerobe, spore-forming pathogen, which causes drug-induced diseases in hospitals worldwide. A detailed analysis of the proteome may provide new targets for drug development or therapeutic strategies to combat this pathogen. The application of metabolic labeling (ML) would allow for accurate quantification of significant differences in protein abundance, even in the case of very small changes. Additionally, it would be possible to perform more accurate studies of the membrane or surface proteomes, which usually require elaborated sample preparation. Such studies are therefore prone to higher standard deviations during the quantification. The implementation of ML strategies for C. difficile is complicated due to the lack in arginine and lysine auxotrophy as well as the Stickland dominated metabolism of this anaerobic pathogen. Hence, quantitative proteome analyses could only be carried out by label free or chemical labeling methods so far. In this paper, a ML approach for C. difficile is described. A cultivation procedure with 15N-labeled media for strain 630?erm was established achieving an incorporation rate higher than 97%. In a proof-of-principle experiment, the performance of the ML approach in C. difficile was tested. The proteome data of the cytosolic subproteome of C. difficile cells grown in complex medium as well as two minimal media in the late exponential and early stationary growth phase obtained via ML were compared with two label free relative quantification approaches (NSAF and LFQ). The numbers of identified proteins were comparable within the three approaches, whereas the number of quantified proteins were between 1,110 (ML) and 1,861 (LFQ) proteins. A hierarchical clustering showed clearly separated clusters for the different conditions and a small tree height with ML approach. Furthermore, it was shown that the quantification based on ML revealed significant altered proteins with small fold changes compared to the label free approaches. The quantification based on ML was accurate, reproducible, and even more sensitive compared to label free quantification strategies.

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Mechanism-Based Strategy for Optimizing HaloTag Protein Labeling.

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