生物化学

Underwater noise is a growing source of anthropogenic pollution in aquatic environments. However, few studies have evaluated the impact of underwater noise on aquatic invertebrates. More importantly, studies involving early developmental stages have been poorly addressed. Significant limitations are due to the lack of standardized protocols for working in the laboratory. Particularly, the design of uniform procedures in the laboratory is important when working with species that inhabit short-term changing habitats, such as estuaries, which makes it difficult to carry out repeated experiments in the natural habitat. Besides, controlling for environmental variables is also important when assessing the effect of a stressor on the physiological parameters of individuals. This experimental protocol addresses that gap by offering an adaptable laboratory-based method to evaluate sublethal physiological responses to sound exposure under highly controlled conditions. Here, we present a reproducible and accessible laboratory protocol to expose crabs to recorded boat noise and evaluate physiological responses using oxidative stress biomarkers. The method is designed for ovigerous females, as we evaluated the effects on embryos and early life stages (i.e., larvae), but it can be readily adapted to different life stages of aquatic invertebrates. A key strength of this protocol is its simplicity and flexibility: animals are exposed to noise using submerged transducers under well-controlled laboratory conditions, ensuring consistency and repeatability. Following exposure, tissues or whole-body samples can be processed for a suite of oxidative stress biomarkers—glutathione-S-transferase (GST), catalase (CAT), lipid peroxidation (LPO), and protein oxidation. These biomarkers are highly responsive, cost-effective indicators that provide a sensitive and early readout of sublethal stress. Together, the exposure and analysis steps described in this protocol offer a powerful and scalable approach for investigating the physiological impacts of underwater noise in crustaceans and other aquatic invertebrates.

Although protein–protein interactions (PPIs) are central to nearly all biological processes, identifying and engineering high-affinity intracellular binders remains a significant challenge due to the complexity of the cellular environment and the folding constraints of proteins. Here, we present a two-stage complementary platform that combines magnetic-activated cell sorting (MACS)-based yeast surface display with functional ligand-binding identification by twin-arginine translocation (Tat)-based recognition of associating proteins (FLI-TRAP), a bacterial genetic selection system for efficient screening, validation, and optimization of PPIs. In the first stage, MACS-based yeast display enables the rapid high-throughput identification of candidate binders for a target antigen from a large synthetic-yeast display library through extracellular interaction screening. In the second stage, an antigen-focused library is subcloned into the FLI-TRAP system, which exploits the hitchhiker export process of the Escherichia coli Tat pathway to evaluate binder–antigen binding in the cytoplasm. This stage is achieved by co-expressing a Tat signal peptide–tagged protein of interest with a β-lactamase-tagged antigen target, such that only binder–antigen pairs with sufficient affinity are co-translocated into the periplasm, thus rendering the bacterium β-lactam antibiotic resistant. Because Tat-dependent export requires fully folded and soluble proteins, FLI-TRAP further serves as a stringent in vivo filter for intracellular compatibility, folding, and stability. Therefore, this approach provides a powerful and cost-effective pipeline for discovering and engineering intracellular protein binders with high affinity, specificity, and functional expression in bacterial systems. This workflow holds promise for several applications, including synthetic biology and screening of theragnostic proteins and PPI inhibitors.

Traditional methods for studying protein–protein interactions often lack the resolution to quantitatively distinguish distinct oligomeric states, particularly for membrane proteins within their native lipid environments. To address this limitation, we developed SiMPull-POP (single-molecule pull-down polymeric nanodisc photobleaching), a single-molecule technique designed to quantify membrane protein oligomerization with high sensitivity and in a near-native context. The goal of SiMPull-POP is to enable precise, quantitative analysis of membrane protein assembly by preserving native lipid interactions using diisobutylene maleic acid (DIBMA) to form nanodiscs. Unlike ensemble methods such as co-immunoprecipitation or FRET, which average out heterogeneous populations, SiMPull-POP uses photobleaching to resolve monomeric, dimeric, and higher-order oligomeric states at the single-molecule level. We validated SiMPull-POP using several model systems. A truncated, single-pass transmembrane protein (Omp25) appeared primarily monomeric, while a membrane-tethered FKBP protein exhibited ligand-dependent dimerization upon addition of the AP ligand. Applying SiMPull-POP to EphA2, a receptor tyrosine kinase, we found it to be mostly monomeric in the absence of its ligand, Ephrin-A1, and shifting toward higher-order oligomers upon ligand binding. To explore factors influencing ligand-independent assembly, we modulated membrane cholesterol content. Reducing cholesterol induced spontaneous EphA2 oligomerization, indicating that cholesterol suppresses receptor self-association. Overall, SiMPull-POP offers significant advantages over conventional techniques by enabling quantitative, single-molecule resolution of membrane protein complexes in a native-like environment. This approach provides critical insights into how membrane properties and external stimuli regulate protein assembly, supporting broader efforts to understand membrane protein function in both normal and disease states.

Small GTPases function as molecular switches in cells, and their activation triggers diverse cellular responses depending on the GTPase type. Therefore, visualizing small GTPase activation in living cells is crucial because their activity is tightly regulated in space and time, and this spatiotemporal pattern of activation often determines their specific cellular functions. Various biosensors, such as relocation-based sensors and fluorescence resonance energy transfer (FRET)-based sensors, have been developed. However, these methods rely on interactions between activated GTPases and their downstream effectors, which limits their applicability for detecting activation of GTPases with unknown or atypical effectors. Recently, we developed a novel method utilizing split fluorescence technology to detect membrane recruitment of small GTPases upon activation, designated the Small GTPase ActIvitY ANalyzing (SAIYAN) system. This approach offers a new strategy for monitoring small GTPase activation based on membrane association and is potentially applicable to a wide range of small GTPases, including those with uncharacterized effectors.

The protochlorophyllide (Pchlide) level is a crucial indicator of plant fitness. Precise quantification of Pchlide content is necessary not only in studies of flu-related mutants that over-accumulate Pchlide in the dark but also for research on plants suffering from environmental stresses. Due to its low content and interference of chlorophylls, quantitative determination of Pchlide content is a challenge. Here, we describe an optimized protocol for Pchlide extraction from Arabidopsis thaliana seedlings and subsequent analysis using high-performance liquid chromatography (HPLC) coupled with fluorescence detection. Divinyl-Protochlorophyllide (DV-Pchlide, the major form of Pchlide in plants) quantification is achieved by interpolating fluorescence peak areas against an experimentally derived standard curve. This protocol provides a reliable workflow for Pchlide quantification, facilitating the deciphering of the underlying mechanism of plant environmental resilience.

The cellular secretome is a rich source of biomarkers and extracellular signaling molecules, but proteomic profiling remains challenging, especially when processing culture volumes greater than 5 mL. Low protein abundance, high serum contamination, and sample loss during preparation limit reproducibility and sensitivity in mass spectrometry–based workflows. Here, we present an optimized and scalable protocol that integrates (i) 50 kDa molecular weight cutoff ultrafiltration, (ii) spin column depletion of abundant serum proteins, and (iii) acetone/TCA precipitation for protein recovery. This workflow enables balanced recovery of both low- and high-molecular-weight proteins while reducing background from serum albumin, thereby improving sensitivity, reproducibility, and dynamic range for LC–MS/MS analysis. Validated in human mesenchymal stromal cell cultures, the protocol is broadly applicable across diverse cell types and experimental designs, making it well-suited for biomarker discovery and extracellular proteomics.

Protein S-nitrosylation is a critical post-translational modification that regulates diverse cellular functions and signaling pathways. Although various biochemical methods have been developed to detect S-nitrosylated proteins, many suffer from limited specificity and sensitivity. Here, we describe a robust protocol that combines a modified biotin-switch technique (BST) with streptavidin-based affinity enrichment and quantitative mass spectrometry to detect and profile nitrosylated proteins in cultured cells. The method involves blocking free thiols, selective reduction of nitrosothiols, biotin labeling, enrichment of biotinylated proteins, and identification by tandem mass tag (TMT)-based quantitative mass spectrometry. Additionally, site-directed mutagenesis is employed to generate “non-nitrosylable” mutants for functional validation of specific nitrosylation sites. This protocol provides high specificity, quantitative capability, and versatility for both targeted and global analysis of protein nitrosylation.

Characterizing the morphology of amyloid proteins is an integral part of studying neurodegenerative diseases. Such morphological characterization can be performed using atomic force microscopy (AFM), which provides high-resolution images of the amyloid protein fibrils. AFM is widely employed for visualizing mechanical and physical properties of amyloid fibrils, not only from a biological and medical perspective but also in relation to their nanotechnological applications. A crucial step in AFM imaging is coating the protein of interest onto a substrate such as mica. However, existing protocols for this process vary considerably. The conventional sample preparation method often introduces artifacts, particularly due to deposition of excess salt. Hence, an optimized protocol is essential to minimize salt aggregation on the mica surface. Here, we present an optimized protocol for coating amyloid proteins onto mica using the dip-washing method to eliminate background noise. This approach improves the adherence of protein to the mica surface while effectively removing residual salts.

Insects rely on chemosensory proteins, including gustatory receptors, to detect chemical cues that regulate feeding, mating, and oviposition behaviours. Conventional approaches for studying these proteins are limited by the scarcity of experimentally resolved structures, especially in non-model pest species. Here, we present a reproducible computational protocol for the identification, functional annotation, and structural modelling of insect chemosensory proteins, demonstrated using gustatory receptors from the red palm weevil (Rhynchophorus ferrugineus) as an example. The protocol integrates publicly available sequence data with OmicsBox for functional annotation and ColabFold for high-confidence structure prediction, providing a step-by-step framework that can be applied to genome-derived or transcriptomic datasets. The workflow is designed for broad applicability across insect species and generates structurally reliable protein models suitable for downstream applications such as ligand docking or molecular dynamics simulations. By bridging functional annotation with structural characterisation, this protocol enables reproducible studies of chemosensory proteins in agricultural and ecological contexts and supports the development of novel pest management strategies.

In neuropharmacology and drug development, in silico methods have become increasingly vital, particularly for studying receptor–ligand interactions at the molecular level. Membrane proteins such as GABA (A) receptors play a central role in neuronal signaling and are key targets for therapeutic intervention. While experimental techniques like electrophysiology and radioligand binding provide valuable functional data, they often fall short in resolving the structural complexity of membrane proteins and can be time-consuming, costly, and inaccessible in many research settings. This study presents a comprehensive computational workflow for investigating membrane protein–ligand interactions, demonstrated using the GABA (A) receptor α5β2γ2 subtype and mitragynine, an alkaloid from Mitragyna speciosa (Kratom), as a case study. The protocol includes homology modeling of the receptor based on a high-resolution template, followed by structure optimization and validation. Ligand docking is then used to predict binding sites and affinities at known modulatory interfaces. Finally, molecular dynamics (MD) simulations assess the stability and conformational dynamics of receptor–ligand complexes over time. Overall, this workflow offers a robust, reproducible approach for structural analysis of membrane protein–ligand interactions, supporting early-stage drug discovery and mechanistic studies across diverse membrane protein targets.