High-throughput structure determination of an intrinsically disordered protein using cell-free protein crystallization.

Intrinsically disordered proteins (IDPs) play a crucial role in various biological phenomena, dynamically changing their conformations in response to external environmental cues. To gain a deeper understanding of these proteins, it is essential to identify the determinants that fix their structures at the atomic level. Here, we developed a pipeline for rapid crystal structure analysis of IDP using a cell-free protein crystallization (CFPC) method. Through this approach, we successfully demonstrated the determination of the structure of an IDP to uncover the key determinants that stabilize its conformation. Specifically, we focused on the 11-residue fragment of c-Myc, which forms an α-helix through dimerization with a binding partner protein. This fragment was strategically recombined with an in-cell crystallizing protein and was expressed in a cell-free system. The resulting crystal structures of the c-Myc fragment were successfully determined at a resolution of 1.92 Å and we confirmed that they are identical to the structures of the complex with the native binding partner protein. This indicates that the environment of the scaffold crystal can fix the structure of c-Myc. Significantly, these crystals were obtained directly from a small reaction mixture (30 µL) incubated for only 72 h. Analysis of eight crystal structures derived from 22 mutants revealed two hydrophobic residues as the key determinants responsible for stabilizing the α-helical structure. These findings underscore the power of our CFPC screening method as a valuable tool for determining the structures of challenging target proteins and elucidating the essential molecular interactions that govern their stability.

Structure and dynamics of the staphylococcal pyridoxal 5-phosphate synthase complex reveal transient interactions at the enzyme interface.

Infectious diseases are a significant cause of death, and recent studies estimate that common bacterial infectious diseases were responsible for 13.6% of all global deaths in 2019. Among the most significant bacterial pathogens is Staphylococcus aureus, accounting for more than 1.1 million deaths worldwide in 2019. Vitamin biosynthesis has been proposed as a promising target for antibacterial therapy. Here, we investigated the biochemical, structural, and dynamic properties of the enzyme complex responsible for vitamin B6 (pyridoxal 5-phosphate, PLP) biosynthesis in S. aureus, which comprises enzymes SaPdx1 and SaPdx2. The crystal structure of the 24-mer complex of SaPdx1-SaPdx2 enzymes indicated that the S. aureus PLP synthase complex forms a highly dynamic assembly with transient interaction between the enzymes. Solution scattering data indicated that SaPdx2 typically binds to SaPdx1 at a substoichiometric ratio. We propose a structure-based view of the PLP synthesis mechanism initiated with the assembly of SaPLP synthase complex that proceeds in a highly dynamic interaction between Pdx1 and Pdx2. This interface interaction can be further explored as a potentially druggable site for the design of new antibiotics.

Crystal structure of bacterial ubiquitin ADP-ribosyltransferase CteC reveals a substrate-recruiting insertion.

ADP-ribosylation is a post-translational modification involved in regulation of diverse cellular pathways. Interestingly, many pathogens have been identified to utilize ADP-ribosylation as a way for host manipulation. A recent study found that CteC, an effector from the bacterial pathogen Chromobacterium violaceum, hinders host ubiquitin (Ub) signaling pathways via installing mono-ADP-ribosylation on threonine 66 of Ub. However, the molecular basis of substrate recognition by CteC is not well understood. In this article, we probed the substrate specificity of this effector at protein and residue levels. We also determined the crystal structure of CteC in complex with NAD+, which revealed a canonical mono-ADP-ribosyltransferase fold with an additional insertion domain. The AlphaFold-predicted model differed significantly from the experimentally determined structure, even in regions not used in crystal packing. Biochemical and biophysical studies indicated unique features of the NAD+ binding pocket, while showing selectivity distinction between Ub and structurally close Ub-like modifiers and the role of the insertion domain in substrate recognition. Together, this study provides insights into the enzymatic specificities and the key structural features of a novel bacterial ADP-ribosyltransferase involved in host-pathogen interaction.

The structures of salt-inducible kinase 3 in complex with inhibitors reveal determinants for binding and selectivity.

The salt-inducible kinases (SIKs) 1 to 3, belonging to the AMPK-related kinase family, serve as master regulators orchestrating a diverse set of physiological processes such as metabolism, bone formation, immune response, oncogenesis, and cardiac rhythm. Owing to its key regulatory role, the SIK kinases have emerged as compelling targets for pharmacological intervention across a diverse set of indications. Therefore, there is interest in developing SIK inhibitors with defined selectivity profiles both to further dissect the downstream biology and for treating disease. However, despite a large pharmaceutical interest in the SIKs, experimental structures of SIK kinases are scarce. This is likely due to the challenges associated with the generation of proteins suitable for structural studies. By adopting a rational approach to construct design and protein purification, we successfully crystallized and subsequently solved the structure of SIK3 in complex with HG-9-91-01, a potent SIK inhibitor. To enable further SIK3-inhibitor complex structures we identified an antibody fragment that facilitated crystallization and enabled a robust protocol suitable for structure-based drug design. The structures reveal SIK3 in an active conformation, where the ubiquitin-associated domain is shown to provide further stabilization to this active conformation. We present four pharmacologically relevant and distinct SIK3-inhibitor complexes. These detail the key interaction for each ligand and reveal how different regions of the ATP site are engaged by the different inhibitors to achieve high affinity. Notably, the structure of SIK3 in complex with a SIK3 specific inhibitor offers insights into isoform selectivity.

Temperature-dependent intracellular crystallization of firefly luciferase in mammalian cells is suppressed by D-luciferin and stabilizing inhibitors.

Firefly luciferase (Fluc) from Photinus pyralis is one of the most widely used reporter proteins in biomedical research. Despite its widespread use, Fluc's protein phase transition behaviors and phase separation characteristics have not received much attention. Current research uncovers Fluc's intrinsic property to phase separate in mammalian cells upon a simple cell culture temperature change. Specifically, Fluc spontaneously produced needle-shaped crystal-like inclusion bodies upon temperature shift to the hypothermic temperatures ranging from 25 °C to 31 °C. The crystal-like inclusion bodies were not associated with or surrounded by membranous organelles and were likely built from the cytosolic pool of Fluc. Furthermore, the crystal-like inclusion formation was suppressed when cells were cultured in the presence of D-luciferin and its synthetic analog, as well as the benzothiazole family of so-called stabilizing inhibitors. These two classes of compounds inhibited intracellular Fluc crystallization by different modes of action as they had contrasting effects on steady-state luciferase protein accumulation levels. This study suggests that, under substrate insufficient conditions, the excess Fluc phase separates into a crystal-like state that can modulate intracellular soluble enzyme availability and protein turnover rate.

SADeepcry: a deep learning framework for protein crystallization propensity prediction using self-attention and auto-encoder networks.

The X-ray diffraction (XRD) technique based on crystallography is the main experimental method to analyze the three-dimensional structure of proteins. The production process of protein crystals on which the XRD technique relies has undergone multiple experimental steps, which requires a lot of manpower and material resources. In addition, studies have shown that not all proteins can form crystals under experimental conditions, and the success rate of the final crystallization of proteins is only <10%. Although some protein crystallization predictors have been developed, not many tools capable of predicting multi-stage protein crystallization propensity are available and the accuracy of these tools is not satisfactory. In this paper, we propose a novel deep learning framework, named SADeepcry, for predicting protein crystallization propensity. The framework can be used to estimate the three steps (protein material production, purification and crystallization) in protein crystallization experiments and the success rate of the final protein crystallization. SADeepcry uses the optimized self-attention and auto-encoder modules to extract sequence, structure and physicochemical features from the proteins. Compared with other state-of-the-art protein crystallization propensity prediction models, SADeepcry can obtain more complex global spatial long-distance dependence of protein sequence information. Our computational results show that SADeepcry has increased Matthews correlation coefficient and area under the curve, by 100.3% and 13.4%, respectively, over the DCFCrystal method on the benchmark dataset. The codes of SADeepcry are available at https://github.com/zhc940702/SADeepcry.

Sulisobenzone is a potent inhibitor of the global transcription factor Cra.

Transcription is carried out by the RNA polymerase and is regulated through a series of interactions with transcription factors. Catabolite activator repressor (Cra), a LacI family transcription factor regulates the virulence gene expression in Enterohaemorrhagic Escherichia coli (EHEC) and thus is a promising drug target for the discovery of antivirulence molecules. Here, we report the crystal structure of the effector molecule binding domain of Cra from E. coli (EcCra) in complex with HEPES molecule. Based on the EcCra-HEPES complex structure, ligand screening was performed that identified sulisobenzone as an potential inhibitor of EcCra. The electrophoretic mobility shift assay (EMSA) and in vitro transcription assay validated the sulisobenzone binding to EcCra. Moreover, the isothermal titration calorimetry (ITC) experiments demonstrated a 40-fold higher binding affinity of sulisobenzone (KD 360 nM) compared to the HEPES molecule. Finally, the sulisobenzone bound EcCra complex crystal structure was determined to elucidate the binding mechanism of sulisobenzone to the effector binding pocket of EcCra. Together, this study suggests that sulisobenzone may be a promising candidate that can be studied and developed as an effective antivirulence agent against EHEC.

CPR-C4 is a highly conserved novel protease from the Candidate Phyla Radiation with remote structural homology to human vasohibins.

The Candidate Phyla Radiation is a recently uncovered and vast expansion of the bacterial domain of life, made up of largely uncharacterized phyla that lack isolated representatives. This unexplored territory of genetic diversity presents an abundance of novel proteins with potential applications in the life-science sectors. Here, we present the structural and functional elucidation of CPR-C4, a hypothetical protein from the genome of a thermophilic Candidate Phyla Radiation organism, identified through metagenomic sequencing. Our analyses revealed that CPR-C4 is a member of a family of highly conserved proteins within the Candidate Phyla Radiation. The function of CPR-C4 as a cysteine protease was predicted through remote structural similarity to the Homo sapiens vasohibins and subsequently confirmed experimentally with fluorescence-based activity assays. Furthermore, detailed structural and sequence alignment analysis enabled identification of a noncanonical cysteine-histidine-leucine(carbonyl) catalytic triad. The unexpected structural and functional similarities between CPR-C4 and the human vasohibins suggest an evolutionary relationship undetectable at the sequence level alone.

Self-activating G protein α subunits engage seven-transmembrane regulator of G protein signaling (RGS) proteins and a Rho guanine nucleotide exchange factor effector in the amoeba Naegleria fowleri.

The free-living amoeba Naegleria fowleri is a causative agent of primary amoebic meningoencephalitis and is highly resistant to current therapies, resulting in mortality rates >97%. As many therapeutics target G protein-centered signal transduction pathways, further understanding the functional significance of G protein signaling within N. fowleri should aid future drug discovery against this pathogen. Here, we report that the N. fowleri genome encodes numerous transcribed G protein signaling components, including G protein-coupled receptors, heterotrimeric G protein subunits, regulator of G protein signaling (RGS) proteins, and candidate Gα effector proteins. We found N. fowleri Gα subunits have diverse nucleotide cycling kinetics; Nf Gα5 and Gα7 exhibit more rapid nucleotide exchange than GTP hydrolysis (i.e., "self-activating" behavior). A crystal structure of Nf Gα7 highlights the stability of its nucleotide-free state, consistent with its rapid nucleotide exchange. Variations in the phosphate binding loop also contribute to nucleotide cycling differences among Gα subunits. Similar to plant G protein signaling pathways, N. fowleri Gα subunits selectively engage members of a large seven-transmembrane RGS protein family, resulting in acceleration of GTP hydrolysis. We show Nf Gα2 and Gα3 directly interact with a candidate Gα effector protein, RGS-RhoGEF, similar to mammalian Gα12/13 signaling pathways. We demonstrate Nf Gα2 and Gα3 each engage RGS-RhoGEF through a canonical Gα/RGS domain interface, suggesting a shared evolutionary origin with G protein signaling in the enteric pathogen Entamoeba histolytica. These findings further illuminate the evolution of G protein signaling and identify potential targets of pharmacological manipulation in N. fowleri.

Nucleation-Supersaturation Dual-Drive Crystallization Strategy Enables Efficient Protein Crystallization.

A rational crystallization strategy is essential to obtain high-quality protein crystals, yet the established methods suffer from different limitations arising from the single regulation on either nucleation or supersaturation. Herein, a nucleation-supersaturation dual-driven crystallization (DDC) strategy that realizes synergistic regulation of heterogeneous nucleation sites and solution supersaturation based on dual surface and confinement effects for efficient protein crystallization is reported. This strategy relies on a p(PEGDA-co-DMAA) hydrogel template with pre-filled NaCl under designed concentrations. Once dropping hen egg white lysozyme (HEWL) protein solution on the hydrogel, the wrinkled surface provides numerous nucleation sites, while the internal structure regulates the solution supersaturation in the crystallization region through diffusion. Finally, DDC strategy can create high-quality HEWL crystals with large sizes (100-300 µm), well-defined morphologies (hexagon and tetragon), and a significantly accelerated nucleation time (9-12 times faster than that achieved using the conventional hanging drop method). It also performs well at wider protein concentrations (10-50 mg mL-1 ) and categories (e.g., achieving fast crystallization and large-size crystals of trypsin), therefore demonstrating clear advantages and great potential for efficiently fabricating protein crystals desirable for diverse applications.

Accurate multistage prediction of protein crystallization propensity using deep-cascade forest with sequence-based features.

X-ray crystallography is the major approach for determining atomic-level protein structures. Because not all proteins can be easily crystallized, accurate prediction of protein crystallization propensity provides critical help in guiding experimental design and improving the success rate of X-ray crystallography experiments. This study has developed a new machine-learning-based pipeline that uses a newly developed deep-cascade forest (DCF) model with multiple types of sequence-based features to predict protein crystallization propensity. Based on the developed pipeline, two new protein crystallization propensity predictors, denoted as DCFCrystal and MDCFCrystal, have been implemented. DCFCrystal is a multistage predictor that can estimate the success propensities of the three individual steps (production of protein material, purification and production of crystals) in the protein crystallization process. MDCFCrystal is a single-stage predictor that aims to estimate the probability that a protein will pass through the entire crystallization process. Moreover, DCFCrystal is designed for general proteins, whereas MDCFCrystal is specially designed for membrane proteins, which are notoriously difficult to crystalize. DCFCrystal and MDCFCrystal were separately tested on two benchmark datasets consisting of 12 289 and 950 proteins, respectively, with known crystallization results from various experimental records. The experimental results demonstrated that DCFCrystal and MDCFCrystal increased the value of Matthew's correlation coefficient by 199.7% and 77.8%, respectively, compared to the best of other state-of-the-art protein crystallization propensity predictors. Detailed analyses show that the major advantages of DCFCrystal and MDCFCrystal lie in the efficiency of the DCF model and the sensitivity of the sequence-based features used, especially the newly designed pseudo-predicted hybrid solvent accessibility (PsePHSA) feature, which improves crystallization recognition by incorporating sequence-order information with solvent accessibility of residues. Meanwhile, the new crystal-dataset constructions help to train the models with more comprehensive crystallization knowledge.

GCmapCrys: Integrating graph attention network with predicted contact map for multi-stage protein crystallization propensity prediction.

X-ray crystallography is the major approach for atomic-level protein structure determination. Since not all proteins can be easily crystallized, accurate prediction of protein crystallization propensity is critical to guiding the experimental design and improving the success rate of X-ray crystallography experiments. In this work, we proposed a new deep learning pipeline, GCmapCrys, for multi-stage crystallization propensity prediction through integrating graph attention network with predicted protein contact map. Experimental results on 1548 proteins with known crystallization records demonstrated that GCmapCrys increased the value of Matthew's correlation coefficient by 37.0% in average compared to state-of-the-art protein crystallization propensity predictors. Detailed analyses show that the major advantages of GCmapCrys lie in the efficiency of the graph attention network with predicted contact map, which effectively associates the residue-interaction knowledge with crystallization pattern. Meanwhile, the designed four sequence-based features can be complementary to further enhance crystallization propensity proprediction.

Lysosomal sialidase NEU1, its intracellular properties, deficiency, and use as a therapeutic agent.

Neuraminidase 1 (NEU1) is a lysosomal sialidase that cleaves terminal α-linked sialic acid residues from sialylglycans. NEU1 is biosynthesized in the rough endoplasmic reticulum (RER) lumen as an N-glycosylated protein to associate with its protective protein/cathepsin A (CTSA) and then form a lysosomal multienzyme complex (LMC) also containing ß-galactosidase 1 (GLB1). Unlike other mammalian sialidases, including NEU2 to NEU4, NEU1 transport to lysosomes requires association of NEU1 with CTSA, binding of the CTSA carrying terminal mannose 6-phosphate (M6P)-type N-glycan with M6P receptor (M6PR), and intralysosomal NEU1 activation at acidic pH. In contrast, overexpression of the single NEU1 gene in mammalian cells causes intracellular NEU1 protein crystallization in the RER due to self-aggregation when intracellular CTSA is reduced to a relatively low level. Sialidosis (SiD) and galactosialidosis (GS) are autosomal recessive lysosomal storage diseases caused by the gene mutations of NEU1 and CTSA, respectively. These incurable diseases associate with the NEU1 deficiency, excessive accumulation of sialylglycans in neurovisceral organs, and systemic manifestations. We established a novel GS model mouse carrying homozygotic Ctsa IVS6 + 1 g/a mutation causing partial exon 6 skipping with simultaneous deficiency of Ctsa and Neu1. Symptoms developed in the GS mice like those in juvenile/adult GS patients, such as myoclonic seizures, suppressed behavior, gargoyle-like face, edema, proctoptosis due to Neu1 deficiency, and sialylglycan accumulation associated with neurovisceral inflammation. We developed a modified NEU1 (modNEU1), which does not form protein crystals but is transported to lysosomes by co-expressed CTSA. In vivo gene therapy for GS and SiD utilizing a single adeno-associated virus (AAV) carrying modNEU1 and CTSA genes under dual promoter control will be created.

Rational design of a hydrolysis-resistant mycobacterial phosphoglycolipid antigen presented by CD1c to T cells.

Whereas proteolytic cleavage is crucial for peptide presentation by classical major histocompatibility complex (MHC) proteins to T cells, glycolipids presented by CD1 molecules are typically presented in an unmodified form. However, the mycobacterial lipid antigen mannosyl-ß1-phosphomycoketide (MPM) may be processed through hydrolysis in antigen presenting cells, forming mannose and phosphomycoketide (PM). To further test the hypothesis that some lipid antigens are processed, and to generate antigens that lead to defined epitopes for future tuberculosis vaccines or diagnostic tests, we aimed to create hydrolysis-resistant MPM variants that retain their antigenicity. Here, we designed and tested three different, versatile synthetic strategies to chemically stabilize MPM analogs. Crystallographic studies of CD1c complexes with these three new MPM analogs showed anchoring of the lipid tail and phosphate group that is highly comparable to nature-identical MPM, with considerable conformational flexibility for the mannose head group. MPM-3, a difluoromethylene-modified version of MPM that is resistant to hydrolysis, showed altered recognition by cells, but not by CD1c proteins, supporting the cellular antigen processing hypothesis. Furthermore, the synthetic analogs elicited T cell responses that were cross-reactive with nature-identical MPM, fulfilling important requirements for future clinical use.

The crystal structure of the S154Y mutant carbonyl reductase from Leifsonia xyli explains enhanced activity for 3,5-bis(trifluoromethyl)acetophenone reduction.

Carbonyl reductase from Leifsonia xyli (LXCAR, UniProtKB - T2FLN4) can stereoselectively catalyze the reduction of 3,5-bis(trifluoromethyl)acetophenone (BTAP) to its corresponding alcohol, (R)-[3,5-bis(trifluoromethyl)phenyl]ethanol ((R)-BTPE), which is a valuable chiral intermediate for the synthesis of antiemetic drugs, Aprepitant and Fosaprepitant. Moreover, this protein was found to have a broad spectrum of substrate specificity and can asymmetrically catalyze the reduction of a variety of ketones and keto esters. Even though molecular modelling of this protein was done by Wang et al. (2014), a crystal structure has not yet obtained. In this study, a single mutant, S154Y, which was shown to have higher catalytic activity toward BTAP than that of the wild type, was overexpressed in Escherichia coli BL21 (DE3), purified, and crystallized. The crystal structure of LXCAR-S154Y explains how the mutant enzyme can work with BTAP more efficiently than wild type carbonyl reductase. Furthermore, the structure explains why LXCAR-S154Y can use either NADH or NADPH efficiently as a cofactor, as well as elucidates a proton relay system present in the enzyme.

Design and evaluation of novel analogs of 2-amino-4-boronobutanoic acid (ABBA) as inhibitors of human gamma-glutamyl transpeptidase.

Inhibitors of gamma-glutamyl transpeptidase (GGT1, aka gamma-glutamyl transferase) are needed for the treatment of cancer, cardiovascular illness and other diseases. Compounds that inhibit GGT1 have been evaluated in the clinic, but no inhibitor has successfully demonstrated specific and systemic GGT1 inhibition. All have severe side effects. L-2-amino-4­boronobutanoic acid (l-ABBA), a glutamate analog, is the most potent GGT1 inhibitor in vitro. In this study, we have solved the crystal structure of human GGT1 (hGGT1) with ABBA bound in the active site. The structure was interrogated to identify interactions between the enzyme and the inhibitor. Based on these data, a series of novel ABBA analogs were designed and synthesized. Their inhibitory activity against the hydrolysis and transpeptidation activities of hGGT1 were determined. The lead compounds were crystalized with hGGT1 and the structures solved. The kinetic data and structures of the complexes provide new insights into the critical role of protein structure dynamics in developing compounds for inhibition of hGGT1.

Machine learning-based protein crystal detection for monitoring of crystallization processes enabled with large-scale synthetic data sets of photorealistic images.

Since preparative chromatography is a sustainability challenge due to large amounts of consumables used in downstream processing of biomolecules, protein crystallization offers a promising alternative as a purification method. While the limited crystallizability of proteins often restricts a broad application of crystallization as a purification method, advances in molecular biology, as well as computational methods are pushing the applicability towards integration in biotechnological downstream processes. However, in industrial and academic settings, monitoring protein crystallization processes non-invasively by microscopic photography and automated image evaluation remains a challenging problem. Recently, the identification of single crystal objects using deep learning has been the subject of increased attention for various model systems. However, the advancement of crystal detection using deep learning for biotechnological applications is limited: robust models obtained through supervised machine learning tasks require large-scale and high-quality data sets usually obtained in large projects through extensive manual labeling, an approach that is highly error-prone for dense systems of transparent crystals. For the first time, recent trends involving the use of synthetic data sets for supervised learning are transferred, thus generating photorealistic images of virtual protein crystals in suspension (PCS) through the use of ray tracing algorithms, accompanied by specialized data augmentations modelling experimental noise. Further, it is demonstrated that state-of-the-art models trained with the large-scale synthetic PCS data set outperform similar fine-tuned models based on the average precision metric on a validation data set, followed by experimental validation using high-resolution photomicrographs from stirred tank protein crystallization processes.

TLCrys: Transfer Learning Based Method for Protein Crystallization Prediction.

X-ray diffraction technique is one of the most common methods of ascertaining protein structures, yet only 2-10% of proteins can produce diffraction-quality crystals. Several computational methods have been proposed so far to predict protein crystallization. Nevertheless, the current state-of-the-art computational methods are limited by the scarcity of experimental data. Thus, the prediction accuracy of existing models hasn't reached the ideal level. To address the problems above, we propose a novel transfer-learning-based framework for protein crystallization prediction, named TLCrys. The framework proceeds in two steps: pre-training and fine-tuning. The pre-training step adopts attention mechanism to extract both global and local information of the protein sequences. The representation learned from the pre-training step is regarded as knowledge to be transferred and fine-tuned to enhance the performance of crystalization prediction. During pre-training, TLCrys adopts a multi-task learning method, which not only improves the learning ability of protein encoding, but also enhances the robustness and generalization of protein representation. The multi-head self-attention layer guarantees that different levels of the protein representation can be extracted by the fine-tuned step. During transfer learning, the fine-tuning strategy used by TLCrys improves the task-specialized learning ability of the network. Our method outperforms all previous predictors significantly in five crystallization stages of prediction. Furthermore, the proposed methodology can be well generalized to other protein sequence classification tasks.

[Progress of target determination and mechanism of bioactive components of traditional Chinese medicine].

The pharmacodynamic substances of traditional Chinese medicine(TCM) are the basis for the research of TCM and the development of innovative drugs. However, the lack of clarity of targets and molecular mechanisms is the bottleneck problem that restricts the research of pharmacodynamic substances of TCM. Bioactive components are the material basis of the efficacy of TCM, which exert activity by regulating the corresponding targets. Therefore, it is very important to identify the targets of the bioactive components to elucidate the pharmacological mechanism of TCM. Proteins are the most important drug targets, and study of the interaction between the proteins and bioactive components of TCM plays a key role in the development of pharmacological mechanism of TCM. In recent years, the main techniques for detecting the interaction between the bioactive components and proteins include surface plasmon resonance, fluorescence resonance energy transfer, bio-layer interference, molecular docking, proteome chip, target fishing, target mutant, and protein crystallization techniques, etc. This review summarized the biological target detection techniques and their applications in locating the targets of the bioactive components in TCM in the last decade, and this paper will provide useful strategies to elucidate the pharmacological mechanisms of TCM.

Discovery of a heme-binding domain in a neuronal voltage-gated potassium channel.

The EAG (ether-à-go-go) family of voltage-gated K+ channels are important regulators of neuronal and cardiac action potential firing (excitability) and have major roles in human diseases such as epilepsy, schizophrenia, cancer, and sudden cardiac death. A defining feature of EAG (Kv10-12) channels is a highly conserved domain on the N terminus, known as the eag domain, consisting of a Per-ARNT-Sim (PAS) domain capped by a short sequence containing an amphipathic helix (Cap domain). The PAS and Cap domains are both vital for the normal function of EAG channels. Using heme-affinity pulldown assays and proteomics of lysates from primary cortical neurons, we identified that an EAG channel, hERG3 (Kv11.3), binds to heme. In whole-cell electrophysiology experiments, we identified that heme inhibits hERG3 channel activity. In addition, we expressed the Cap and PAS domain of hERG3 in Escherichia coli and, using spectroscopy and kinetics, identified the PAS domain as the location for heme binding. The results identify heme as a regulator of hERG3 channel activity. These observations are discussed in the context of the emerging role for heme as a regulator of ion channel activity in cells.