How many drug targets are there?

Chromatographic Phospholipid Trapping for Automated H/D Exchange Mass Spectrometry of Membrane Protein-Lipid Assemblies

Lipid interactions modulate the function, folding, structure, and organization of membrane proteins. Hydrogen/deuterium exchange mass spectrometry (HDX-MS) has emerged as a useful tool to understand the structural dynamics of these proteins within lipid environments. Lipids, however, have proven problematic for HDX-MS analysis of membrane-embedded proteins due to their presence of impairing proteolytic digestion, causing liquid chromatography column fouling, ion suppression, and/or mass spectral overlap. Herein, we describe the integration of a chromatographic phospholipid trap column into the HDX-MS apparatus to enable online sample delipidation prior to protease digestion of deuterium-labeled protein-lipid assemblies. We demonstrate the utility of this method on membrane scaffold protein-lipid nanodisc─both empty and loaded with the ∼115 kDa transmembrane protein AcrB─proving efficient and automated phospholipid capture with minimal D-to-H back-exchange, peptide carry-over, and protein loss. Our results provide insights into the efficiency of phospholipid capture by ZrO₂-coated and TiO₂ beads and describe how solution conditions can be optimized to maximize not only the performance of our online but also the existing offline, delipidation workflows for HDX-MS. We envision that this HDX-MS method will significantly ease membrane protein analysis, allowing to better interrogate their dynamics in artificial lipid bilayers or even native cell membranes.

Harnessing Split-Inteins as a Tool for the Selective Modification of Surface Receptors in Live Cells

Biochemical studies of integral membrane proteins are often hampered by low purification yields and technical limitations such as aggregation causing in vitro manipulations to be challenging. The ability of controlling proteins in live cells bypasses these limitations while broadening the scope of accessible questions owing to the proteins being in their native environment. Here we take advantage of the intein biorthogonality to mammalian systems, site specificity, fast kinetics, and auto-processing nature as an attractive option for modifying surface proteins. Using EGFR as a model, we demonstrate that the split-intein pair AvaN /NpuC can be used to efficiently and specifically modify target membrane proteins with a synthetic adduct for downstream live cell application.

Zwitterionic fluorinated detergents: From design to membrane protein applications

We report herein the synthesis of zwitterionic sulfobetaine (SB) and dimethylamine oxide (AO) detergents whose alkyl chain is made of either a perfluorohexyl (F₆H₃) or a perfluoropentyl (F₅H₅) group linked to a hydrogenated spacer arm. In aqueous solution, the critical micellar concentrations (CMCs) measured by surface tensiometry (SFT) and isothermal titration calorimetry (ITC) were found in the millimolar range (1.3-2.4 mM). The morphologies of the aggregates were evaluated by dynamic light scattering (DLS), analytical ultracentrifugation (AUC), small-angle X-ray scattering (SAXS), and transmission electron microscopy (TEM), demonstrating that the two perfluoropentyl derivatives formed small micelles less than 10 nm in diameter, whereas the perfluorohexyl derivatives formed larger and more heterogeneous micelles. The two SB detergents were able to solubilize synthetic lipid vesicles in a few hours; by contrast, the perfluoropentyl AO induced much faster solubilization, whereas the perfluorohexyl AO did not show any solubilization. All detergents were tested for their abilities to stabilize three membrane proteins, namely, bacteriorhodopsin (bR), the Bacillus subtilis ABC transporter BmrA, and the Streptococcus pneumoniae enzyme SpNOX. The SB detergents outperformed the AO derivatives as well as their hydrogenated analogs in stabilizing these proteins. Among the four new compounds, F₅H₅SB combines many desirable properties for membrane-protein study, as it is a powerful yet gentle detergent.

Antifungal mechanisms of volatile organic compounds produced by Pseudomonas fluorescens ZX as biological fumigants against Botrytis cinerea

To explore the antifungal mechanisms of volatile organic compounds (VOCs) produced by Pseudomonas fluorescens ZX against Botrytis cinerea, biochemical analyses and transcriptomic techniques were employed in this work. The results showed that P. fluorescens ZX-producing VOCs can increase the cell membrane permeability of B. cinerea and disrupt cell membrane integrity, resulting in leakage of the pathogen's cellular contents, inhibition of ergosterol biosynthesis (about 76%), and an increase in malondialdehyde (MDA) content. Additionally, for B. cinerea respiration, P. fluorescens ZX-producing VOCs (1 × 10⁹ CFU /mL) significantly inhibited the activities of ATPase (55.7%), malate dehydrogenase (MDH) (33.1%), and succinate dehydrogenase (SDH) (57.9%), seriously interfering with energy metabolism and causing accumulation of reactive oxygen species (ROS). Furthermore, transcriptome analysis of B. cinerea following exposure to VOCs revealed 4590 differentially expressed genes (DEGs) (1388 upregulated, 3202 downregulated). Through GO analysis, these DEGs were determined to be enriched in intrinsic components of membrane, integral components of membrane, and membrane parts, while KEGG analysis indicated that they were enriched in many amino acid metabolism pathways. Significantly, the DEGs related to ergosterol biosynthesis, ATPase, mitochondrial respiratory chain, malate dehydrogenase, and cell membrane showed down-regulation, corroborating the biochemical analyses. Taken together, these results suggest that the antifungal activity of P. fluorescens ZX-producing VOCs against B. cinerea occurs primary mechanisms: causing significant damage to the cell membrane, negatively affecting respiration, and interfering with amino acid metabolism.

Regulation of membrane protein structure and function by their lipid nano-environment

Transmembrane proteins comprise ~30% of the mammalian proteome, mediating metabolism, signalling, transport and many other functions required for cellular life. The microenvironment of integral membrane proteins (IMPs) is intrinsically different from that of cytoplasmic proteins, with IMPs solvated by a compositionally and biophysically complex lipid matrix. These solvating lipids affect protein structure and function in a variety of ways, from stereospecific, high-affinity protein-lipid interactions to modulation by bulk membrane properties. Specific examples of functional modulation of IMPs by their solvating membranes have been reported for various transporters, channels and signal receptors; however, generalizable mechanistic principles governing IMP regulation by lipid environments are neither widely appreciated nor completely understood. Here, we review recent insights into the inter-relationships between complex lipidomes of mammalian membranes, the membrane physicochemical properties resulting from such lipid collectives, and the regulation of IMPs by either or both. The recent proliferation of high-resolution methods to study such lipid-protein interactions has led to generalizable insights, which we synthesize into a general framework termed the 'functional paralipidome' to understand the mutual regulation between membrane proteins and their surrounding lipid microenvironments.

A comparative characterisation of commercially available lipid-polymer nanoparticles formed from model membranes

From the discovery of the first membrane-interacting polymer, styrene maleic-acid (SMA), there has been a rapid development of membrane solubilising polymers. These new polymers can solubilise membranes under a wide range of conditions and produce varied sizes of nanoparticles, yet there has been a lack of broad comparison between the common polymer types and solubilising conditions. Here, we present a comparative study on the three most common commercial polymers: SMA 3:1, SMA 2:1, and DIBMA. Additionally, this work presents, for the first time, a comparative characterisation of polymethacrylate copolymer (PMA). Absorbance and dynamic light scattering measurements were used to evaluate solubilisation across key buffer conditions in a simple, adaptable assay format that looked at pH, salinity, and divalent cation concentration. Lipid-polymer nanoparticles formed from SMA variants were found to be the most susceptible to buffer effects, with nanoparticles from either zwitterionic DMPC or POPC:POPG (3:1) bilayers only forming in low to moderate salinity (< 600 mM NaCl) and above pH 6. DIBMA-lipid nanoparticles could be formed above a pH of 5 and were stable in up to 4 M NaCl. Similarly, PMA-lipid nanoparticles were stable in all NaCl concentrations tested (up to 4 M) and a broad pH range (3-10). However, for both DIBMA and PMA nanoparticles there is a severe penalty observed for bilayer solubilisation in non-optimal conditions or when using a charged membrane. Additionally, lipid fluidity of the DMPC-polymer nanoparticles was analysed through cw-EPR, showing no cooperative gel-fluid transition as would be expected for native-like lipid membranes.

Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications

Membrane proteins mediate a wide spectrum of biological processes, such as signal transduction and cell communication. Due to the arduous and costly nature inherent to the experimental process, membrane proteins have long been devoid of well-resolved atomic-level tertiary structures and, consequently, the understanding of their functional roles underlying a multitude of life activities has been hampered. Currently, computational tools dedicated to furthering the structure-function understanding are primarily focused on utilizing intelligent algorithms to address a variety of site-wise prediction problems (e.g., topology and interaction sites), but are scattered across different computing sources. Moreover, the recent advent of deep learning techniques has immensely expedited the development of computational tools for membrane protein-related prediction problems. Given the growing number of applications optimized particularly by manifold deep neural networks, we herein provide a review on the current status of computational strategies mainly in membrane protein type classification, topology identification, interaction site detection, and pathogenic effect prediction. Meanwhile, we provide an overview of how the entire prediction process proceeds, including database collection, data pre-processing, feature extraction, and method selection. This review is expected to be useful for developing more extendable computational tools specific to membrane proteins.

Diagnostic models and predictive drugs associated with cuproptosis hub genes in Alzheimer's disease

Alzheimer's disease (AD) is a chronic neurodegenerative disease, and its underlying genes and treatments are unclear. Abnormalities in copper metabolism can prevent the clearance of β-amyloid peptides and promote the progression of AD pathogenesis. Therefore, the present study used a bioinformatics approach to perform an integrated analysis of the hub gene based on cuproptosis that can influence the diagnosis and treatment of AD. The gene expression profiles were obtained from the Gene Expression Omnibus database, including non-demented (ND) and AD samples. A total of 2,977 cuproptosis genes were retrieved from published articles. The seven hub genes associated with cuproptosis and AD were obtained from the differentially expressed genes and WGCNA in brain tissue from GSE33000. The GO analysis demonstrated that these genes were involved in phosphoribosyl pyrophosphate, lipid, and glucose metabolism. By stepwise regression and logistic regression analysis, we screened four of the seven cuproptosis genes to construct a diagnostic model for AD, which was validated by GES15222, GS48350, and GSE5281. In addition, immune cell infiltration of samples was investigated for correlation with these hub genes. We identified six drugs targeting these seven cuproptosis genes in DrugBank. Hence, these cuproptosis gene signatures may be an important prognostic indicator for AD and may offer new insights into treatment options.

Direct cell extraction of membrane proteins for structure-function analysis

Membrane proteins are the largest group of therapeutic targets in a variety of disease areas and yet, they remain particularly difficult to investigate. We have developed a novel one-step approach for the incorporation of membrane proteins directly from cells into lipid Salipro nanoparticles. Here, with the pannexin1 channel as a case study, we demonstrate the applicability of this method for structure-function analysis using SPR and cryo-EM.

Functional antibody delivery: Advances in cellular manipulation

The current therapeutic antibody market in the U.S. consists of 100 antibody-based products and their market value is expected to explode beyond $300 billion by 2025. These therapies are presently limited to extracellular targets due to the innate inability of antibodies to transverse membranes. To expand the number of accessible therapeutic targets, intracellular antibody delivery is necessary. Many delivery vehicles for antibodies have been used with some promising results, such as nanoparticles and cell penetrating polymers. Despite the success of these delivery platforms using model antibody cargo, there is a surprisingly small number of studies that focus on functional antibody delivery into the cytosol that also measures a cellular response. Antibodies can be designed for essentially unlimited targets, including proteins and DNA, that will ultimately control cell function once delivered inside cells. Advancement in cellular manipulation depends on the application of intracellularly delivering functional antibodies to achieve a desired result. This review focuses on the emerging field of functional antibody delivery which enables various cellular responses and cell manipulation.

A unique hyperdynamic dimer interface permits small molecule perturbation of the melanoma oncoprotein MITF for melanoma therapy

Microphthalmia transcription factor (MITF) regulates melanocyte development and is the "lineage-specific survival" oncogene of melanoma. MITF is essential for melanoma initiation, progression, and relapse and has been considered an important therapeutic target; however, direct inhibition of MITF through small molecules is considered impossible, due to the absence of a ligand-binding pocket for drug design. Here, our structural analyses show that the structure of MITF is hyperdynamic because of its out-of-register leucine zipper with a 3-residue insertion. The dynamic MITF is highly vulnerable to dimer-disrupting mutations, as we observed that MITF loss-of-function mutations in human Waardenburg syndrome type 2 A are frequently located on the dimer interface and disrupt the dimer forming ability accordingly. These observations suggest a unique opportunity to inhibit MITF with small molecules capable of disrupting the MITF dimer. From a high throughput screening against 654,650 compounds, we discovered compound TT-012, which specifically binds to dynamic MITF and destroys the latter's dimer formation and DNA-binding ability. Using chromatin immunoprecipitation assay and RNA sequencing, we showed that TT-012 inhibits the transcriptional activity of MITF in B16F10 melanoma cells. In addition, TT-012 inhibits the growth of high-MITF melanoma cells, and inhibits the tumor growth and metastasis with tolerable toxicity to liver and immune cells in animal models. Together, this study demonstrates a unique hyperdynamic dimer interface in melanoma oncoprotein MITF, and reveals a novel approach to therapeutically suppress MITF activity.

Next-generation retinoid X receptor agonists increase ATRA signaling in organotypic epithelium cultures and have distinct effects on receptor dynamics

Retinoid X receptors (RXRs) are nuclear transcription factors that partner with other nuclear receptors to regulate numerous physiological processes. Although RXR represents a valid therapeutic target, only a few RXR-specific ligands (rexinoids) have been identified, in part due to the lack of clarity on how rexinoids selectively modulate RXR response. Previously, we showed that rexinoid UAB30 potentiates all-trans-retinoic acid (ATRA) signaling in human keratinocytes, in part by stimulating ATRA biosynthesis. Here, we examined the mechanism of action of next-generation rexinoids UAB110 and UAB111 that are more potent in vitro than UAB30 and the FDA-approved Targretin. Both UAB110 and UAB111 enhanced ATRA signaling in human organotypic epithelium at a 50-fold lower concentration than UAB30. This was consistent with the 2- to 5- fold greater increase in ATRA in organotypic epidermis treated with UAB110/111 versus UAB30. Furthermore, at 0.2 μM, UAB110/111 increased the expression of ATRA genes up to 16-fold stronger than Targretin. The less toxic and more potent UAB110 also induced more changes in differential gene expression than Targretin. Additionally, our hydrogen deuterium exchange mass spectrometry analysis showed that both ligands reduced the dynamics of the ligand-binding pocket but also induced unique dynamic responses that were indicative of higher affinity binding relative to UAB30, especially for Helix 3. UAB110 binding also showed increased dynamics towards the dimer interface through the Helix 8 and Helix 9 regions. These data suggest that UAB110 and UAB111 are potent activators of RXR-RAR signaling pathways but accomplish activation through different molecular responses to ligand binding.

ABC-transporter CFTR folds with high fidelity through a modular, stepwise pathway

The question how proteins fold is especially pointed for large multi-domain, multi-spanning membrane proteins with complex topologies. We have uncovered the sequence of events that encompass proper folding of the ABC transporter CFTR in live cells by combining kinetic radiolabeling with protease-susceptibility assays. We found that CFTR folds in two clearly distinct stages. The first, co-translational, stage involves folding of the 2 transmembrane domains TMD1 and TMD2, plus one nucleotide-binding domain, NBD1. The second stage is a simultaneous, post-translational increase in protease resistance for both TMDs and NBD2, caused by assembly of these domains onto NBD1. Our assays probe every 2-3 residues (on average) in CFTR. This in-depth analysis at amino-acid level allows detailed analysis of domain folding and importantly also the next level: assembly of the domains into native, folded CFTR. Defects and changes brought about by medicines, chaperones, or mutations also are amenable to analysis. We here show that the well-known disease-causing mutation F508del, which established cystic fibrosis as protein-folding disease, caused co-translational misfolding of NBD1 but not TMD1 nor TMD2 in stage 1, leading to absence of stage-2 folding. Corrector drugs rescued stage 2 without rescuing NBD1. Likewise, the DxD motif in NBD1 that was identified to be required for export of CFTR from the ER we found to be required already upstream of export as CFTR mutated in this motif phenocopies F508del CFTR. The highly modular and stepwise folding process of such a large, complex protein explains the relatively high fidelity and correctability of its folding.

Surfaceome mapping of primary human heart cells with CellSurfer uncovers cardiomyocyte surface protein LSMEM2 and proteome dynamics in failing hearts

Cardiac cell surface proteins are drug targets and useful biomarkers for discriminating among cellular phenotypes and disease states. Here we developed an analytical platform, CellSurfer, that enables quantitative cell surface proteome (surfaceome) profiling of cells present in limited quantities, and we apply it to isolated primary human heart cells. We report experimental evidence of surface localization and extracellular domains for 1,144 N-glycoproteins, including cell-type-restricted and region-restricted glycoproteins. We identified a surface protein specific for healthy cardiomyocytes, LSMEM2, and validated an anti-LSMEM2 monoclonal antibody for flow cytometry and imaging. Surfaceome comparisons among pluripotent stem cell derivatives and their primary counterparts highlighted important differences with direct implications for drug screening and disease modeling. Finally, 20% of cell surface proteins, including LSMEM2, were differentially abundant between failing and non-failing cardiomyocytes. These results represent a rich resource to advance development of cell type and organ-specific targets for drug delivery, disease modeling, immunophenotyping and in vivo imaging.

Cell Surface Biotinylation Using Furan Cross-Linking Chemistry

A detailed study of the cellular surfaceome poses major challenges for mass spectrometry analysis. Surface proteins are low abundant compared to intracellular proteins, and their inefficient extraction in aqueous medium leads to their aggregation and precipitation. To tackle such problems, surface biotinylation is frequently used to tag surface proteins with biotin, allowing for their enrichment, leading to a more sensitive mapping of surface proteomes. We here detail a new surface biotinylation protocol based on furan-biotin affinity purification to enrich plasma membrane proteins for proteomics. This protocol involves biotinylation of cell surface membrane proteins on viable cells, followed by affinity enrichment using streptavidin beads, trypsin digestion, peptide cleanup, and LC-MS/MS analysis.

Deciphering the importance of MD descriptors in designing Vitamin D Receptor agonists and antagonists using machine learning

The Vitamin D Receptor (VDR) ligand-binding domain undergoes conformation change upon the binding of VDR agonists/antagonists. Helix 12 ((H)12) is one of the important helices at VDR ligand binding and its conformational changes are controlled by the binding of agonists and antagonists molecules. Various molecular modeling studies are available to explain the agonistic and antagonistic activity of vitamin D analogs. In this work, for the first time, we attempted to generate a machine learning model with fingerprints, 2D, 3D and MD descriptors that are specific to Vitamin D analogs and VDR. Initially, 2D and 3D descriptors and fingerprints of 1003 vitamin D analogs were calculated using CDK and RDKit. The machine learning model was generated using descriptors and fingerprints. Further, 80 Vitamin D analogs (40 VDR agonists + 40 VDR antagonists) were docked in the VDR active site. 50ns MD simulation was performed for each protein-ligand complex. Different MD descriptors such as Solvent Accessible Surface Area (SASA), radius of gyration, PC1 and PC2 were calculated and considered along with CDK and RDKit descriptors as features for machine learning calculations. A few other descriptors that are related to VDR conformational changes such as conformation of the (H)12, the angle at kink were considered for machine learning model generation. It was observed that the descriptors calculated from VDR conformational changes i) were able to distinguish between agonists and antagonists ii) provide key and comprehensive information about the unique binding characteristics of agonists and antagonists iii) provide a strong basis for the machine learning model generation. Overall, this study attempts the utilization of descriptors that are specific to a protein conformation will be helpful for the generation of an efficient machine learning model.

Drug delivery challenges and formulation aspects of proteolysis targeting chimera (PROTACs)

Proteolysis targeting chimeras (PROTACs) have been extensively explored for targeted proteasomal degradation of disease-related proteins with enormous potential in the treatment of intractable diseases. However, PROTACs are poorly soluble and permeable bulky molecules facing several bioavailability challenges irrespective of the route of administration. Our review lays out crucial challenges in the delivery of target protein degraders and nanoformulation approaches to overcome physicochemical and biological hurdles that can aid in transporting these target-protein degraders to the disease site. We have elaborated on the current formulation approaches and further highlighted the prospective delivery strategies that could be probed for disease-specific targeted delivery of PROTACs.

Nuclear Receptor Imaging In Vivo-Clinical and Research Advances

Nuclear receptors are transcription factors that function in normal physiology and play important roles in diseases such as cancer, inflammation, and diabetes. Noninvasive imaging of nuclear receptors can be achieved using radiolabeled ligands and positron emission tomography (PET). This quantitative imaging approach can be viewed as an in vivo equivalent of the classic radioligand binding assay. A main clinical application of nuclear receptor imaging in oncology is to identify metastatic sites expressing nuclear receptors that are targets for approved drug therapies and are capable of binding ligands to improve treatment decision-making. Research applications of nuclear receptor imaging include novel synthetic ligand and drug development by quantifying target drug engagement with the receptor for optimal therapeutic drug dosing and for fundamental research into nuclear receptor function in cells and animal models. This mini-review provides an overview of PET imaging of nuclear receptors with a focus on radioligands for estrogen receptor, progesterone receptor, and androgen receptor and their use in breast and prostate cancer.

Sensitive and Specific Detection of Estrogens Featuring Doped Silicon Nanowire Arrays

Estrogens and estrogen-mimicking compounds in the aquatic environment are known to cause negative impacts to both ecosystems and human health. In this initial proof-of-principle study, we developed a novel vertically oriented silicon nanowire (vSiNW) array-based biosensor for low-cost, highly sensitive and selective detection of estrogens. The vSiNW arrays were formed using an inexpensive and scalable metal-assisted chemical etching (MACE) process. A vSiNW array-based p-n junction diode (vSiNW-diode) transducer design for the biosensor was used and functionalized via 3-aminopropyltriethoxysilane (APTES)-based silane chemistry to bond estrogen receptor-alpha (ER-α) to the surface of the vSiNWs. Following receptor conjugation, the biosensors were exposed to increasing concentrations of estradiol (E2), resulting in a well-calibrated sensor response (R ² ≥ 0.84, 1-100 ng/mL concentration range). Fluorescence measurements quantified the distribution of estrogen receptors across the vSiNW array compared to planar Si, indicating an average of 7 times higher receptor presence on the vSiNW array surface. We tested the biosensor's target selectivity by comparing it to another estrogen (estrone [E1]) and an androgen (testosterone), where we measured a high positive electrical biosensor response after E1 exposure and a minimal response after testosterone. The regeneration capacity of the biosensor was tested following three successive rinses with phosphate buffer solution (PBS) between hormone exposure. Traditional horizontally oriented Si NW field effect transistor (hSiNW-FET)-based biosensors report electrical current changes at the nanoampere (nA) level that require bulky and expensive measurement equipment making them unsuitable for field measurements, whereas the reported vSiNW-diode biosensor exhibits current changes in the microampere (μA) range, demonstrating up to 100-fold electrical signal amplification, thus enabling sensor signal measurement using inexpensive electronics. The highly sensitive and specific vSiNW-diode biosensor developed here will enable the creation of low-cost, portable, field-deployable biosensors that can detect estrogenic compounds in waterways in real-time.

Structural Flexibility of Proteins Dramatically Alters Membrane Stability─A Novel Aspect of Lipid-Protein Interaction

Protein isoforms are structural variants with changes in the overall flexibility predominantly at the tertiary level. For membrane associated proteins, such structural flexibility or rigidity affects membrane stability by playing modulatory roles in lipid-protein interaction. Herein, we investigate the protein chain flexibility mediated changes in the mechanistic behavior of phospholipid model membranes in the presence of two well-known isoforms, erythroid (ER) and nonerythroid (NER) spectrin. We show dramatic alterations of membrane elasticity and stability induced by spectrin in the Langmuir monolayers of phosphatidylocholine (PC) and phosphatidylethanolamine (PE) by a combination of isobaric relaxation, surface pressure-area isotherm, X-ray scattering, and microscopy measurements. The NER spectrin drives all monolayers to possess an approximately equal stability, and that required 25-fold increase and 5-fold decrease of stability in PC and PE monolayers, respectively. The untilting transition of the PC membrane in the presence of NER spectrin observed in X-ray measurements can explain better membrane packing and stability.

Use of Cheminformatics to Determine Potential Drug Interactions between Popular Barbadian Botanical Medicines and Antihypertensive Drugs

Barbados has a rich traditional use of medicinal plants, especially among the older population who may have a chronic noncommunicable disease. This study aims to identify possible drug-herb interactions between popular herbal remedies used to manage elevated blood pressure and conventional antihypertensive drugs. In this study, in silico molecular docking experiments with AutoDock Vina (Scripps Research Institute, La Jolla, CA), a part of Yasara Structure software, version 20.12.24, were used to screen 30 potential phytochemicals for drug interactions from 11 popular plants in Barbados that are used for high blood pressure and could influence the pharmacology of the most prescribed antihypertensive drugs in Barbados. Thiazide and thiazide-like diuretics, calcium channel blockers (CCBs), angiotensin-converting enzyme inhibitors (ACE-I), and angiotensin receptor blockers (ARBs) are the most prescribed antihypertensive drugs. Twenty-seven phytochemicals show dissociation constants (K d) < 10 μM with pharmacological drug targets. Catharanthus roseus (L.) G. Don, Phyllanthus niruri L., Petroselinum crispum (Mill.) Fuss, and Lantana involucrata L. contain various compounds that show high binding affinities in all experiments. Possible interactions could affect renal excretion (thiazide-like diuretics), CYP metabolism (CCBs), absorption (ACE-I), hepatic CYP, and phase II metabolism (ARB). Oleanolic acid shows high binding affinities to almost all protein targets. This study also reveals potential candidates for the drug targets: T-type Cav3.3 (psychiatric diseases), PEPT1/2 (influencing bioavailability), and BK channel (epilepsy). Twenty-seven of 30 phytochemicals from C. roseus (L.) G. Don (Madagascar periwinkle), P. niruri L. (Seed under leaf), P. crispum Mill. Fuss (Parsley), and L. involucrata L. (Rock sage) have potential binding affinities with pharmacological targets of frequently prescribed antihypertensive drugs in Barbados and are likely to cause drug interactions. Compounds that are similar to naringin (e.g., astragalin, rutin, and quercitrin) and compounds that bind to OATP1, PEPT1/2, and enzymes involved in the metabolism of CCBs may be clinically relevant for further research. There should be greater awareness of potential drug-herb interactions, and further in vitro and in vivo studies are needed to unravel the exact effects on the pharmacology.

Transporter and metabolizer gene polymorphisms affect fluoroquinolone pharmacokinetic parameters

Tuberculosis (TB) is an infectious disease that occurs globally. Treatment of TB has been hindered by problems with multidrug-resistant strains (MDR-TB). Fluoroquinolones are one of the main drugs used for the treatment of MDR-TB. The success of therapy can be influenced by genetic factors and their impact on pharmacokinetic parameters. This review was conducted by searching the PubMed database with keywords polymorphism and fluoroquinolones. The presence of gene polymorphisms, including UGT1A1, UGT1A9, SLCO1B1, and ABCB1, can affect fluoroquinolones pharmacokinetic parameters such as area under the curve (AUC), creatinine clearance (C_Cr), maximum plasma concentration (C_max), half-life (t_1/2) and peak time (t_max) of fluoroquinolones.

Transition-Linker Containing Detergents for Membrane Protein Studies

It is a pressing need, but still challenging to explore the structure and function of membrane proteins (MPs). One of the main obstacles is the limited availability of matched detergents for the handling of specific MPs. We describe herein the design of new detergents by incorporation of a transition linker between the hydrophilic head and the hydrophobic tail. This design allows a gradual change of hydrophobicity between the outside and inside of micelles, in contrast to the abrupt switch in conventional detergents. Notably, many of these detergents assembled into micelles in while retaining low critical micelle concentrations. Meanwhile, thermal stabilizing evaluation identified superior detergents for representative MPs, including G protein-coupled receptors and a transporter protein. Among them, further improved the NMR study of MPs. We anticipate these that results will encourage future detergent expansion through new remodeling on the traditional detergent scaffold.

P. Ala278Val mutation might cause a pathogenic defect in HEXB folding leading to the Sandhoff disease

Sandhoff disease is a rare neurodegenerative and autosomal recessive disorder, which is characterized by a defect in ganglioside metabolism. Also, it is caused by mutations in the HEXB gene for the β-subunit isoform 1 of β-N-acetyl hexosaminidase. In the present study, an Iranian 14- month -old girl with 8- month history of unsteady walking and involuntary movements was described. In this regard, biochemical testing showed some defects in the normal activity of beta-hexosaminidase protein. Following sequencing of HEXB gene, a homozygous c.833C > T mutation was identified in the patient's genome. After recognition of p.A278V, several different in silico methods were used to assess the mutant protein stability, ranging from mutation prediction methods to ligand docking. The p.A278V mutation might be disruptive because of changing the three-dimensional folding at the end of the 5th alpha helix. According to the medical prognosis, in silico and structural analyses, it was predicted to be disease cause.

Comprehensive bioinformatics analysis reveals biomarkers of DNA methylation-related genes in varicose veins

Background: Patients with Varicose veins (VV) show no obvious symptoms in the early stages, and it is a common and frequent clinical condition. DNA methylation plays a key role in VV by regulating gene expression. However, the molecular mechanism underlying methylation regulation in VV remains unclear. Methods: The mRNA and methylation data of VV and normal samples were obtained from the Gene Expression Omnibus (GEO) database. Methylation-Regulated Genes (MRGs) between VV and normal samples were crossed with VV-associated genes (VVGs) obtained by weighted gene co-expression network analysis (WGCNA) to obtain VV-associated MRGs (VV-MRGs). Their ability to predict disease was assessed using receiver operating characteristic (ROC) curves. Biomarkers were then screened using a random forest model (RF), support vector machine model (SVM), and generalized linear model (GLM). Next, gene set enrichment analysis (GSEA) was performed to explore the functions of biomarkers. Furthermore, we also predicted their drug targets, and constructed a competing endogenous RNAs (ceRNA) network and a drug target network. Finally, we verified their mRNA expression using quantitative real-time polymerase chain reaction (qRT-PCR). Results: Total three VV-MRGs, namely Wnt1-inducible signaling pathway protein 2 (WISP2), Cysteine-rich intestinal protein 1 (CRIP1), and Odd-skipped related 1 (OSR1) were identified by VVGs and MRGs overlapping. The area under the curves (AUCs) of the ROC curves for these three VV-MRGs were greater than 0.8. RF was confirmed as the optimal diagnostic model, and WISP2, CRIP1, and OSR1 were regarded as biomarkers. GSEA showed that WISP2, CRIP1, and OSR1 were associated with oxidative phosphorylation, extracellular matrix (ECM), and respiratory system functions. Furthermore, we found that lncRNA MIR17HG can regulate OSR1 by binding to hsa-miR-21-5p and that PAX2 might treat VV by targeting OSR1. Finally, qRT-PCR results showed that the mRNA expression of the three genes was consistent with the results of the datasets. Conclusion: This study identified WISP2, CRIP1, and OSR1 as biomarkers of VV through comprehensive bioinformatics analysis, and preliminary explored the DNA methylation-related molecular mechanism in VV, which might be important for VV diagnosis and exploration of potential molecular mechanisms.

Construction of a synthetic methodology-based library and its application in identifying a GIT/PIX protein-protein interaction inhibitor

In recent years, the flourishing of synthetic methodology studies has provided concise access to numerous molecules with new chemical space. These compounds form a large library with unique scaffolds, but their application in hit discovery is not systematically evaluated. In this work, we establish a synthetic methodology-based compound library (SMBL), integrated with compounds obtained from our synthetic researches, as well as their virtual derivatives in significantly larger scale. We screen the library and identify small-molecule inhibitors to interrupt the protein-protein interaction (PPI) of GIT1/β-Pix complex, an unrevealed target involved in gastric cancer metastasis. The inhibitor 14-5-18 with a spiro[bicyclo[2.2.1]heptane-2,3'-indolin]-2'-one scaffold, considerably retards gastric cancer metastasis in vitro and in vivo. Since the PPI targets are considered undruggable as they are hard to target, the successful application illustrates the structural specificity of SMBL, demonstrating its potential to be utilized as compound source for more challenging targets.

How Carvedilol activates β2-adrenoceptors

Carvedilol is among the most effective β-blockers for improving survival after myocardial infarction. Yet the mechanisms by which carvedilol achieves this superior clinical profile are still unclear. Beyond blockade of β1-adrenoceptors, arrestin-biased signalling via β2-adrenoceptors is a molecular mechanism proposed to explain the survival benefits. Here, we offer an alternative mechanism to rationalize carvedilol's cellular signalling. Using primary and immortalized cells genome-edited by CRISPR/Cas9 to lack either G proteins or arrestins; and combining biological, biochemical, and signalling assays with molecular dynamics simulations, we demonstrate that G proteins drive all detectable carvedilol signalling through β2ARs. Because a clear understanding of how drugs act is imperative to data interpretation in basic and clinical research, to the stratification of clinical trials or to the monitoring of drug effects on the target pathway, the mechanistic insight gained here provides a foundation for the rational development of signalling prototypes that target the β-adrenoceptor system.

De novo transcriptome assembly and identification of G-Protein-Coupled-Receptors (GPCRs) in two species of monogenean parasites of fish

Genomic resources for Platyhelminthes of the class Monogenea are scarce, despite the diversity of these parasites, some species of which are highly pathogenic to their fish hosts. This work aimed to generate de novo-assembled transcriptomes of two monogenean species, Scutogyrus longicornis (Dactylogyridae) and Rhabdosynochus viridisi (Diplectanidae), providing a protocol for cDNA library preparation with low input samples used in single cell transcriptomics. This allowed us to work with sub-microgram amounts of total RNA with success. These transcriptomes consist of 25,696 and 47,187 putative proteins, respectively, which were further annotated according to the Swiss-Prot, Pfam, GO, KEGG, and COG databases. The completeness values of these transcriptomes evaluated with BUSCO against Metazoa databases were 54.1% and 73%, respectively, which is in the range of other monogenean species. Among the annotations, a large number of terms related to G-protein-coupled receptors (GPCRs) were found. We identified 109 GPCR-like sequences in R. viridisi, and 102 in S. longicornis, including family members specific for Platyhelminthes. Rhodopsin was the largest family according to GRAFS classification. Two putative melatonin receptors found in S. longicornis represent the first record of this group of proteins in parasitic Platyhelminthes. Forty GPCRs of R. viridisi and 32 of S. longicornis that were absent in Vertebrata might be potential drug targets. The present study provides the first publicly available transcriptomes for monogeneans of the subclass Monopisthocotylea, which can serve as useful genomic datasets for functional genomic research of this important group of parasites.

The Influence of Cationic Nitrosyl Iron Complex with Penicillamine Ligands on Model Membranes, Membrane-Bound Enzymes and Lipid Peroxidation

This paper shows the biological effects of cationic binuclear tetranitrosyl iron complex with penicillamine ligands (TNIC-PA). Interaction with a model membrane was assessed using a fluorescent probes technique. Antioxidant activity was studied using a thiobarbituric acid reactive species assay (TBARS) and a chemiluminescence assay. The catalytic activity of monoamine oxidase (MAO) was determined by measuring liberation of ammonia. Antiglycation activity was determined fluometrically by thermal glycation of albumine by D-glucose. The higher values of Stern-Volmer constants (K_SV) obtained for the pyrene located in hydrophobic regions (3.9 × 10⁴ M^-1) compared to K_SV obtained for eosin Y located in the polar headgroup region (0.9 × 10⁴ M^-1) confirms that TNIC-PA molecules prefer to be located in the hydrophobic acyl chain region, close to the glycerol group of lipid molecules. TNIC-PA effectively inhibited the process of spontaneous lipid peroxidation, due to additive contributions from releasing NO and penicillamine ligand (IC50 = 21.4 µM) and quenched luminol chemiluminescence (IC50 = 3.6 μM). High activity of TNIC-PA in both tests allows us to assume a significant role of its radical-scavenging activity in the realization of antioxidant activity. It was shown that TNIC-PA (50-1000 μM) selectively inhibits the membrane-bound enzyme MAO-A, a major source of ROS in the heart. In addition, TNIC-PA is an effective inhibitor of non-enzymatic protein glycation. Thus, the evaluated biological effects of TNIC-PA open up the possibility of its practical application in chemotherapy for socially significant diseases, especially cardiovascular diseases.

Large-Scale Crystallographic Fragment Screening Expedites Compound Optimization and Identifies Putative Protein-Protein Interaction Sites

The identification of starting points for compound development is one of the key steps in early-stage drug discovery. Information-rich techniques such as crystallographic fragment screening can potentially increase the efficiency of this step by providing the structural information of the binding mode of the ligands in addition to the mere binding information. Here, we present the crystallographic screening of our 1000-plus-compound F2X-Universal Library against the complex of the yeast spliceosomal Prp8 RNaseH-like domain and the snRNP assembly factor Aar2. The observed 269 hits are distributed over 10 distinct binding sites on the surface of the protein-protein complex. Our work shows that hit clusters from large-scale crystallographic fragment screening campaigns identify known interaction sites with other proteins and suggest putative additional interaction sites. Furthermore, the inherent binding pose validation within the hit clusters may accelerate downstream compound optimization.

PacDOCK: A Web Server for Positional Distance-Based and Interaction-Based Analysis of Docking Results

Molecular docking is a key method for structure-based drug design used to predict the conformations assumed by small drug-like ligands when bound to their target. However, the evaluation of molecular docking studies can be hampered by the lack of a free and easy to use platform for the complete analysis of results obtained by the principal docking programs. To this aim, we developed PacDOCK, a freely available and user-friendly web server that comprises a collection of tools for positional distance-based and interaction-based analysis of docking results, which can be provided in several file formats. PacDOCK allows a complete analysis of molecular docking results through root mean square deviation (RMSD) calculation, molecular visualization, and cluster analysis of docked poses. The RMSD calculation compares docked structures with a reference structure, also when atoms are randomly labelled, and their conformational and positional differences can be visualised. In addition, it is possible to visualise a ligand into the target binding pocket and investigate the key receptor–ligand interactions. Moreover, PacDOCK enables the clustering of docking results by identifying a restrained number of clusters from many docked poses. We believe that PacDOCK will contribute to facilitating the analysis of docking results to improve the efficiency of computer-aided drug design.

Medium-Chain Lipid Conjugation Facilitates Cell-Permeability and Bioactivity

The majority of bioactive molecules act on membrane proteins or intracellular targets and therefore needs to partition into or cross biological membranes. Natural products often exhibit lipid modifications to facilitate critical molecule-membrane interactions, and in many cases their bioactivity is markedly reduced upon removal of a lipid group. However, despite its importance in nature, lipid-conjugation of small molecules is not commonly used in chemical biology and medicinal chemistry, and the effect of such conjugation has not been systematically studied. To understand the composition of lipids found in natural products, we carried out a chemoinformatic characterization of the "natural product lipidome". According to this analysis, lipidated natural products predominantly contain saturated medium-chain lipids (MCLs), which are significantly shorter than the long-chain lipids (LCLs) found in membranes and lipidated proteins. To study the usefulness of such modifications in probe design, we systematically explored the effect of lipid conjugation on five different small molecule chemotypes and find that permeability, cellular retention, subcellular localization, and bioactivity can be significantly modulated depending on the type of lipid tail used. We demonstrate that MCL conjugation can render molecules cell-permeable and modulate their bioactivity. With all explored chemotypes, MCL-conjugates consistently exhibited superior uptake or bioactivity compared to LCL-conjugates and either comparable or superior uptake or bioactivity to short-chain lipid (SCL)-conjugates. Together, our findings suggest that conjugation of small molecules with MCLs could be a powerful strategy for the design of probes and drugs.

Three-stage model of helical membrane protein folding: Role of membrane-water interface as the intermediate stage vestibule for TM helices during their in membrano assembly

Integral membrane proteins (MPs) are dominated by transmembrane α-helical (TMH) proteins playing critical roles in cellular signaling processes. These proteins display a wide range of sizes from one TMH domain to at least 26 TMH domains and diverse structural folds. A common feature of most of these folds is the TM orientation of the helical domains and the approximately parallel packing of these domains into helical bundles of varying stability, however, it has been challenging to study the folding of these proteins experimentally. The contribution of helix stabilization in membrane and interface to the folding energy landscape are investigated here for the full range of TMH protein sizes containing 1 TM domain (1-TMH protein) to 24 TM domains (24-TMH protein) for all TMH proteins with available structures using structural bioinformatics based hydropathy analysis. The TM helix insertion stabilization energies from Water to membrane-water Interface (WAT→INT energies) are on average half of those insertion energies from water to transmembrane orientation (WAT→TM energies) for the whole polytopic helical membrane proteome (1-TMH to 24-TMH proteins). This suggests a potentially dominant role of the membrane-water interface as a viable holding vestibule for the TM helices during their release from the translocon. This provides proteome-level evidence for the broadly applicable four-step thermodynamic framework by White and co-workers as well as a natural extension of Popot and Engelman's original two-stage model of helical MP folding to a three-stage model, where, in the new intermediate stage, the membrane-water interface acts as a holding vestibule for the translated TM helices, reconciling the interface's critical role in MP folding seen in many previous studies. Support for this model is provided by showing the stability of hydrophobic TM helices at the membrane-water interface through several microsecond long molecular dynamics simulations of five hydrophobic helical domains and a helical hairpin pre-folded from the ribosomal exit vestibule.

Time Matters - In vitro Cellular Disposition Kinetics Help Rationalizing Cellular Potency Disconnects

Loss in potency is commonly observed in early drug discovery when moving from biochemical to more complex cellular systems. Among other factors, low permeability is often considered to cause such potency disconnects.We developed a novel cellular disposition assay in MDCK cells to determine passive uptake clearance (PS_inf), cell-to-medium ratios at steady-state (Kp) and the time to reach 90% steady-state (TTSS₉₀) from a single experiment in a high-throughput format.The assay was validated using 40 marketed drugs, showing a wide distribution of PS_inf and K_p values. The parameters generally correlated with transcellular permeability and lipophilicity, while PS_inf data revealed better resolution in the high and low permeability ranges compared to traditional permeability data. A linear relationship between the K_p/PS_inf ratio and TTSS₉₀ was mathematically derived and experimentally validated, demonstrating the dependency of TTSS₉₀ on the rate and extent of cellular accumulation.Cellular disposition parameters could explain potency (IC₅₀) disconnects noted for seven Bruton's tyrosine kinase degrader compounds in a cellular potency assay. In contrast to transcellular permeability, PS_inf data enabled identification of the compounds with IC₅₀ disconnects based on their time to reach equilibrium. Overall, the novel assay offers the possibility to address potency disconnects in early drug discovery.

Putting on molecular weight: Enabling cryo-EM structure determination of sub-100-kDa proteins

Significant advances in the past decade have enabled high-resolution structure determination of a vast variety of proteins by cryogenic electron microscopy single particle analysis. Despite improved sample preparation, next-generation imaging hardware, and advanced single particle analysis algorithms, small proteins remain elusive for reconstruction due to low signal-to-noise and lack of distinctive structural features. Multiple efforts have therefore been directed at the development of size-increase techniques for small proteins. Here we review the latest methods for increasing effective molecular weight of proteins <100 ​kDa through target protein binding or target protein fusion - specifically by using nanobody-based assemblies, fusion tags, and symmetric scaffolds. Finally, we summarize these state-of-the-art techniques into a decision-tree to facilitate the design of tailored future approaches, and thus for further exploration of ever-smaller proteins that make up the largest part of the human genome.

Pharmacometabolomics-Based Translational Biomarkers: How to Navigate the Data Ocean

Metabolome is the end point of the genome-environment interplay, and enables an important holistic overview of individual adaptability and host responses to environmental, ecological, as well as endogenous changes such as disease. Pharmacometabolomics is the application of metabolome knowledge to decipher the mechanisms of interindividual and intraindividual variations in drug efficacy and safety. Pharmacometabolomics also contributes to prediction of drug treatment outcomes on the basis of baseline (predose) and postdose metabotypes through mathematical modeling. Thus, pharmacometabolomics is a strong asset for a diverse community of stakeholders interested in theory and practice of evidence-based and precision/personalized medicine: academic researchers, public health scholars, health professionals, pharmaceutical, diagnostics, and biotechnology industries, among others. In this expert review, we discuss pharmacometabolomics in four contexts: (1) an interdisciplinary omics tool and field to map the mechanisms and scale of interindividual variability in drug effects, (2) discovery and development of translational biomarkers, (3) advance digital biomarkers, and (4) empower drug repurposing, a field that is increasingly proving useful in the current era of Covid-19. As the applications of pharmacometabolomics are growing rapidly in the current postgenome era, next-generation proteomics and metabolomics follow the example of next-generation sequencing analyses. Pharmacometabolomics can also empower data reliability and reproducibility through multiomics integration strategies, which use each data layer to correct, connect with, and inform each other. Finally, we underscore here that contextual data remain crucial for precision medicine and drug development that stand the test of time and clinical relevance.

Azide- and diazirine-modified membrane lipids: Physicochemistry and applicability to study peptide/lipid interactions via cross-linking/mass spectrometry

Although the incorporation of photo-activatable lipids into membranes potentially opens new avenues for studying interactions with peptides and proteins, the question of whether azide- or diazirine-modified lipids are suitable for such studies remains controversial. We have recently shown that diazirine-modified lipids can indeed form cross-links to membrane peptides after UV activation and that these cross-links can be precisely determined in their position by mass spectrometry (MS). However, we also observed an unexpected backfolding of the lipid's diazirine-containing stearoyl chain to the membrane interface challenging the potential application of this modified lipid for future cross-linking (XL)-MS studies of protein/lipid interactions. In this work, we compared an azide- (AzidoPC) and a diazirine-modified (DiazPC) membrane lipid regarding their self-assembly properties, their mixing behavior with saturated bilayer-forming phospholipids, and their reactivity upon UV activation using differential scanning calorimetry (DSC), dynamic light scattering (DLS), small-angle X-ray scattering (SAXS), transmission electron microscopy (TEM), and MS. Mixtures of both modified lipids with DMPC were further used for photo-chemically induced XL experiments with a transmembrane model peptide (KLAW23) to elucidate similarities and differences between the azide and the diazirine moiety. We showed that both photo-reactive lipids can be used to study lipid/peptide and lipid/protein interactions. The AzidoPC proved easier to handle, whereas the DiazPC had fewer degradation products and a higher cross-linking yield. However, the problem of backfolding occurs in both lipids; thus, it seems to be a general phenomenon.

Modulation of Ligand-Gated Glycine Receptors Via Functional Monoclonal Antibodies

Ion channels are targets of considerable therapeutic interest to address a wide variety of neurologic indications, including pain perception. Current pharmacological strategies have focused mostly on small molecule approaches that can be limited by selectivity requirements within members of a channel family or superfamily. Therapeutic antibodies have been proposed, designed, and characterized to alleviate this selectivity limitation; however, there are no Food and Drug Administration-approved therapeutic antibody-based drugs targeting ion channels on the market to date. Here, in an effort to identify novel classes of engineered ion channel modulators for potential neurologic therapeutic applications, we report the generation and characterization of six (EC50 < 25nM) Cys-loop receptor family monoclonal antibodies with modulatory function against rat and human glycine receptor alpha 1 (GlyRα1) and/or GlyRα3. These antibodies have activating (i.e., positive modulator) or inhibiting (i.e., negative modulator) profiles. Moreover, GlyRα3 selectivity was successfully achieved for two of the three positive modulators identified. When dosed intravenously, the antibodies achieved sufficient brain exposure to cover their calculated in vitro EC50 values. When compared head-to-head at identical exposures, the GlyRα3-selective antibody showed a more desirable safety profile over the nonselective antibody, thus demonstrating, for the first time, an advantage for GlyRα3-selectivity. Our data show that ligand-gated ion channels of the glycine receptor family within the central nervous system can be functionally modulated by engineered biologics in a dose-dependent manner and that, despite high protein homology between the alpha subunits, selectivity can be achieved within this receptor family, resulting in future therapeutic candidates with more desirable drug safety profiles. SIGNIFICANCE STATEMENT: This study presents immunization and multiplatform screening approaches to generate a diverse library of functional antibodies (agonist, potentiator, or inhibitory) raised against human glycine receptors (GlyRs). This study also demonstrates the feasibility of acquiring alpha subunit selectivity, a desirable therapeutic profile. When tested in vivo, these tool molecules demonstrated an increased safety profile in favor of GlyRα3-selectivity. These are the first reported functional GlyR antibodies that may open new avenues to treating central nervous system diseases with subunit selective biologics.

Imaging Technologies for Cerebral Pharmacokinetic Studies: Progress and Perspectives

Pharmacokinetic assessment of drug disposition processes in vivo is critical in predicting pharmacodynamics and toxicology to reduce the risk of inappropriate drug development. The blood–brain barrier (BBB), a special physiological structure in brain tissue, hinders the entry of targeted drugs into the central nervous system (CNS), making the drug concentrations in target tissue correlate poorly with the blood drug concentrations. Additionally, once non-CNS drugs act directly on the fragile and important brain tissue, they may produce extra-therapeutic effects that may impair CNS function. Thus, an intracerebral pharmacokinetic study was developed to reflect the disposition and course of action of drugs following intracerebral absorption. Through an increasing understanding of the fine structure in the brain and the rapid development of analytical techniques, cerebral pharmacokinetic techniques have developed into non-invasive imaging techniques. Through non-invasive imaging techniques, molecules can be tracked and visualized in the entire BBB, visualizing how they enter the BBB, allowing quantitative tools to be combined with the imaging system to derive reliable pharmacokinetic profiles. The advent of imaging-based pharmacokinetic techniques in the brain has made the field of intracerebral pharmacokinetics more complete and reliable, paving the way for elucidating the dynamics of drug action in the brain and predicting its course. The paper reviews the development and application of imaging technologies for cerebral pharmacokinetic study, represented by optical imaging, radiographic autoradiography, radionuclide imaging and mass spectrometry imaging, and objectively evaluates the advantages and limitations of these methods for predicting the pharmacodynamic and toxic effects of drugs in brain tissues.

Proposal to Consider Chemical/Physical Microenvironment as a New Therapeutic Off-Target Approach

The molecular revolution could lead drug discovery from chance observation to the rational design of new classes of drugs that could simultaneously be more effective and less toxic. Unfortunately, we are witnessing some failure in this sense, and the causes of the crisis involve a wide range of epistemological and scientific aspects. In pharmacology, one key point is the crisis of the paradigm the “magic bullet”, which is to design therapies based on specific molecular targets. Drug repurposing is one of the proposed ways out of the crisis and is based on the off-target effects of known drugs. Here, we propose the microenvironment as the ideal place to direct the off-targeting of known drugs. While it has been extensively investigated in tumors, the generation of a harsh microenvironment is also a phenotype of the vast majority of chronic diseases. The hostile microenvironment, on the one hand, reduces the efficacy of both chemical and biological drugs; on the other hand, it dictates a sort of “Darwinian” selection of those cells armed to survive in such hostile conditions. This opens the way to the consideration of the microenvironment as a convenient target for pharmacological action, with a clear example in proton pump inhibitors.

DBP-iDWT: Improving DNA-Binding Proteins Prediction Using Multi-Perspective Evolutionary Profile and Discrete Wavelet Transform

DNA-binding proteins (DBPs) have crucial biotic activities including DNA replication, recombination, and transcription. DBPs are highly concerned with chronic diseases and are used in the manufacturing of antibiotics and steroids. A series of predictors were established to identify DBPs. However, researchers are still working to further enhance the identification of DBPs. This research designed a novel predictor to identify DBPs more accurately. The features from the sequences are transformed by F-PSSM (Filtered position-specific scoring matrix), PSSM-DPC (Position specific scoring matrix-dipeptide composition), and R-PSSM (Reduced position-specific scoring matrix). To eliminate the noisy attributes, we extended DWT (discrete wavelet transform) to F-PSSM, PSSM-DPC, and R-PSSM and introduced three novel descriptors, namely, F-PSSM-DWT, PSSM-DPC-DWT, and R-PSSM-DWT. Onward, the training of the four models were performed using LiXGB (Light eXtreme gradient boosting), XGB (eXtreme gradient boosting, ERT (extremely randomized trees), and Adaboost. LiXGB with R-PSSM-DWT has attained 6.55% higher accuracy on training and 5.93% on testing dataset than the best existing predictors. The results reveal the excellent performance of our novel predictor over the past studies. DBP-iDWT would be fruitful for establishing more operative therapeutic strategies for fatal disease treatment.

Modulation of Hedgehog Signaling for the Treatment of Basal Cell Carcinoma and the Development of Preclinical Models

Basal Cell Carcinoma (BCC) is the most commonly diagnosed cancer worldwide. While the survivability of BCC is high, many patients are excluded from clinically available treatments due to health risks or personal choice. Further, patients with advanced or metastatic disease have severely limited treatment options. The dysregulation of the Hedgehog (Hh) signaling cascade drives onset and progression of BCC. As such, the modulation of this pathway has driven advancements in BCC research. In this review, we focus firstly on inhibitors that target the Hh pathway as chemotherapeutics against BCC. Two therapies targeting Hh signaling have been made clinically available for BCC patients, but these treatments suffer from limited initial efficacy and a high rate of chemoresistant tumor recurrence. Herein, we describe more recent developments of chemical scaffolds that have been designed to hopefully improve upon the available therapeutics. We secondly discuss the history and recent efforts involving modulation of the Hh genome as a method of producing in vivo models of BCC for preclinical research. While there are many advancements left to be made towards improving patient outcomes with BCC, it is clear that targeting the Hh pathway will remain at the forefront of research efforts in designing more effective chemotherapeutics as well as relevant preclinical models.

Membrane Lipids Are an Integral Part of Transmembrane Allosteric Sites in GPCRs: A Case Study of Cannabinoid CB1 Receptor Bound to a Negative Allosteric Modulator, ORG27569, and Analogs

A growing number of G-protein-coupled receptor (GPCR) structures reveal novel transmembrane lipid-exposed allosteric sites. Ligands must first partition into the surrounding membrane and take lipid paths to these sites. Remarkably, a significant part of the bound ligands appears exposed to the membrane lipids. The experimental structures do not usually account for the surrounding lipids, and their apparent contribution to ligand access and binding is often overlooked and poorly understood. Using classical and enhanced molecular dynamics simulations, we show that membrane lipids are critical in the access and binding of ORG27569 and its analogs at the transmembrane site of cannabinoid CB1 receptor. The observed differences in the binding affinity and cooperativity arise from the functional groups that interact primarily with lipids. Our results demonstrate the significance of incorporating membrane lipids as an integral component of transmembrane sites for accurate characterization, binding-affinity calculations, and lead optimization in drug discovery.

Phos-tag-based non-radioactive protocols for monitoring Arabidopsis kinase activities in vitro

Kinases are indispensable signaling components. Radioactive-based phosphorylation assays are widely used but require specific protective equipment and safety trainings. Here, we present a Phos-tag-based non-radioactive kinase assay to study Arabidopsis kinase activities. We expressed and purified both kinase and substrate proteins from E. coli cells and then used the Phos-tag technology to detect the kinase activities under either different temperatures or chemical treatments. This non-radioactive approach is environmentally friendly and applicable to other kinases and organisms. For complete details on the use and execution of this protocol, please refer to Lin et al. (2022).

Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework

Discovery of potential drugs requires rapid and precise identification of drug targets. Although traditional experimental methodologies can accurately identify drug targets, they are time-consuming and inappropriate for high-throughput screening. Computational approaches based on machine learning (ML) algorithms can expedite the prediction of druggable proteins; however, the performance of the existing computational methods remains unsatisfactory. This study proposes a computational tool, SPIDER, to enhance the accurate prediction of druggable proteins. SPIDER employs various feature descriptors pertaining to several aspects, including physicochemical properties, compositional information, and composition-transition-distribution information, coupled with well-known ML algorithms to facilitate the construction of the final meta-predictor. The experimental results showed that SPIDER enabled more precise and robust prediction of druggable proteins than the baseline models and current existing methods in terms of the independent test dataset. An online web server was established and made freely available online.

Cryo-electron Microscopic Analysis of Single-Pass Transmembrane Receptors

Single-pass transmembrane receptors (SPTMRs) represent a diverse group of integral membrane proteins that are involved in many essential cellular processes, including signal transduction, cell adhesion, and transmembrane transport of materials. Dysregulation of the SPTMRs is linked with many human diseases. Despite extensive efforts in past decades, the mechanisms of action of the SPTMRs remain incompletely understood. One major hurdle is the lack of structures of the full-length SPTMRs in different functional states. Such structural information is difficult to obtain by traditional structural biology methods such as X-ray crystallography and nuclear magnetic resonance (NMR). The recent rapid development of single-particle cryo-electron microscopy (cryo-EM) has led to an exponential surge in the number of high-resolution structures of integral membrane proteins, including SPTMRs. Cryo-EM structures of SPTMRs solved in the past few years have tremendously improved our understanding of how SPTMRs function. In this review, we will highlight these progresses in the structural studies of SPTMRs by single-particle cryo-EM, analyze important structural details of each protein involved, and discuss their implications on the underlying mechanisms. Finally, we also briefly discuss remaining challenges and exciting opportunities in the field.

Combinatorial synthesis enables scalable designer detergents for membrane protein studies

Non-ionic detergents with tailor-made properties are indispensable tools for today's world applications, such as cleaning, disinfection, and drug discovery. To facilitate their challenging production, herein we introduce a new detergent class, namely scalable hybrid detergents. We report a combinatorial synthesis strategy that allows us to fuse head groups of different detergents into hybrid detergents with unbeatable ease. Importantly, combinatorial synthesis also enables the choice between (i) high-throughput preparation of detergents for small scale applications and (ii) large scale preparation of individual detergents. This combinatorial synthesis strategy enables an unprecedented fine tuning of detergent properties, such as overall polarity and shape, which are determining factors in applications, such as membrane protein research. Our data show that membrane protein purification parameters, such as protein yields and activity, can be linked to overall polarity and shape. Conveniently, both parameters can be theoretically described by means of the hydrophilic-lipophilic balance (HLB) and packing parameter concepts. Both concepts are principally applicable to all non-ionic detergent classes, which facilitates the identification of widely applicable design guidelines for the predictable optimization of non-ionic detergents. Our findings permit access to a yet unexplored chemical space of the detergentome, therefore creating new possibilities for structure-property relationship studies. Seen from a broader perspective, combinatorial synthesis will facilitate the preparation of designer detergents with tailor-made properties for future applications in today's world.