GPCR associated sorting protein 1 (GPRASP1) interacts with numerous GPCRs including the Beta2-adrenoceptor (B2AR) and has been proposed to be involved in adaptations associated with chronic stimulation of those receptors. In clinic, long acting B2AR agonists (LABAs) such as formoterol are used in the treatment of asthma as potent bronchodilators but with adverse side effects including the development of tolerance and airway hyperresponsiveness upon chronic administration. Here, we investigated the role of GPRASP1 on B2AR activity and on B2AR agonists-associated side effects in vitro and in vivo. To this purpose, we set-up a model of chronic formoterol administration in mouse leading to B2AR down-regulation as well as to the development of airway hyperreactivity and bronchodilator tolerance and studied the phenotype of GPRASP1 knockout animals. We show in cells that GPRASP1 expression has no impact on agonist-induced B2AR down-regulation but strongly modulate B2AR-associated signalling. Moreover, wild-type mice chronically treated with formoterol developed airway hyperresponsiveness to methacholine and bronchodilator tolerance to formoterol that were absent in GPRASP1 KO mice while B2AR down-regulation was similar in both genotypes. These adverse side effects were correlated with an increase in the number of cells and in collagen levels in the lungs of wild-type but not of GPRASP1 KO mice. Collectively, our data show that GPRASP1 is critically involved in adaptations to chronic activation of B2AR that leads to lung tissue remodelling, development of bronchial hyperresponsiveness and bronchodilator tolerance to B2AR agonist formoterol and could therefore represent an interesting target to limit side effects associated with LABAs.

Deep learning models have demonstrated their potential in learning effective molecular representations critical for drug property prediction and drug discovery. Despite significant advancements in leveraging multimodal drug molecule semantics, existing approaches often struggle with challenges such as low-quality data and structural complexity. Large language models (LLMs) excel in generating high-quality molecular representations due to their robust characterization capabilities. In this work, we introduce GICL, a cross-modal contrastive learning framework that integrates LLM-derived embeddings with molecular image representations. Specifically, LLMs extract feature representations from the SMILES strings of drug molecules, which are then contrasted with graphical representations of molecular images to achieve a holistic understanding of molecular features. Experimental results demonstrate that GICL achieves state-of-the-art performance on the ADMET task while offering interpretable insights into drug properties, thereby facilitating more efficient drug design and discovery.

Although membrane proteins constitute a significant portion of the genomes of all species and represent well-validated targets for numerous therapeutic interventions, high-resolution structural knowledge of this class of proteins still falls behind that of their soluble counterparts. Despite serious technological developments in the methods presently available for structural characterizations, as well as decades spent on such investigations, membrane proteins remain notoriously difficult to study. This is particularly true for environments which mimic native membranes well enough to maintain their proper functional states. This mini review covers the most recent advances in the structural and dynamic characterization of membrane proteins through the utilization of solution nuclear magnetic resonance methods applied to lipid nanodiscs.

Nucleic acid drugs, which trigger gene silencing by hybridizing with target genes, have shown great potential in targeting those undruggable targets. However, most of the existing nucleic acid drugs are only sequence specific for target genes and lack cellular or tissue selectivity, which challenges their therapeutic safety. Here, the study proposes a tumor cell-specific gene silencing strategy by using hairpin DNA oligonucleotides to trigger target RNA degrading by highly expressed endogenous flap endonuclease 1 (FEN1) in tumor cells, for selective tumor therapy. Using Kirsten rat sarcoma viral oncogene homolog (KRASG12S) and B-cell lymphoma 2 (Bcl-2) genes as targets, it is verified that the hairpin DNA oligonucleotides show cytotoxicity only to tumor cells but very low effects on normal cells. In addition, hairpin DNA oligonucleotides designed for KRAS inhibition, which are encapsulated in lipid nanoparticles, inhibit tumor growth in mice and demonstrate excellent antitumor efficacy in combination with gefitinib, but has little effect on normal tissues, suggesting that the proposed strategy enables highly selective tumor therapy and has the potential to give rise to a new class of nucleic acid drugs.

Current strategies for active targeting in the brain are entirely based on the effective interaction of the ligand with the orthosteric sites of specific receptors on the blood-brain barrier (BBB), which is highly susceptible to various pathophysiological factors and limits the efficacy of drug delivery. Here, we propose an allosteric targeted drug delivery strategy that targets classical BBB transmembrane receptors by designing peptide ligands that specifically bind to their transmembrane domains. This strategy prevents competitive interference from endogenous ligands and antibodies by using the insulin receptor and integrin αv as model targets, respectively, and can effectively overcome pseudotargets or target loss caused by shedding or mutating the extracellular domain of target receptors. Moreover, these ligands can be spontaneously embedded in the phospholipid layer of lipid carriers using a plug-and-play approach without chemical modification, with excellent tunability and immunocompatibility. Overall, this allosteric targeted drug delivery strategy can be applied to multiple receptor targets and drug carriers and offers promising therapeutic benefits in brain diseases.

G-protein-coupled receptors (GPCRs) constitute one of the most prominent families of integral membrane receptor proteins that mediate most transmembrane signaling processes. Malfunction of these signal transduction processes is one of the underlying causes of many human pathologies (Parkinson's, Huntington's, heart diseases, etc), provoking that GPCRs are the largest family of druggable proteins. However, these receptors have been targeted traditionally by orthosteric ligands, which usually causes side effects due to the simultaneous targeting of homologous receptor subtypes. Allosteric modulation offers a promising alternative approach to circumvent this problematic and, thus, comprehending its details is a most important task. Here we use the Cannabinoid type-1 receptor (CB1R) in trying to shed light on this issue, focusing on positive allosteric modulation. This is done by using the protein-dipole Langevin-dipole (PDLD) within the linear response approximation (LRA) framework (PDLD/S-2000) along with our coarse-grained (CG) model of membrane proteins to evaluate the dissociation constants (K Bs) and cooperativity factors (αs) for a diverse series of CB1R positive allosteric modulators belonging to the 2-phenylindole structural class, considering CP55940 as an agonist. The agreement with the experimental data evinces that significantly populated allosteric modulator:CB1R and allosteric modulator:CP55940:CB1R complexes have been identified and characterized successfully. Analyzing them, it has been determined that CB1R positive allosteric modulation lies in an outwards displacement of transmembrane α helix (TM) 4 extracellular end and in the regulation of the range of motion of a compound TM7 movement for binary and ternary complexes, respectively. In this respect, we achieved a better comprehension of the molecular architecture of CB1R positive allosteric site, identifying Lys1923.28 and Gly1943.30 as key residues regarding electrostatic interactions inside this cavity, and to rationalize (at both structural and molecular level) the exhibited stereoselectivity in relation to positive allosteric modulation activity by considered CB1R allosteric modulators. Additionally, putative/postulated allosteric binding sites have been screened successfully, identifying the real CB1R positive allosteric site, and most structure-activity relationship (SAR) studies of CB1R 2-phenylindole allosteric modulators have been rationalized. All these findings point out towards the predictive value of the methodology used in the current work, which can be applied to other biophysical systems of interest. The results presented in this study contribute significantly to understand GPCRs allosteric modulation and, hopefully, will encourage a more thorough exploration of the topic.

The therapeutic efficacy of anticancer drugs heavily relies on their concentration and retention at the corresponding target site. Hence, merely increasing the cellular concentration of drugs is insufficient to achieve satisfactory therapeutic outcomes, especially for the drugs that target specific intracellular sites. This necessitates the implementation of more precise targeting strategies to overcome the limitations posed by diffusion distribution and nonspecific interactions within cells. Consequently, subcellular organelle-targeted cancer therapy, characterized by its exceptional precision, have emerged as a promising approach to eradicate cancer cells through the specific disruption of subcellular organelles. Owing to several advantages including minimized dosage and side effect, optimized efficacy, and reversal of multidrug resistance, subcellular organelle-targeted therapies have garnered significant research interest in recent years. In this review, we comprehensively summarize the distribution of drug targets, targeted delivery strategies at various levels, and sophisticated strategies for targeting specific subcellular organelles. Additionally, we highlight the significance of subcellular targeting in cancer therapy and present essential considerations for its clinical translation.

Detergents are essential for preserving the structural integrity and functionality of membrane proteins (MPs) outside the biological membrane or in aqueous solution, and thus ensuring accurate biochemical and structural analyses. Here, we introduce peptide-scaffolded detergents, a novel class of hybrid molecules formed by preassembling detergent monomers with peptides of varying lengths, mediated via Click chemistry. These detergents are characterized by scalable, straightforward synthesis and enhanced solubility. Among the variants, A4B2 emerged as the optimal detergent, demonstrating superior thermal stabilization across a range of G protein-coupled receptors, including A2AAR, SMO and GLP-1R. Additionally, A4B2 exhibits a low critical micelle concentration and small micelle size, together making it particularly effective for electron microscopy studies of A2AAR. This innovative design leverages the benefits of peptide-based and traditional detergents, offering new insights for the development of advanced detergents in MP research.

Artificial intelligence (AI) can transform drug discovery and early drug development by addressing inefficiencies in traditional methods, which often face high costs, long timelines, and low success rates. In this review we provide an overview of how to integrate AI to the current drug discovery and development process, as it can enhance activities like target identification, drug discovery, and early clinical development. Through multiomics data analysis and network-based approaches, AI can help to identify novel oncogenic vulnerabilities and key therapeutic targets. AI models, such as AlphaFold, predict protein structures with high accuracy, aiding druggability assessments and structure-based drug design. AI also facilitates virtual screening and de novo drug design, creating optimized molecular structures for specific biological properties. In early clinical development, AI supports patient recruitment by analyzing electronic health records and improves trial design through predictive modeling, protocol optimization, and adaptive strategies. Innovations like synthetic control arms and digital twins can reduce logistical and ethical challenges by simulating outcomes using real-world or virtual patient data. Despite these advancements, limitations remain. AI models may be biased if trained on unrepresentative datasets, and reliance on historical or synthetic data can lead to overfitting or lack generalizability. Ethical and regulatory issues, such as data privacy, also challenge the implementation of AI. In conclusion, in this review we provide a comprehensive overview about how to integrate AI into current processes. These efforts, although they will demand collaboration between professionals, and robust data quality, have a transformative potential to accelerate drug development.

Tissue exposure to implanted biomaterials triggers a foreign body response (FBR), which is a stepwise immunological process involving innate immune cells and tissue repair cells. Although the regulatory T (Treg) cells play a crucial role in inflammation and tissue repair, their function in the process of FBR has not been well investigated. In this study, as titanium (Ti) exhibits better biocompatibility and induces milder FBR than polymethyl methacrylate (PMMA), we analyzed the characteristics of Treg cells during FBR caused by the two types of biomaterials. In a rat femur implantation model, we found that the number of Treg cells around titanium implants was much more than that in the PMMA-implanted group. Meanwhile, the expression of CXCR4 in tissues around Ti implants was significantly higher, and the expression of CXCR7 was lower. When co-cultured with biomaterials and macrophages, the differentiation and migration of Treg cells in the Ti-implanted group were promoted, and this effect could be modulated by CXCR4/7 inhibitors. Moreover, targeting CXCR4/7 influenced the amount of Treg cells in vivo and then reversed the FBR induced by PMMA or Ti implants. In summary, our findings revealed the role of CXCR4/CXCR7 in regulating the migration and differentiation of Treg cells during FBR and suggested that the CXCL12-CXCR4/CXCR7 axis may serve as a potential therapeutic target for immunomodulating foreign body response.

Transcription factors (TFs) are master regulators of cellular function and orchestrate diverse signaling pathways and processes. Acting as convergence points of signaling pathways, they integrate extracellular stimuli with intracellular responses to regulate cell functions. Dysregulation of TFs drives tumorigenesis including proliferation, drug resistance, and immune evasion of multiple myeloma (MM), the second most-common hematologic malignancy.

The discovery that IMiDs are molecular glue degraders, which reprogram the E3-ligase cereblon (CRBN) to ubiquitinate and degrade IKZF1 and IKZF3, two otherwise un-druggable crucial TFs in MM, gave rise to the widespread interest in proximity-induced protein-degradation as an exciting novel therapeutic strategy. This review summarizes our up-to-date knowledge on the pre/clinical development of IMiD-related, more potent CRBN E3-Ligase Modulatory Drugs (CELMoDs), directed PROteolysis TArgeting Chimeras (PROTACs) and degronomids as well as on promising future avenues in the field of targeted protein-degradation (TPD).

TPD is an emerging field to treat cancer, including MM. CELMoDs are already reshaping the treatment landscape of MM. Preclinical data on PROTACs are promising. Nevertheless, a deeper understanding of TF biology as well as further advancements in screening methodologies and chemoproteomics are crucial to further spur the transformative potential of targeted TF degradation in MM.

The canonical model of G protein-coupled receptor (GPCR) signaling comprises G protein activation at the plasma membrane, followed by receptor phosphorylation and β-arrestin recruitment, which leads to receptor desensitization and endocytosis. However, the activation of some GPCRs results in sustained G protein signaling from intracellular compartments in a manner reportedly dependent on β-arrestin and receptor endocytosis. The vasopressin type 2 receptor (V2R) can be activated by two structurally similar hormones, arginine vasopressin and oxytocin, both of which stimulate the production of the second messenger cyclic adenosine monophosphate (cAMP). In this study, we showed that sustained V2R signaling and endosomal Gαs (stimulatory G protein alpha subunit) translocation could occur without β-arrestin-mediated receptor endocytosis and was primarily controlled by the residence time of the ligand on the receptor. β-Arrestin had opposing effects on sustained signaling: It facilitated receptor internalization into endosomes, where it activated Gαs, and promoted cAMP production from this compartment. However, β-arrestin-mediated receptor endocytosis also induced ligand dissociation due to the acidic endosomal environment, thereby limiting the signal. Overall, our data suggest that signals originating at the plasma membrane play a dominant role in sustained V2R signaling stimulated by arginine vasopressin.

Pharmaceutical discharge to the environment is of concern due to its potential adverse effects on aquatic species. It is estimated that around 40% of pharmaceuticals target G protein-coupled receptors (GPCRs). The in vitro transforming growth factor-α (TGFα) shedding assay was applied to measure the antagonistic activities of pharmaceuticals against human GPCRs. However, their ability to stimulate fish GPCRs remains unclear. Here, antagonistic activities of 30 pharmaceuticals against zebrafish dopamine (zD2a and zD2c), adrenergic family member (zβ1), and histamine (zH1 and zH3) receptors were measured by the TGFα shedding assay. The study found an interspecies difference in binding affinities between human and zebrafish: pharmaceuticals more strongly inhibited the zD2c and zH1 receptors than human D2 (hD2) and hH1 receptors, while zD2a and zβ1 receptors were less inhibited than hD2 and hβ1 receptors. The potential molecular explanations for the observed interspecies differences in binding affinity for hydroxyzine and bisoprolol were investigated using molecular docking. Pharmaceutical potency against zebrafish GPCRs and predicted effluent concentrations were used to predict equivalent quantities (EQs), and these EQs were used to prioritize pharmaceuticals of concern in wastewater in England and Japan. This study highlights the use of the TGFα shedding assay adopting zebrafish GPCRs to better understand the ecological effects of pharmaceuticals on fish.

Nuclear receptors (NRs) are ligand-activated transcription factors that regulate gene expression in response to physiological signals, such as hormones and other chemical messengers. These receptors either activate or repress the transcription of target genes, which in turn promotes or suppresses physiological processes governing growth, differentiation, and homeostasis. NRs bind to specific DNA sequences and, in response to ligand binding, either promote or hinder the assembly of the transcriptional machinery, thereby influencing gene expression at the transcriptional level. These receptors are involved in a wide range of pathological conditions, including cancer, metabolic disorders, chronic inflammatory diseases, and immune system-related disorders. Modulation of NR function through targeted drugs has shown therapeutic benefits in treating such conditions. NR-targeted drugs, which either completely or selectively activate or block receptor function, represent a significant class of clinically valuable therapeutics. However, the pathways of NR-mediated gene expression and the resulting physiological effects are complex, involving crosstalk between various biomolecular components. As a result, NR-targeted drug discovery is challenging. With improved understanding of how NRs regulate physiological functions and deeper insights into their molecular structure, the process of NR-targeted drug discovery has evolved. While many traditional NR-targeting drugs are associated with side effects of varying severity, new drug candidates are being designed to minimize these adverse effects. Given that NR activity varies according to the tissue in which they are expressed and the specific isoform that is activated or repressed, achieving selectivity in targeting specific tissues and isoform classes may help reduce systemic side effects. In a recent breakthrough, the isoform-selective, hepato-targeted thyroid hormone-β agonist, Resmetirom (marketed as Rezdiffra), was approved for the treatment of non-alcoholic steatohepatitis. This chapter explores the structural and mechanistic principles guiding NR-targeted drug discovery and provides insights into recent developments in this field.

Recent advancements in deep learning and generative models have significantly expanded the applications of virtual screening for drug-like compounds. Here, we introduce a multitarget transformer model, PCMol, that leverages the latent protein embeddings derived from AlphaFold2 as a means of conditioning a de novo generative model on different targets. Incorporating rich protein representations allows the model to capture their structural relationships, enabling the chemical space interpolation of active compounds and target-side generalization to new proteins based on embedding similarities. In this work, we benchmark against other existing target-conditioned transformer models to illustrate the validity of using AlphaFold protein representations over raw amino acid sequences. We show that low-dimensional projections of these protein embeddings cluster appropriately based on target families and that model performance declines when these representations are intentionally corrupted. We also show that the PCMol model generates diverse, potentially active molecules for a wide array of proteins, including those with sparse ligand bioactivity data. The generated compounds display higher similarity known active ligands of held-out targets and have comparable molecular docking scores while maintaining novelty. Additionally, we demonstrate the important role of data augmentation in bolstering the performance of generative models in low-data regimes. Software package and AlphaFold protein embeddings are freely available at https://github.com/CDDLeiden/PCMol.

Metabolism reprogramming is a crucial hallmark of malignant tumors. Tumor cells demonstrate enhanced metabolic efficiency, converting nutrient inputs into glucose, amino acids, and lipids essential for their malignant proliferation and progression. Metformin, a commonly prescribed medication for type 2 diabetes mellitus, has garnered attention for its potential anticancer effects beyond its established hypoglycemic benefits.

This review adopts a comprehensive approach to delineate the mechanisms underlying metabolite abnormalities within the primary metabolic processes of malignant tumors.

This review examines the abnormal activation of G protein-coupled receptors (GPCRs) in these metabolic pathways, encompassing aerobic glycolysis with increased lactate production in glucose metabolism, heightened lipid synthesis and cholesterol accumulation in lipid metabolism, and glutamine activation alongside abnormal protein post-translational modifications in amino acid and protein metabolism. Furthermore, the intricate metabolic pathways and molecular mechanisms through which metformin exerts its anticancer effects are synthesized and analyzed, particularly its impacts on AMP-activated protein kinase activation and the mTOR pathway. The analysis reveals a multifaceted understanding of how metformin can modulate tumor metabolism, targeting key nodes in metabolic reprogramming essential for tumor growth and progression. The review compiles evidence that supports metformin's potential as an adjuvant therapy for malignant tumors, highlighting its capacity to interfere with critical metabolic pathways.

In conclusion, this review offers a comprehensive overview of the plausible mechanisms mediating metformin's influence on tumor metabolism, fostering a deeper comprehension of its anticancer mechanisms. By expanding the clinical horizons of metformin and providing insight into metabolism-targeted tumor therapies, this review lays the groundwork for future research endeavors aimed at refining and advancing metabolic intervention strategies for cancer treatment.

The nucleic acid topoisomerases (TOP) are an evolutionary conserved mechanism to solve topological problems within DNA and RNA that have been historically well-established as a chemotherapeutic target. During investigation of trends within clinical trials, we have identified a very high number of clinical trials involving TOP inhibitors, prompting us to further evaluate the current status of this class of therapeutic agents. In total, we have identified 233 unique molecules with TOP-inhibiting activity. In this review, we provide an overview of the clinical drug development highlighting advances in current clinical uses and discussing novel drugs and indications under development. A wide range of bacterial infections, along with solid and hematologic neoplasms, represent the bulk of clinically approved indications. Negative ADR profile and drug resistance among the antibacterial TOP inhibitors and anthracycline-mediated cardiotoxicity in the antineoplastic TOP inhibitors are major points of concern, subject to continuous research efforts. Ongoing development continues to focus on bacterial infections and cancer; however, there is a degree of diversification in terms of novel drug classes and previously uncovered indications, such as glioblastoma multiforme or Clostridium difficile infections. Preclinical studies show potential in viral, protozoal, parasitic and fungal infections as well and suggest the emergence of a novel target, TOP IIIβ. We predict further growth and diversification of the field thanks to the large number of experimental TOP inhibitors emerging.

Despite considerable research efforts, inflammatory diseases remain a heavy burden on human health, causing significant economic losses annually. Histone deacetylases (HDACs) play a significant role in regulating inflammation (via histone and non-histone protein deacetylation) and chromatin structure and gene expression regulation. Herein, we present a detailed description of the different HDACs and their functions and analyze the role of HDACs in inflammatory diseases, including pro-inflammatory cytokine production reduction, immune cell function modulation, and anti-inflammatory cell activity enhancement. Although HDAC inhibitors have shown broad inflammatory disease treatment potentials, their clinical applicability remains limited because of their non-specific effects, adverse effects, and drug resistance. With further research and insight, these inhibitors are expected to become important tools for the treatment of a wide range of inflammatory diseases. This review aims to explore the mechanisms and application prospects of HDACs and their inhibitors in multiple inflammatory diseases.

Small-molecule drugs are effective and thus most widely used. However, their applications are limited by their reliance on active high-affinity binding sites, restricting their target options. A breakthrough approach involves molecular glues, a novel class of small-molecule compounds capable of inducing protein-protein interactions (PPIs). This opens avenues to target conventionally undruggable proteins, overcoming limitations seen in conventional small-molecule drugs. Molecular glues play a key role in targeted protein degradation (TPD) techniques, including ubiquitin-proteasome system-based approaches such as proteolysis targeting chimeras (PROTACs) and molecular glue degraders and recently emergent lysosome system-based techniques like molecular degraders of extracellular proteins through the asialoglycoprotein receptors (MoDE-As) and macroautophagy degradation targeting chimeras (MADTACs). These techniques enable an innovative targeted degradation strategy for prolonged inhibition of pathology-associated proteins. This review provides an overview of them, emphasizing the clinical potential of molecular glues and guiding the development of molecular-glue-mediated TPD techniques.

The intricate molecular mechanisms underlying estrogen receptor-positive (ER+) breast carcinogenesis and resistance to endocrine therapy remain elusive. In this study, we elucidate the pivotal role of GPR81, a G protein-coupled receptor, in ER+ breast cancer (BC) by demonstrating low expression of GPR81 in tamoxifen (TAM)-resistant ER+ BC cell lines and tumor samples, along with the underlying molecular mechanisms.

Fatty acid oxidation (FAO) levels and lipid accumulation were explored using MDA and FAβO assay, BODIPY 493/503 staining, and Lipid TOX staining. Autophagy levels were assayed using CYTO-ID detection and Western blotting. The impact of GPR81 on TAM resistance in BC was investigated through CCK8 assay, colony formation assay and a xenograft mice model.

Aberrantly low GPR81 expression in TAM-resistant BC cells disrupts the Rap1 pathway, leading to the upregulation of PPARα and CPT1. This elevation in PPARα/CPT1 enhances FAO, impedes lipid accumulation and lipid droplet (LD) formation, and subsequently inhibits cell autophagy, ultimately promoting TAM-resistant BC cell growth. Moreover, targeting GPR81 and FAO emerges as a promising therapeutic strategy, as the GPR81 agonist and the CPT1 inhibitor etomoxir effectively inhibit ER+ BC cell and tumor growth in vivo, re-sensitizing TAM-resistant ER+ cells to TAM treatment.

Our data highlight the critical and functionally significant role of GPR81 in promoting ER+ breast tumorigenesis and resistance to endocrine therapy. GPR81 and FAO levels show potential as diagnostic biomarkers and therapeutic targets in clinical settings for TAM-resistant ER+ BC.

Large-scale drug discovery and repurposing is challenging. Identifying the mechanism of action (MOA) is crucial, yet current approaches are costly and low-throughput. Here we present an approach for MOA identification by profiling changes in mitochondrial phenotypes. By temporally imaging mitochondrial morphology and membrane potential, we established a pipeline for monitoring time-resolved mitochondrial images, resulting in a dataset comprising 570,096 single-cell images of cells exposed to 1,068 United States Food and Drug Administration-approved drugs. A deep learning model named MitoReID, using a re-identification (ReID) framework and an Inflated 3D ResNet backbone, was developed. It achieved 76.32% Rank-1 and 65.92% mean average precision on the testing set and successfully identified the MOAs for six untrained drugs on the basis of mitochondrial phenotype. Furthermore, MitoReID identified cyclooxygenase-2 inhibition as the MOA of the natural compound epicatechin in tea, which was successfully validated in vitro. Our approach thus provides an automated and cost-effective alternative for target identification that could accelerate large-scale drug discovery and repurposing.

Proteins on the surfaces of cells serve as physical connection points to bridge one cell with another, enabling direct communication between cells and cohesive structure. As biomedical research makes the leap from characterizing individual cells toward understanding the multicellular organization of the human body, the binding interactions between molecules on the surfaces of cells are foundational both for computational models and for clinical efforts to exploit these influential receptor pathways. To achieve this grander vision, we must assemble the full interactome of ways surface proteins can link together. This review investigates how close we are to knowing the human cell surface protein interactome. We summarize the current state of databases and systematic technologies to assemble surface protein interactomes, while highlighting substantial gaps that remain. We aim for this to serve as a road map for eventually building a more robust picture of the human cell surface protein interactome.

Acquired brain injury is an urgent situation that requires rapid diagnosis and treatment. Magnetic resonance imaging (MRI) and computed tomography (CT) are required for accurate diagnosis. However, these methods are costly and require substantial infrastructure and specialized staff. Circulatory biomarkers of acute brain injury may help in the management of patients with acute cerebrovascular events and prevent poor outcome and mortality. The purpose of this review is to provide an overview of the development of potential biomarkers of brain damage to increase diagnostic possibilities. For this purpose, we searched the PubMed database of studies on the diagnostic potential of brain injury biomarkers. We also accessed information from Clinicaltrials.gov to identify any clinical trials of biomarker measurements for the diagnosis of brain damage. In total, we present 41 proteins, enzymes and hormones that have been considered as biomarkers for brain injury, of which 20 have been studied in clinical trials. Several microRNAs have also emerged as potential clinical biomarkers for early diagnosis. Combining multiple biomarkers in a panel, along with other parameters, is yielding promising outcomes.

Aptamers are short oligonucleotides with single-stranded regions or peptides that recently started to transform the field of diagnostics. Their unique ability to bind to specific target molecules with high affinity and specificity is at least comparable to many traditional biorecognition elements. Aptamers are synthetically produced, with a compact size that facilitates deeper tissue penetration and improved cellular targeting. Furthermore, they can be easily modified with various labels or functional groups, tailoring them for diverse applications. Even more uniquely, aptamers can be regenerated after use, making aptasensors a cost-effective and sustainable alternative compared to disposable biosensors. This review delves into the inherent properties of aptamers that make them advantageous in established diagnostic methods. Furthermore, we will examine some of the limitations of aptamers, such as the need to engage in bioinformatics procedures in order to understand the relationship between the structure of the aptamer and its binding abilities. The objective is to develop a targeted design for specific targets. We analyse the process of aptamer selection and design by exploring the current landscape of aptamer utilisation across various industries. Here, we illuminate the potential advantages and applications of aptamers in a range of diagnostic techniques, with a specific focus on quartz crystal microbalance (QCM) aptasensors and their integration into the well-established ELISA method. This review serves as a comprehensive resource, summarising the latest knowledge and applications of aptamers, particularly highlighting their potential to revolutionise diagnostic approaches.

Understanding the role of biased G protein-coupled receptor (GPCR) agonism in receptor signaling may provide novel insights into the opposing effects mediated by cannabinoids, particularly in cancer and cancer metastasis. GPCRs can have more than one active state, a phenomenon called either 'biased agonism', 'functional selectivity', or 'ligand-directed signaling'. However, there are increasing arrays of cannabinoid allosteric ligands with different degrees of modulation, called 'biased modulation', that can vary dramatically in a probe- and pathway-specific manner, not from simple differences in orthosteric ligand efficacy or stimulus-response coupling. Here, emerging evidence proposes the involvement of CB1 GPCRs in a novel biased GPCR signaling paradigm involving the crosstalk between neuraminidase-1 (Neu-1) and matrix metalloproteinase-9 (MMP-9) in the activation of glycosylated receptors through the modification of the receptor glycosylation state. The study findings highlighted the role of CB1 agonists AM-404, Aravnil, and Olvanil in significantly inducing Neu-1 sialidase activity in a dose-dependent fashion in RAW-Blue, PANC-1, and SW-620 cells. This approach was further substantiated by findings that the neuromedin B receptor inhibitor, BIM-23127, MMP-9 inhibitor, MMP9i, and Neu-1 inhibitor, oseltamivir phosphate, could specifically block CB1 agonist-induced Neu-1 sialidase activity. Additionally, we found that CB1 receptors exist in a multimeric receptor complex with Neu-1 in naïve, unstimulated RAW-Blue, PANC-1, and SW-620 cells. This complex implies a molecular link that regulates the interaction and signaling mechanism among these molecules present on the cell surface. Moreover, the study results demonstrate that CB1 agonists induce NFκB-dependent secretory alkaline phosphatase (SEAP) activity in influencing the expression of epithelial-mesenchymal markers, E-cadherin, and vimentin in SW-620 cells, albeit the impact on E-cadherin expression is less pronounced compared to vimentin. In essence, this innovative research begins to elucidate an entirely new molecular mechanism involving a GPCR signaling paradigm in which cannabinoids, as epigenetic stimuli, may traverse to influence gene expression and contribute to cancer and cancer metastasis.

Vibrio vulnificus (vv) is a multidrug-resistant human bacterial pathogen whose prevalence is expected to increase over the years. Transketolases (TK), transferases catalyzing two reactions of the nonoxidative branch of the pentose-phosphate pathway and therefore linked to several crucial metabolic pathways, are potential targets for new drugs against this pathogen. Here, the vvTK is crystallized and its structure is solved at 2.1 Å. A crown of 6 histidyl residues is observed in the active site and expected to participate in the thiamine pyrophosphate (cofactor) activation. Docking of fructose-6-phosphate and ferricyanide used in the activity assay, suggests that both substrates can bind vvTK simultaneously. This is confirmed by steady-state kinetics showing a sequential mechanism, on the contrary to the natural transferase reaction which follows a substituted mechanism. Inhibition by the I38-49 inhibitor (2-(4-ethoxyphenyl)-1-(pyrimidin-2-yl)-1H-pyrrolo[2,3-b]pyridine) reveals for the first time a cooperative behavior of a TK and docking experiments suggest a previously undescribed binding site at the interface between the pyrophosphate and pyridinium domains.

Stereospecific recognition of chiral molecules plays a crucial role in biological systems. The μ-opioid receptor (MOR) exhibits binding affinity towards (-)-morphine, a well-established gold standard in pain management, while it shows minimal binding affinity for the (+)-morphine enantiomer, resulting in a lack of analgesic activity. Understanding how MOR stereoselectively recognizes morphine enantiomers has remained a puzzle in neuroscience and pharmacology for over half-a-century due to the lack of direct observation techniques. To unravel this mystery, we constructed the binding and unbinding processes of morphine enantiomers with MOR via molecular dynamics simulations to investigate the thermodynamics and kinetics governing MOR's stereoselective recognition of morphine enantiomers. Our findings reveal that the binding of (-)-morphine stabilizes MOR in its activated state, exhibiting a deep energy well and a prolonged residence time. In contrast, (+)-morphine fails to sustain the activation state of MOR. Furthermore, the results suggest that specific residues, namely D1142.50 and D1473.32, are deprotonated in the active state of MOR bound to (-)-morphine. This work highlights that the selectivity in molecular recognition goes beyond binding affinities, extending into the realm of residence time.

G protein coupled receptors (GPCRs) are seven-transmembrane domain proteins that modulate cellular processes in response to external stimuli. These receptors represent the largest family of membrane proteins, and in mammals, their signaling regulates important physiological functions, such as vision, taste, and olfaction. Many organisms, including yeast, slime molds, and viruses encode GPCRs. Cytomegaloviruses (CMVs) are large, betaherpesviruses, that encode viral GPCRs (vGPCRs). Human CMV (HCMV) encodes four vGPCRs, including UL33, UL78, US27, and US28. Each of these vGPCRs, as well as their rodent and primate orthologues, have been investigated for their contributions to viral infection and disease. Herein, we discuss how the CMV vGPCRs function during lytic and latent infection, as well as our understanding of how they impact viral pathogenesis.

Acting as GTPase activating proteins promoting the silencing of activated G-proteins, regulators of G protein signaling (RGSs) are generally considered negative modulators of cell signaling. In the CNS, the expression of RGS4 is altered in diverse pathologies and its upregulation was reported in astrocytes exposed to an inflammatory environment. In a model of cultured cortical astrocytes, we herein investigate the influence of RGS4 on intracellular calcium signaling mediated by type 5 metabotropic glutamate receptor (mGluR5), which is known to support the bidirectional communication between neurons and glial cells. RGS4 activity was manipulated by exposure to the inhibitor CCG 63802 or by infecting the cells with lentiviruses designed to achieve the silencing or overexpression of RGS4. The pharmacological inhibition or silencing of RGS4 resulted in a decrease in the percentage of cells responding to the mGluR5 agonist DHPG and in the proportion of cells showing typical calcium oscillations. Conversely, RGS4-lentivirus infection increased the percentage of cells showing calcium oscillations. While the physiological implication of cytosolic calcium oscillations in astrocytes is still under investigation, the fine-tuning of calcium signaling likely determines the coding of diverse biological events. Indirect signaling modulators such as RGS4 inhibitors, used in combination with receptor ligands, could pave the way for new therapeutic approaches for diverse neurological disorders with improved efficacy and selectivity.

Compared with traditional tedious organic solvent-assisted separation process in natural medicinal chemistry, cytomembrane (CM) fishing technique became a more appealing and greener choice for screening bioactive components from natural products. However, its large-scale practical value was greatly weakened by the easy fall-off of CMs from magnetic supports, rooted in the instability of common Fe3O4 particles and their insufficient interaction with CMs. In this research, a new green biostable platform was developed for drug screening through the integration of hyperbranched quaternized hydrothermal magnetic carbon spheres (HQ-HMCSs) and CMs. The positive-charged HQ-HMCSs were constructed by chitosan-based hydrothermal carbonization onto Fe3O4 nanospheres and subsequent aqueous hyperbranching quaternization with 1,4-butanediol diglycidyl ether and methylamine. The strong interaction between HQ-HMCSs and CMs was formed via electrostatic attraction of HQ-HMCSs to negative-charged CMs and covalent linkage derived from the epoxy-amine addition reactions. The chemically stable HMCSs and its integration with CMs contributed to dramatically higher stability and recyclability of bionic nanocomposites. With the fishing of osteoblast CMs integrated HQ-HMCSs, two novel potential anti-osteoporosis compounds, narcissoside and beta-ionone, were discovered from Hippophae rhamnoides L. Enhanced osteoblast proliferation, alkaline phosphatase, and mineralization levels proved their positive osteogenesis effects. Preliminary pharmacological investigation demonstrated their potential action on membrane proteins of estrogen receptor alpha and insulin-like growth factor 1. Furthermore, beta-ionone showed apparent therapeutic effects on osteogenic lesions in zebrafish. These results provide a green, stable, cost-efficient, and reliable access to rapid discovery of drug leads, which verifiably benefits the design of nanocarbon-based biocomposites with increasingly advanced functionality.

Gastric cancer (GC) remains a leading cause of cancer-related mortality globally. This is due to the fact that majority of the cases of GC are diagnosed at an advanced stage when the treatment options are limited and prognosis is poor. The diffuse subtype of gastric cancer (DGC) under Lauren's classification is more aggressive and usually occurs in younger patients than the intestinal subtype. The concept of personalized medicine is leading to the identification of multiple biomarkers in a large variety of cancers using different combinations of omics technologies. Proteomic changes including post-translational modifications are crucial in oncogenesis. We analyzed the phosphoproteome of DGC by using paired fresh frozen tumor and adjacent normal tissue from five patients diagnosed with DGC. We found proteins involved in the epithelial-to-mesenchymal transition (EMT), c-MYC pathway, and semaphorin pathways to be differentially phosphorylated in DGC tissues. We identified three kinases, namely, bromodomain adjacent to the zinc finger domain 1B (BAZ1B), WNK lysine-deficient protein kinase 1 (WNK1), and myosin light-chain kinase (MLCK) to be hyperphosphorylated, and one kinase, AP2-associated protein kinase 1 (AAK1), to be hypophosphorylated. LMNA hyperphosphorylation at serine 392 (S392) was demonstrated in DGC using immunohistochemistry. Importantly, we have detected heparin-binding growth factor (HDGF), heat shock protein 90 (HSP90), and FTH1 as potential therapeutic targets in DGC, as drugs targeting these proteins are currently under investigation in clinical trials. Although these new findings need to be replicated in larger study samples, they advance our understanding of signaling alterations in DGC, which could lead to potentially novel actionable targets in GC.

Pancreatic cancer and cervical cancer are among the most common cancers. Brown algae have anti-inflammatory, anti-cancer, anti-fungal, antioxidant, and immune-boosting properties. This study investigated the antioxidant properties and the effect of brown algae extract on pancreatic and uterine cancer cells.

In this study, Cervical (Hela) and pancreas (Paca-2) cancer cell lines were examined. The algae materials were extracted by sequential maceration method and amount of fucoxanthin content in the sample was determined by using High Performance Liquid Chromatography (HPLC) system. The cytotoxic effect of different concentrations of brown algae was measured by the MTT assay. All statistical calculations for comparing IC50 were analyzed using Graph Pad Prism software.

the algal sample contained an average of 102.52 ± 0.12 μg of fucoxanthin per 100 g. IC50 for 2, 2-diphenyl-1-picrylhydrazyl (DPPH) and hydrogen peroxide free radical scavenging activity for methanolic extract was 2.02 and 11.98 ± 0.13 respectively. Brown algae in all fractions inhibited cell growth and survival. In Hela cell lines, the methanolic extract was the most effective inhibitor, while in Paca cell lines, hexane and methanolic extracts were particularly potent. The methanolic extract was more toxic than other fractions on Hela and Paca cell lines.

This study highlights brown algae extracts strong anticancer effects on uterine and pancreatic cancer cells, suggesting its potential as a natural anticancer drug. Different fractions of the extract showed superior apoptotic and cytotoxic effects, with higher concentrations leading to increased apoptotic effects and reduced survival rates of cancer cells.

Protein-ligand blind docking is a widely used method for studying the binding sites and poses of ligands and receptors in pharmaceutical and biological research. Recently, our new blind docking server named CB-Dock2 has been released and is currently being utilized by researchers worldwide. CB-Dock2 outperforms state-of-the-art methods due to its accuracy in binding site identification and binding pose prediction, which are enabled by its knowledge-based docking engine. This highly automated server offers interactive and intuitive input and output web interfaces, making it an efficient and user-friendly tool for the bioinformatics and cheminformatics communities. This chapter provides a brief overview of the methods, followed by a detailed guide on using the CB-Dock2 server. Additionally, we present a case study that evaluates the performance of protein-ligand blind docking using this tool.

Detergents are amphiphilic molecules that serve as enabling steps for today's world applications. The increasing diversity of the detergentome is key to applications enabled by detergent science. Regardless of the application, the optimal design of detergents is determined empirically, which leads to failed preparations, and raising costs. To facilitate project planning, here we review synthesis strategies that drive the diversification of the detergentome. Synthesis strategies relevant for industrial and academic applications include linear, modular, combinatorial, bio-based, and metric-assisted detergent synthesis. Scopes and limitations of individual synthesis strategies in context with industrial product development and academic research are discussed. Furthermore, when designing detergents, the selection of molecular building blocks, i. e., head, linker, tail, is as important as the employed synthesis strategy. To facilitate the design of safe-to-use and tailor-made detergents, we provide an overview of established head, linker, and tail groups and highlight selected scopes and limitations for applications. It becomes apparent that most recent contributions to the increasing chemical diversity of detergent building blocks originate from the development of detergents for membrane protein studies. The overview of synthesis strategies and molecular blocks will bring us closer to the ability to predictably design and synthesize optimal detergents for challenging future applications.

Solute carrier (SLC) transporters play a dual role in the occurrence and progression of tumours by acting as both suppressors and promoters. However, the overall impact of SLC transcriptome signatures on the tumour microenvironment, biological behaviour and clinical stratification of gastric cancer has not been thoroughly investigated. Therefore, we comprehensively analysed the expression profiles of the SLC transporter family members to identify novel molecular subtypes in gastric cancer. We identified two distinct SLC subtypes, SLC-S1 and SLC-S2, using non-negative matrix factorization. These subtypes were markedly linked with the tumour microenvironment landscape, biological pathway activation and distinct clinical features of gastric cancer. Furthermore, a new scoring model, the SLC score, was developed to quantify the SLC subtypes. High SLC scores indicated a pattern of 'SLC-S2', characterized by stromal infiltration and activation, poor prognosis and insensitivity to chemotherapy and immunotherapy, but high sensitivity to imatinib. The SLC score could serve as a supplement to the Tumour Node Metastasis (TNM) staging system to guide personalized treatment strategies and predict prognosis for patients with gastric cancer.

The gut microbiota and its homeostasis play a crucial role in human health. However, for some diseases related to the gut microbiota, current traditional medicines can only relieve symptoms, and it is difficult to solve the root causes or even cause side effects like disturbances in the gut microbiota. Increasing clinical studies and evidences have demonstrated that probiotics, prebiotics, and postbiotics can prevent and treat various diseases, but currently they can only be used as dietary supplements rather than medicines, which restricts the application of probiotics in the field of medicine. Here, this review analyzes the importance of gut microbiota in human health and the current problems of traditional medicines, and systematically summarizes the effectiveness and mechanisms of probiotics, prebiotics, and postbiotics in maintaining health and treating diseases based on animal models and clinical trials. And based on current research outcomes and development trends in this field, the challenges and prospects of their clinical application in maintaining health, alleviating and treating diseases are analyzed. It is hoped to promote the application of probiotics, prebiotics, and postbiotics in disease treatment and open up new frontiers in probiotic research.

G protein-coupled receptors (GPCRs) are a large superfamily of cell membrane proteins that play an important physiological role as transmitters of extracellular signals. Signal transmission through the cell membrane depends on conformational changes in the transmembrane region of the receptor, which makes the investigation of the dynamics in these regions particularly relevant. Molecular dynamics (MD) simulations provide a wealth of data about the structure, dynamics, and physiological function of biological macromolecules by modelling the interactions between their atomic constituents. In this study, a Recurrent and Convolutional Neural Network (RNN) model, namely Long Short-Term Memory (LSTM), is used to predict the dynamics of two GPCR states and three specific simulations of each one, through their activation path and focussing on specific receptor regions. Active and inactive states of the GPCRs are analysed in six scenarios involving APO, Full Agonist (BI 167107) and Partial Inverse Agonist (carazolol) of the receptor. Four Machine Learning models with increasing complexity in terms of neural network architecture are evaluated, and their results discussed. The best method achieves an overall RMSD lower than 0.139 Å and the transmembrane helices are the regions showing the minimum prediction errors and minimum relative movements of the protein.

Statins are widely used for cardiovascular disease prevention but their effects on cognition remain unclear. Statins reduce cholesterol concentration and have been suggested to provide both beneficial and detrimental effects. Our aim was to investigate the cross-sectional and longitudinal association between statin use and cognitive performance, and whether blood low-density lipoprotein, high-density lipoprotein, triglycerides, glucose, C-reactive protein, and vitamin D biomarkers mediated this association. We used participants from the UK biobank aged 40-69 without neurological and psychiatric disorders (n = 147 502 and n = 24 355, respectively). We performed linear regression to evaluate the association between statin use and cognitive performance and, mediation analysis to quantify the total, direct, indirect effects and the proportion meditated by blood biomarkers. Statin use was associated with lower cognitive performance at baseline (β = -0.40 [-0.53, -0.28], p = <.0001), and this association was mediated by low-density lipoprotein (proportion mediated = 51.4%, p = .002), C-reactive protein (proportion mediated = -11%, p = .006) and blood glucose (proportion mediated = 2.6%, p = .018) concentrations. However, statin use was not associated with cognitive performance, measured 8 years later (β = -0.003 [-0.11, 0.10], p = .96). Our findings suggest that statins are associated with lower short-term cognitive performance by lowering low-density lipoprotein and raising blood glucose concentrations, and better performance by lowering C-reactive protein concentrations. In contrast, statins have no effect on long-term cognition and remain beneficial in reducing cardiovascular risk factors.

G protein-coupled receptors (GPCRs) are the largest protein family in humans and are important drug targets. Yeast, especially Saccharomyces cerevisiae, is a useful host for modifying the function and stability of GPCRs through protein engineering, which is advantageous for mammalian cells. When GPCRs are expressed in yeast, their function is often impaired. In this study, we performed random mutagenesis using error-prone PCR and then an in vivo screening to obtain mutants that recovered the activity of the human histamine H3 receptor (H3R), which loses its signaling function when expressed in yeast. Four mutations with recovered activity were identified after screening. Three of the mutations were identified near the DRY and NPxxY motifs of H3R, which are important for activation and are commonly found in class A GPCRs. The mutants responded exclusively to the yeast YB1 strain harboring Gi-chimera proteins, showing retention of G protein specificity. Analysis of one of the mutants with recovered activity, C415R, revealed that it maintained its ligand-binding characteristics. The strategy used in this study may enable the recovery of the activity of other GPCRs that do not function in S. cerevisiae and may be useful in creating GPCRs mutants stabilized in their active conformations.

Drug-drug interaction (DDI) identification is essential to clinical medicine and drug discovery. The two categories of drugs (i.e. chemical drugs and biotech drugs) differ remarkably in molecular properties, action mechanisms, etc. Biotech drugs are up-to-comers but highly promising in modern medicine due to higher specificity and fewer side effects. However, existing DDI prediction methods only consider chemical drugs of small molecules, not biotech drugs of large molecules. Here, we build a large-scale dual-modal graph database named CB-DB and customize a graph-based framework named CB-TIP to reason event-aware DDIs for both chemical and biotech drugs. CB-DB comprehensively integrates various interaction events and two heterogeneous kinds of molecular structures. It imports endogenous proteins founded on the fact that most drugs take effects by interacting with endogenous proteins. In the modality of molecular structure, drugs and endogenous proteins are two heterogeneous kinds of graphs, while in the modality of interaction, they are nodes connected by events (i.e. edges of different relationships). CB-TIP employs graph representation learning methods to generate drug representations from either modality and then contrastively mixes them to predict how likely an event occurs when a drug meets another in an end-to-end manner. Experiments demonstrate CB-TIP's great superiority in DDI prediction and the promising potential of uncovering novel DDIs.

Telomeres are repetitive DNA sequences located at the ends of chromosomes, playing a vital role in maintaining chromosomal integrity and stability. Dysregulation of telomeres has been implicated in the development of various cancers, including non-small cell lung cancer (NSCLC), which is the most common type of lung cancer. Genetic variations within telomere maintenance genes may influence the risk of developing NSCLC. The present study aimed to evaluate the genetic associations of select variants within telomere maintenance genes in a population from Jammu and Kashmir, North India, and to investigate the relationship between telomere length and NSCLC risk.

We employed the cost-effective and high-throughput MassARRAY MALDI-TOF platform to assess the genetic associations of select variants within telomere maintenance genes in a population from Jammu and Kashmir, North India. Additionally, we used TaqMan genotyping to validate our results. Furthermore, we investigated telomere length variation and its relation to NSCLC risk in the same population using dual-labeled fluorescence-based qPCR.

Our findings revealed significant associations of TERT rs10069690 and POT1 rs10228682 with NSCLC risk (adjusted p-values = 0.019 and 0.002, respectively), while TERF2 rs251796 and rs2975843 showed no significant associations. The TaqMan genotyping validation further substantiated the associations of TERT rs10069690 and rs2242652 with NSCLC risk (adjusted p-values = 0.02 and 0.003, respectively). Our results also demonstrated significantly shorter telomere lengths in NSCLC patients compared to controls (p = 0.0004).

This study highlights the crucial interplay between genetic variation in telomere maintenance genes, telomere attrition, and NSCLC risk in the Jammu and Kashmir population of North India. Our findings suggest that TERT and POT1 gene variants, along with telomere length, may serve as potential biomarkers and therapeutic targets for NSCLC in this population. Further research is warranted to elucidate the underlying mechanisms and to explore the potential clinical applications of these findings.

Membrane proteins such as G protein-coupled receptors (GPCRs) are involved in awide range of physiological and pathological cellular processes. Binding of extracellular signals to GPCRs, including hormones, neurotransmitters, peptides and proteins, can activate intracellular signaling cascades via G protein interaction. Chemokine receptors are key GPCRs implicated in cancers, immune responses, cell migration and inflammation. Specifically, the CCR5 and CXCR4 chemokine receptors serve as important therapeutic targets against Human Immunodeficiency virus (HIV) entry into human cells. Maraviroc and Vicriviroc, two clinically used HIV entry inhibitors, are antagonists of the CCR5 receptor. These drugs block HIV entry, but ultimately resistance develops, due to emergence of viruses that can utilize the CXCR4 co-receptor. Unfortunately, development of chemokine receptor antagonists as selective drugs of HIV infection has been greatly hindered as their target orthosteric site is conserved among different receptor subtypes. Accordingly, it is important to understand the structural dynamics of these receptors to develop more effective therapeutics. In this chapter, we describe the latest advances in studies of these two key chemokine receptors with respect to their structures, dynamics and function.