Epik: pKa and Protonation State Prediction through Machine Learning

Postharvest integration of prickly pear betalain-enriched gummies with different sugar substitutes for decoding diabetes type-II and skin resilience - in vitro and in silico study

Postharvest processing plays a crucial role in harnessing the benefits of prickly pear fruit by utilizing betalain as natural colorants to replace artificial colors in model food systems. Prickly pear betalain-enriched gummies were developed using various sugar substitutes, including table sugar, xylitol, stevia, and fructo-oligosaccharides (FOS). These gummies were analyzed for in vitro enzymatic inhibition, anti-inflammatory effects and molecular docking studies for decoding diabetes type-II and skin resilience. FTIR and HPLC confirmed the presence of betalain and isorhamnetin across all gummies. FOS and stevia incorporated gummies exhibited the highest polyphenolics and antioxidant activity. Betalain extract combined with stevia and FOS showed significant in vitro enzyme inhibition compared to other studied gummies. Specifically, FOS gummies showed the highest inhibition rates for α-amylase (23.58 %), α-glucosidase (24.12 %), tyrosinase (54.68 %), and collagenase (2.38 %). Additionally, all samples were non-toxic to RAW cell lines and exhibited anti-inflammatory effects. Molecular docking studies corroborated the in vitro results, and pharmacokinetics profiling confirmed the gummies' suitability for oral consumption and skin safety. The developed prickly pear betalain-enriched gummies, particularly those formulated with fructo-oligosaccharides and stevia, demonstrate significant potential as functional supplements for managing diabetes type-II and skin-related conditions.

Lysine 204 is crucial for the antiport function of the human LAT1 transporter

LAT1 (SLC7A5) catalyzes an antiport reaction of amino acids with specificity towards the essential ones. It is mainly expressed at the Blood Brain Barrier and placenta barriers, but it becomes over-expressed in virtually all human cancers even if originating from tissues with lower expression levels. The antiport reaction of LAT1 is crucial at the BBB since its inherited loss causes Autism Spectrum Disorder. We have investigated the possible molecular determinant of the antiport by site-directed mutagenesis, in vitro transport assay and computational analysis. Previous data indicated that mutation of K204 impairs, but does not knock-out LAT1 functionality. We have investigated the activity changes in the K204Q mutant by following the transport of [3H]-histidine, one of the major substrates, in proteoliposomes harbouring the WT or K204Q. In the mutant, the [3H]-histidine uptake and efflux are not more stimulated by the counter-substrate as they occur in the WT. Moreover, the mutation strongly decreases the substrate affinity and alters the pH dependence of K204Q. Molecular Dynamics analysis correlates well with the experimental data since it shows that substrate prematurely escapes the binding site. In addition, the K204Q shows a strongly increased mobility in those regions, transmembrane domains and random coils, involved in the transport cycle. The identified Lys residue could be responsible of the same phenomenon in those members of the SLC7 family, described as antiporters, in which it is conserved.

Inhibiting brushite crystal growth: molecular docking exploration of Enhydra fluctuans phytoconstituents and their interaction with human serum albumin

In our preliminary in vitro studies, the Enhydra fluctuans extract demonstrated inhibition of calcium phosphate (brushite) crystals. Human serum albumin (HSA) is known to act as a promoter of brushite crystal growth. Therefore, the present study aims to explore the molecular mechanisms involved in brushite crystal nephrolithiasis by conducting molecular docking of phytoconstituents from E. fluctuans with HSA. Molecular docking is conducted on 35 phytoconstituents of E. fluctuans against HSA, and the top five compounds are further analyzed using Induced Fit Docking (IFD) and Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) methods. Molecular dynamics simulations for 50 ns are performed to assess the stability of the protein-ligand complexes. Additionally, in silico physicochemical; absorption, distribution, metabolism, excretion, and toxicity (ADME/T); and pharmacophore modeling studies are conducted. The binding pocket analysis identifies potential binding sites on HSA, and molecular docking reveals Baicalein-7-o-glucoside as the top-performing compound with a strong binding affinity. IFD and MM-GBSA support the stability of the complex. Molecular dynamics simulations indicate stable interactions over the 50 ns period. In silico ADME/T studies suggest that the top five phytoconstituents exhibit drug-like properties with satisfactory pharmacokinetic profiles. Pharmacophore modeling generates a three-point hypothesis, and its validation indicates suitability for the HSA-Baicalein-7-O-glucoside complex. The findings from the current computational investigations indicate that polyphenolic phytoconstituents of E. fluctuans containing the 5,6-dihydroxy chromone ring, such as Baicalein-7-O-diglucoside, may modulate the activity of HSA (PDB ID: 1E7H), potentially inhibiting the process of crystallization.

Mechanistic insights into the selective targeting of P2X3 receptor by camlipixant antagonist

ATP-activated P2X3 receptors play a pivotal role in chronic cough, affecting more than 10% of the population. Despite the challenges posed by the highly conserved structure of P2X receptors, efforts to develop selective drugs targeting P2X3 have led to the development of camlipixant, a potent, selective P2X3 antagonist. However, the mechanisms of receptor desensitization, ion permeation, and structural basis of camlipixant binding to P2X3 remain unclear. Here, we report a cryo-EM structure of camlipixant-bound P2X3, revealing a previously undiscovered selective drug-binding site in the receptor. Our findings also demonstrate that conformational changes in the upper-body domain, including the turret and camlipixant-binding pocket, play a critical role: turret opening facilitates P2X3 channel closure to a radius of 0.7 Å, hindering cation transfer, while turret closure leads to channel opening. Structural and functional studies combined with molecular dynamics simulations provide a comprehensive understanding of camlipixant's selective inhibition of P2X3, offering a foundation for future drug development targeting this receptor.

The effect of cancer-associated mutations on ligand binding and receptor function - A case for the 5-HT2C receptor

The serotonin 5-HT2C receptor is a G protein-coupled receptor (GPCR) mainly expressed in the central nervous system. Besides regulating mood, appetite, and reproductive behavior, it has been identified as a potential target for cancer treatment. In this study, we aimed to investigate the effects of cancer patient-derived 5-HT2C receptor mutations on ligand binding and receptor functionality. By filtering the sequencing data from the Genomic Data Commons data portal (GDC), we selected 12 mutations from multiple cancer types. We found that the affinity of the endogenous agonist serotonin (5-HT) and inverse agonist mesulergine were both drastically decreased by mutations L209HECL2 and F328S6.52, which are located in the orthosteric binding pocket. In the calcium-flux assay, the potency of 5-HT was decreased at F328S6.52, while a trend of increased efficacy was observed. In contrast, 5-HT displayed higher affinity at E306K6.30 and E306A6.30, while a trend of decreased efficacy was observed. These two mutations may disrupt the conserved ionic interaction between E6.30 and R3.50, and thus increase the constitutive activity of the receptor. The inhibitory potency of mesulergine was increased at E306A6.30 but not E306K6.30. Lastly, P365H7.50 decreased the expression level of the receptor by more than ten-fold, which prevented further functional analyses. This study shows that cancer-associated mutations of 5-HT2C receptor have diverse effects on ligand binding and function. Such mutations may affect serotonin-mediated signaling in tumor cells as well as treatment strategies targeting this receptor.

In Silico Enabled Discovery of KAI-11101, a Preclinical DLK Inhibitor for the Treatment of Neurodegenerative Disease and Neuronal Injury

Dual leucine zipper kinase (DLK), expressed primarily in neuronal cells, is a regulator of neuronal degeneration in response to cellular stress from chronic disease or neuronal injury. This makes it an attractive target for the treatment of neurodegenerative diseases such as Alzheimer's, Parkinson's, and amyotrophic lateral sclerosis, and neuronal injury, such as chemotherapy-induced peripheral neuropathy. Here, we describe the discovery of a potent, selective, brain-penetrant DLK inhibitor, KAI-11101 (59). Throughout the program's progression, medicinal chemistry challenges such as potency, hERG inhibition, CNS penetration, CYP3A time-dependent inhibition, and kinase selectivity were overcome through the implementation of cutting-edge in silico tools. KAI-11101 displayed an excellent in vitro safety profile and showed neuroprotective properties in an ex vivo axon fragmentation assay as well as dose-dependent activity in a mouse PD model.

3D variability analysis reveals a hidden conformational change controlling ammonia transport in human asparagine synthetase

Advances in X-ray crystallography and cryogenic electron microscopy (cryo-EM) offer the promise of elucidating functionally relevant conformational changes that are not easily studied by other biophysical methods. Here we show that 3D variability analysis (3DVA) of the cryo-EM map for wild-type (WT) human asparagine synthetase (ASNS) identifies a functional role for the Arg-142 side chain and test this hypothesis experimentally by characterizing the R142I variant in which Arg-142 is replaced by isoleucine. Support for Arg-142 playing a role in the intramolecular translocation of ammonia between the active site of the enzyme is provided by the glutamine-dependent synthetase activity of the R142 variant relative to WT ASNS, and MD simulations provide a possible molecular mechanism for these findings. Combining 3DVA with MD simulations is a generally applicable approach to generate testable hypotheses of how conformational changes in buried side chains might regulate function in enzymes.

De Novo Discovery of a Noncovalent Cell-Penetrating Bicyclic Peptide Inhibitor Targeting SARS-CoV-2 Main Protease

Macrocyclic peptides have garnered significant attention as promising drug candidates. However, they typically face challenges in achieving and enhancing cell permeability for access to intracellular targets. In this study, we focused on the de novo screening of macrocyclic peptide inhibitors against the main protease (Mpro) of SARS-CoV-2 and identified novel noncovalently bound macrocyclic peptides that effectively inhibit proteolytic activity. High-resolution crystal structures further revealed molecular interactions between the macrocyclic peptides and Mpro. Subsequently, a specific macrocyclic peptide lacking cell permeability was further optimized and transformed into a low-toxicity, metabolically stable bicyclic peptide with a cell penetration capacity and therapeutic potential against SARS-CoV-2. The bicyclic peptide was achieved using a novel strategy that involved introducing both a bicyclic structure and a bridging perfluorobiphenyl group. Our study not only provides a lead peptide inhibitor for COVID-19 but also offers valuable insights into achieving cell penetration for macrocyclic peptides through strategic modifications.

Discovery of ERD-1233 as a Potent and Orally Efficacious Estrogen Receptor PROTAC Degrader for the Treatment of ER+ Human Breast Cancer

Despite the development of highly effective therapies for the treatment of estrogen receptor α (ERα)-positive human breast cancer, clinical resistance to current therapies requires the development of novel therapeutic strategies. Herein, we report the discovery of ERD-1233 as a potent and orally efficacious ERα degrader designed using the PROTAC technology. ERD-1233 was developed based on Lasofoxifene as the ER binding moiety and a novel cereblon ligand through extensive optimization of the linker. ERD-1233 potently and effectively reduces the ERα protein in vitro and achieves excellent oral bioavailability in mice and rats. Oral administration of ERD-1233 effectively reduces ER protein in ER+ tumors and achieves tumor regression in the ER wild-type MCF-7 xenograft tumor model and strong tumor growth inhibition in the ESR1Y537S mutated model in mice. Our data demonstrate that ERD-1233 is a promising ER PROTAC degrader for extensive evaluation as a new therapy for the treatment of ER+ human breast cancer.

Inhibition of Pseudomonas aeruginosa Carbonic Anhydrases, Exploring Ciprofloxacin Functionalization Toward New Antibacterial Agents: An In-Depth Multidisciplinary Study

Ciprofloxacin (CPX) is one of the most employed antibiotics in clinics to date. However, the rise of drug-resistant bacteria is dramatically impairing its efficacy, especially against life-threatening pathogens, such as Pseudomonas aeruginosa. This Gram-negative bacterium is an opportunistic pathogen, often infecting immuno-compromised patients with severe or fatal outcomes. The evidence of the possibility of exploiting Carbonic Anhydrase (CA, EC: 4.2.1.1) enzymes as pharmacological targets along with their role in P. aeruginosa virulence inspired the derivatization of CPX with peculiar CA-inhibiting chemotypes. Thus, a large library of CPX derivatives was synthesized and tested on a panel of bacterial CAs and human isoenzymes I and II. Selected derivatives were evaluated for antibacterial activity, revealing bactericidal and antibiofilm properties for some compounds. Importantly, promising preliminary absorption, distribution, metabolism, and excretion (ADME) properties in vitro were found and no cytotoxicity was detected for some representative compounds when tested in Galleria mellonella larvae.

Recent Applications of In Silico Approaches for Studying Receptor Mutations Associated with Human Pathologies

In recent years, the advent of computational techniques to predict the potential activity of a drug interacting with a receptor or to predict the structure of unidentified proteins with aberrant characteristics has significantly impacted the field of drug design. We provide a comprehensive review of the current state of in silico approaches and software for investigating the effects of receptor mutations associated with human diseases, focusing on both frequent and rare mutations. The reported techniques include virtual screening, homology modeling, threading, docking, and molecular dynamics. This review clearly shows that it is common for successful studies to integrate different techniques in drug design, with docking and molecular dynamics being the most frequently used techniques. This trend reflects the current emphasis on developing novel therapies for diseases resulting from receptor mutations with the recently discovered AlphaFold algorithm as the driving force.

Paraherquamides - A new hope and great expectations of anthelmintic agents: Computational studies

Nematode infections impose a significant health and economic burden, particularly as parasites develop resistance to existing treatments and evade host defenses. This study explores the efficacy of 48 paraherquamide analogs, a class of polycyclic spiro-oxindole alkaloids with unique structural features, as potential anthelmintic agents. Employing advanced computational methods, including molecular docking, MM-GBSA, and molecular dynamics simulations, we assessed the interaction of these analogs with the Ls-AchBP receptor, a model for nematode neurotransmission. Among the analogs studied, Paraherquamide K, Mangrovamide A, and Chrysogenamide A showed comparable docking and MM-GBSA scores to the native antagonist. Notably, their binding interactions exhibited slight distinction attributed to structural differences, such as the absence of a di-oxygenated 7-membered ring. Additionally, these analogs demonstrated robust binding stability in the molecular dynamic simulation studies and favorable pharmacokinetic properties in our in-silico ADME assessment. The insights gained from the study highlight the potential of these analogs as a basis for developing new therapeutics for nematode infections. The promising results from this computational analysis set the stage for subsequent in-vivo validations and pre-clinical studies, contributing to the arsenal against parasitic resistance.

Relevance and Potential Applications of C2-Carboxylated 1,3-Azoles

Carbon dioxide (CO2) is an economically viable and abundant carbon source that can be incorporated into compounds such as C2-carboxylated 1,3-azoles relevant to the pharmaceutical, cosmetics, and pesticide industries. Of the 2.4 million commercially available C2-unsubstituted 1,3-azole compounds, less than 1 % are currently purchasable as their C2-carboxylated derivatives, highlighting the substantial gap in compound availability. This availability gap leaves ample opportunities for exploring the synthetic accessibility and use of carboxylated azoles in bioactive compounds. In this study, we analyze and quantify the relevance of C2-carboxylated 1,3-azoles in small-molecule research. An analysis of molecular databases such as ZINC, ChEMBL, COSMOS, and DrugBank identified relevant C2-carboxylated 1,3-azoles as anticoagulant and aroma-giving compounds. Moreover, a pharmacophore analysis highlights promising pharmaceutical potential associated with C2-carboxylated 1,3-azoles, revealing the ATP-sensitive inward rectifier potassium channel 1 (KATP) and Kinesin-like protein KIF18 A as targets that can potentially be addressed with C2-carboxylated 1,3-azoles. Moreover, we identified several bioisosteres of C2-carboxylated 1,3-azoles. In conclusion, further exploration of the chemical space of C2-carboxylated 1,3-azoles is recommended to harness their full potential in drug discovery and related fields.

Identification of new potential inhibitors of pteridine reductase-1 (PTR1) via biophysical and biochemical mechanism-based approaches: Step towards the treatment of Leishmaniasis

Leishmaniasis is a parasitic disease, which spreads from the bite of an infected Phlebotomine fly to human hosts. The disease is characterized by a number of clinical manifestations, such as ulcerative lesions at the site of sandfly bite (cutaneous form), inflammation of mucosal membranes (mucosal leishmaniasis) or the deadly visceral form. This study was aimed to target pteridine reductase-1 (PTR1), a member of short chain dehydrogenases, which accounts for the reduction of conjugated and unconjugated pterins in Leishmania parasite. The ptr1-pET28a+-tev construct was expressed using BL21 (DE3) cells, followed by two tandem purification steps including affinity and gel permeation chromatography. In the next phase, functional studies of PTR1 were performed via screening of an in-house library of 500 compounds. The biochemical-mechanism based assay of PTR1 identified 11 hits that were also found to be non-cytotoxic against human fibroblast cell line (BJ) (except compound 6), and thus further studied via computational technique and saturation transfer difference-nuclear magnetic resonance (STD-NMR) spectroscopy. These high throughput techniques identified six compounds 2, 4, 5, 7, 9, and 11 as active, which were then assessed via in-vitro assay. Among them, compounds 2, 4, and 7 showed substantial leishmanicidal activity, comparable to the standard drug, miltefosine (IC50 value: 31.8 ± 0.2 μM). These results narrowed down the search to 3 compounds as potential leads, with prominent protein-ligand interaction profiles. Hence, the respective compounds can be further assessed for their therapeutic potential against leishmaniasis.

Molecular Modeling and In Vitro Functional Analysis of the RGS12 PDZ Domain Variant Associated with High-Penetrance Familial Bipolar Disorder

Bipolar disorder's etiology involves genetics, environmental factors, and gene-environment interactions, underlying its heterogeneous nature and treatment complexity. In 2020, Forstner and colleagues catalogued 378 sequence variants co-segregating with familial bipolar disorder. A notable candidate was an R59Q missense mutation in the PDZ (PSD-95/Dlg1/ZO-1) domain of RGS12. We previously demonstrated that RGS12 loss removes negative regulation on the kappa opioid receptor, disrupting basal ganglia dopamine homeostasis and dampening responses to dopamine-eliciting psychostimulants. Here, we investigated the R59Q variation in the context of potential PDZ domain functional alterations. We first validated a new target for the wildtype RGS12 PDZ domain-the SAPAP3 C-terminus-by molecular docking, surface plasmon resonance (SPR), and co-immunoprecipitation. While initial molecular dynamics (MD) studies predicted negligible effects of the R59Q variation on ligand binding, SPR showed a significant reduction in binding affinity for the three peptide targets tested. AlphaFold2-generated models predicted a modest reduction in protein-peptide interactions, which is consistent with the reduced binding affinity observed by SPR, suggesting that the substituted glutamine side chain may weaken the affinity of RGS12 for its in vivo binding targets, likely through allosteric changes. This difference may adversely affect the CNS signaling related to dynorphin and dopamine in individuals with this R59Q variation, potentially impacting bipolar disorder pathophysiology.

Computational Approach to Identifying New Chemical Entities as Elastase Inhibitors with Potential Antiaging Effects

In the aging process, skin morphology might be affected by wrinkle formation due to the loss of elasticity and resilience of connective tissues linked to the cleavage of elastin by the enzymatic activity of elastase. Little information is available about the structural requirements to efficiently inhibit elastase 1 (EC 3.4.21.36) expressed in skin keratinocytes. In this study, a structure-based approach led to the identification to the pharmacophoric hypotheses that described the main structural requirements for binding to porcine pancreatic elastase as a valuable tool for the development of skin therapeutic agents due to its similarity with human elastase 1. The obtained models were subsequently refined through the application of computational alanine-scanning mutagenesis to evaluate the effect of single residues on the binding affinity and protein stability; in turn, molecular dynamic simulations were carried out; these procedures led to a simplified model bearing few essential features, enabling a reliable collection of chemical features for their interactions with elastase. Then, a virtual screening campaign on the in-house library of synthetic compounds led to the identification of a nonpeptide-based inhibitor (IC50 = 60.4 µM) belonging to the class of N-substituted-1H-benzimidazol-2-yl]thio]acetamides, which might be further exploited to obtain more efficient ligands of elastase for therapeutic applications.

Phosphorylation-driven epichaperome assembly is a regulator of cellular adaptability and proliferation

The intricate network of protein-chaperone interactions is crucial for maintaining cellular function. Recent discoveries have unveiled the existence of specialized chaperone assemblies, known as epichaperomes, which serve as scaffolding platforms that orchestrate the reconfiguration of protein-protein interaction networks, thereby enhancing cellular adaptability and proliferation. This study explores the structural and regulatory aspects of epichaperomes, with a particular focus on the role of post-translational modifications (PTMs) in their formation and function. A key finding is the identification of specific PTMs on HSP90, particularly at residues Ser226 and Ser255 within an intrinsically disordered region, as critical determinants of epichaperome assembly. Our data demonstrate that phosphorylation of these serine residues enhances HSP90's interactions with other chaperones and co-chaperones, creating a microenvironment conducive to epichaperome formation. Moreover, we establish a direct link between epichaperome function and cellular physiology, particularly in contexts where robust proliferation and adaptive behavior are essential, such as in cancer and pluripotent stem cell maintenance. These findings not only provide mechanistic insights but also hold promise for the development of novel therapeutic strategies targeting chaperone assemblies in diseases characterized by epichaperome dysregulation, thereby bridging the gap between fundamental research and precision medicine.

Exploring Niclosamide as a Multi-target Drug Against SARS-CoV-2: Molecular Dynamics Simulation Studies on Host and Viral Proteins

Niclosamide has emerged as a promising repurposed drug against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In vitro studies suggested that niclosamide inhibits the host transmembrane protein 16F (hTMEM16F), crucial for lipid scramblase activity, which consequently reduces syncytia formation that aids viral spread. Based on other in vitro reports, niclosamide may also target viral proteases such as papain-like protease (PLpro) and main protease (Mpro), essential for viral replication and maturation. However, the precise interactions by which niclosamide interacts with these multiple targets remain largely unclear. Docking and molecular dynamics (MD) simulation studies were undertaken based on a homology model of the hTMEM16F and available crystal structures of SARS-CoV-2 PLpro and Mpro. Niclosamide was observed to bind stably throughout a 400 ns MD simulation at the extracellular exit gate of the hTMEM16F tunnel, forming crucial interactions with residues spanning the TM1-TM2 loop (Gln350), TM3 (Phe481), and TM5-TM6 loop (Lys573, Glu594, and Asp596). Among the SARS-CoV-2 proteases, niclosamide was found to interact effectively with conserved active site residues of PLpro (Tyr268), exhibiting better stability in comparison to the control inhibitor, GRL0617. In conclusion, our in silico analyses support niclosamide as a multi-targeted drug inhibiting viral and host proteins involved in SARS-CoV-2 infections.

Computational and in vitro binding studies of theophylline against phosphodiesterases functioning in sperm in presence and absence of pentoxifylline

Fertility is a result of a synergy among the sperm's various functions including capacitation, motility, chemotaxis, acrosome reaction, and, finally, the fertilization of the oocyte. Subpar motility is the most common cause of infertility in males. Cyclic adenosine monophosphate (cAMP) signalling underlies motility and is depleted by the phosphodiesterases (PDEs) in sperm, such as PDE10A, PDE1, and PDE4. Therefore, the PDE inhibitor (PDEI) category of fertility drugs aim to enhance motility in assisted reproduction technologies (ARTs) through inhibition of PDEs, though they might have adverse effects on other physiological variables. For example, the popular drug pentoxifylline (PTX), widely used in ARTs, improves motility but causes premature acrosome reaction and exerts toxicity on the fertilized oocyte. Another xanthine-derived drug, theophylline (TP), has been repurposed for treating infertility, but its mechanism of PDE inhibition remains unexplored. Here, using biophysical and computational approaches, we identified that TP binds to the same binding pocket as PTX with higher affinity than PTX. We also found that PTX and TP co-bind to the same binding pocket, but at different sites.

Edaravone N-benzyl pyridinium derivatives: BACE-1 inhibition, kinetics and in silico binding pose determination

BACE-1 plays a pivotal role in the production of β-amyloid (Aβ) peptides, implicated in Alzheimer's Disease (AD) pathology. We previously described edaravone N-benzyl pyridinium derivatives (EBPDs) that exhibited multifunctional activity against multiple AD targets. In this study we explored the EBPDs BACE-1 inhibitory activity to potentially enhance the compounds therapeutic profile. The EBPDs exhibited moderate BACE-1 inhibitory activity (IC50 = 44.10 µM - 123.70 µM) and obtained IC50 values between 2.0 and 5.8-fold greater than resveratrol, a known BACE-1 inhibitor (IC50 = 253.20 µM), in this assay. Compound 3 was the most potent inhibitor with an IC50 of 44.10 µM and a Ki of 19.96 µM and a mixed-type mode of inhibition that favored binding in a competitive manner. Molecular docking identified crucial interactions with BACE-1 active site residues, supported by 100 ns MD simulations. The study highlighted the EBPDs therapeutic potential as BACE-1 inhibitors and multifunctional anti-AD therapeutic agents.

Phytochemical investigation of Chrysanthellum americanum Vatke and its constituents- a targeted approach for the treatment of leishmaniasis

The present study is focused on the isolation and identification of new therapeutic candidates from Chrysanthellum americanum Vatke., and their efficacy against pteridine reductase-1 (PTR1), a valid chemotherapeutic target in the Leishmania parasite. Henceforth, a new compound, chrysanamerine (1), along with 7 known compounds, polyacetylene 2, and flavonoids 3-8, were isolated from C. americanum. Their structures were determined by chemical and spectroscopic analyses and compared with the reported spectroscopic data. All compounds were evaluated for their anti-leishmanial activity against PTR1 via biochemical mechanism-based assay. The in vitro results showed five potential hits including a new compound, chrysanamerine (1), and four known compounds against the PTR1 enzyme. Among them, compound 1 showed a potent enzyme inhibition with an IC50 of 31.02 ± 2.36 μM, whereas a moderate inhibition was observed in cases of compounds 5 and 6 (IC50 = 59.86 ± 3.32, and 45.32 ± 3.5 μM, respectively). Whereas, compounds 3 and 8 showed mild inhibition (IC50 = 72.12 ± 1.12, and 97.18 ± 1.23 μM, respectively) against PTR1, compared with trimethoprim (positive control) (IC50 = 21.07 ± 1.6 μM). Moreover, the results were further validated via molecular docking and molecular dynamics (MD) simulations. Compound 1 showed a strong affinity to the binding site with a docking score of -11.83, along with the formation of a stable protein-ligand complex over the trajectory of 100 ns. Besides, compounds 1-8 were found to be non-cytotoxic on BJ (human fibroblast) cells.

Dual activity of Minnelide chemosensitize basal/triple negative breast cancer stem cells and reprograms immunosuppressive tumor microenvironment

Triple negative breast cancer (TNBC) subtype is characterized with higher EMT/stemness properties and immune suppressive tumor microenvironment (TME). Women with advanced TNBC exhibit aggressive disease and have limited treatment options. Although immune suppressive TME is implicated in driving aggressive properties of basal/TNBC subtype and therapy resistance, effectively targeting it remains a challenge. Minnelide, a prodrug of triptolide currently being tested in clinical trials, has shown anti-tumorigenic activity in multiple malignancies via targeting super enhancers, Myc and anti-apoptotic pathways such as HSP70. Distinct super-enhancer landscape drives cancer stem cells (CSC) in TNBC subtype while inducing immune suppressive TME. We show that Minnelide selectively targets CSCs in human and murine TNBC cell lines compared to cell lines of luminal subtype by targeting Myc and HSP70. Minnelide in combination with cyclophosphamide significantly reduces the tumor growth and eliminates metastasis by reprogramming the tumor microenvironment and enhancing cytotoxic T cell infiltration in 4T1 tumor-bearing mice. Resection of residual tumors following the combination treatment leads to complete eradication of disseminated tumor cells as all mice are free of local and distant recurrences. All control mice showed recurrences within 3 weeks of post-resection while single Minnelide treatment delayed recurrence and one mouse was free of tumor. We provide evidence that Minnelide targets tumor intrinsic pathways and reprograms the immune suppressive microenvironment. Our studies also suggest that Minnelide in combination with cyclophosphamide may lead to durable responses in patients with basal/TNBC subtype warranting its clinical investigation.

Bridging Machine Learning and Thermodynamics for Accurate pK a Prediction

Integrating scientific principles into machine learning models to enhance their predictive performance and generalizability is a central challenge in the development of AI for Science. Herein, we introduce Uni-pK a, a novel framework that successfully incorporates thermodynamic principles into machine learning modeling, achieving high-precision predictions of acid dissociation constants (pK a), a crucial task in the rational design of drugs and catalysts, as well as a modeling challenge in computational physical chemistry for small organic molecules. Uni-pK a utilizes a comprehensive free energy model to represent molecular protonation equilibria accurately. It features a structure enumerator that reconstructs molecular configurations from pK a data, coupled with a neural network that functions as a free energy predictor, ensuring high-throughput, data-driven prediction while preserving thermodynamic consistency. Employing a pretraining-finetuning strategy with both predicted and experimental pK a data, Uni-pK a not only achieves state-of-the-art accuracy in chemoinformatics but also shows comparable precision to quantum mechanics-based methods.

Investigation of small molecules disrupting dengue virus assembly by inhibiting capsid protein and blocking RNA encapsulation

Dengue fever is a significant global public health concern, causing substantial morbidity and mortality worldwide. The disease can manifest in various forms, from mild fever to potentially life-threatening complications. Developing effective treatments remains a critical challenge to healthcare systems. Despite extensive research, no antiviral drugs have been approved for either the prevention or treatment of dengue. Targeting the virus during its early phase of attachment is essential to inhibit viral replication. The capsid protein plays a crucial role in the virus's structural integrity, assembly, and viral genome release. In the present study, we employed a computational approach focused on the capsid protein to identify possible potent inhibitors against the dengue virus from a library of FDA-approved drugs. We employed high-throughput virtual screening on FDA-approved drugs to identify drug molecules that could potentially combat the disease and save both cost and time. The screening process identified four drug molecules (Nordihydroguaiaretic acid, Ifenprodil tartrate, Lathyrol, and Safinamide Mesylate) based on their highest binding affinity and MM/GBSA scores. Among these, Nordihydroguaiaretic acid showed higher binding affinity than the reference molecule with - 11.66 kcal/mol. In contrast, Ifenprodil tartrate and Lathyrol showed similar results to the reference molecule, with binding energies of - 9.42 kcal/mol and - 9.29 kcal/mol, respectively. Following the screening, molecular dynamic simulations were performed to explore the molecular stability and conformational possibilities. The drug molecules were further supported by post-molecular simulation analysis. Furthermore, binding energies were also computed using the MM/GBSA approach, and the free energy landscape was used to calculate the different transition states, revealing that the drugs exhibited significant transition states. Specifically, Nordihydroguaiaretic acid and Ifenprodil tartrate displayed higher flexibility, while Lathyrol and Safinamide Mesylate showed more predictable and consistent protein folding. This significant breakthrough offers new hope against dengue, highlighting the power of computational drug discovery in identifying potent inhibitors and paving the way for novel treatment approaches.

ANI neural network potentials for small molecule pKa prediction

The pKa value of a molecule is of interest to chemists across a broad spectrum of fields including pharmacology, environmental chemistry and theoretical chemistry. Determination of pKa values can be accomplished through several experimental methods such as NMR techniques and titration together with computational techniques such as DFT calculations. However, all of these methods remain time consuming and computationally expensive. In this work we develop a method for the rapid calculation of pKa values of small molecules which utilises a combination of neural network potentials, low energy conformer searches and thermodynamic cycles. We show that neural network potentials trained on different phase and charge states can be employed in tandem to predict the full thermodynamic energy cycle of molecules. Focusing here on imidazolium derived carbene species, the method utilised can easily be extended to other functional groups of interest such as amines with further training.

Structural Models of Human Norepinephrine Transporter Ensemble Reveal the Allosteric Sites and Ligand-Binding Mechanism

The norepinephrine transporter (NET) plays a pivotal role in recycling norepinephrine (NE) from the synaptic cleft. However, the structures referring to the conformational heterogeneity of NET during the transport cycle remain poorly understood. Here, three structural models of NE bound to the orthosteric site of NET in outward-open (OOholo), outward-occluded (OCholo), and inward-open (IOholo) conformations were first obtained using the multistate structures of serotonin transporter as templates and further characterized through Gaussian-accelerated molecular dynamics and free energy reweighting. Analysis of the structures revealed eight potential allosteric sites on the functional-specific states of NET. One of the pharmacologically relevant pockets located at the extracellular vestibule was further verified by simulating the binding behaviors of a clinical trial drug χ-MrIA that is allosterically regulating NET. These structural and energetic insights into NET advanced our understanding of NE reuptake and paved the way for discovering novel molecules targeting the allosteric sites.

Modulation of endogenous opioid signaling by inhibitors of puromycin-sensitive aminopeptidase

The endogenous opioid system regulates pain through local release of neuropeptides and modulation of their action on opioid receptors. However, the effect of opioid peptides, the enkephalins, is short-lived due to their rapid hydrolysis by enkephalin-degrading enzymes. In turn, an innovative approach to the management of pain would be to increase the local concentration and prolong the stability of enkephalins by preventing their inactivation by neural enkephalinases such as puromycin-sensitive aminopeptidase (PSA). Our previous structure-activity relationship studies offered the S-diphenylmethyl cysteinyl derivative of puromycin (20) as a nanomolar inhibitor of PSA. This chemical class, however, suffered from undesirable metabolism to nephrotoxic puromycin aminonucleoside (PAN). To prevent such toxicity, we designed and synthesized 5'-chloro substituted derivatives. The compounds retained the PSA inhibitory potency of the corresponding 5'-hydroxy analogs and had improved selectivity toward PSA. In vivo treatment with the lead compound 19 caused significantly reduced pain response in antinociception assays, alone and in combination with Met-enkephalin. The analgesic effect was reversed by the opioid antagonist naloxone, suggesting the involvement of opioid receptors. Further, PSA inhibition by compound 19 in brain slices caused local increase in endogenous enkephalin levels, corroborating our rationale. Pharmacokinetic assessment of compound 19 showed desirable plasma stability and identified the cysteinyl sulfur as the principal site of metabolic liability. We gained additional insight into inhibitor-PSA interactions by molecular modeling, which underscored the importance of bulky aromatic amino acid in puromycin scaffold. The results of this study strongly support our rationale for the development of PSA inhibitors for effective pain management.

Integrating Blood Biomarkers and Marine Brown Algae-Derived Inhibitors in Parkinson's Disease: A Multi-scale Approach from Interactomics to Quantum Mechanics

Parkinson's disease (PD) involves alpha-synuclein accumulation according to Braak's pattern, with diverse clinical progressions that complicate diagnosis and treatment. We aimed to correlate Braak's pattern with rapid progressive PD to identify blood-based biomarkers and therapeutic targets exploiting brown algae-derived bioactives for potential treatment. We implemented a systematic workflow of transcriptomic profiling, co-expression networks, cluster profiling, transcriptional regulator identification, molecular docking, quantum calculations, and dynamic simulations. The transcriptomic analyses exhibited highly expressed genes at each Braak's stage and in rapidly progressive PD. Co-expression networks for Braak's stages were built, and the top five clusters from each stage displayed significant overlap with differentially expressed genes in rapidly progressive PD, indicating shared biomarkers between the blood and the PD brain. Further investigation showed, NF-kappa-B p105 as the master transcriptional regulator of these biomarkers. Molecular docking screened phlorethopentafuhalol-A from brown algae, exhibiting a superior inhibitory effect with p105 (- 7.51 kcal/mol) by outperforming PD drugs and anti-inflammatory compounds (- 5.73 to - 4.38 kcal/mol). Quantum mechanics and molecular mechanics (QM/MM) calculations and dynamic simulations have confirmed the interactive stability of phlorethopentafuhalol-A with p105. Overall, our combined computational study shows that phlorethopentafuhalol-A derived from brown algae, may have healing properties that could help treat PD.

Discovery and characterization of novel FGFR1 inhibitors in triple-negative breast cancer via hybrid virtual screening and molecular dynamics simulations

The overexpression of FGFR1 is thought to significantly contribute to the progression of triple-negative breast cancer (TNBC), impacting aspects such as tumorigenesis, growth, metastasis, and drug resistance. Consequently, the pursuit of effective inhibitors for FGFR1 is a key area of research interest. In response to this need, our study developed a hybrid virtual screening method. Utilizing KarmaDock, an innovative algorithm that blends deep learning with molecular docking, alongside Schrödinger's Residue Scanning. This strategy led us to identify compound 6, which demonstrated promising FGFR1 inhibitory activity, evidenced by an IC50 value of approximately 0.24 nM in the HTRF bioassay. Further evaluation revealed that this compound also inhibits the FGFR1 V561M variant with an IC50 value around 1.24 nM. Our subsequent investigations demonstrate that Compound 6 robustly suppresses the migration and invasion capacities of TNBC cell lines, through the downregulation of p-FGFR1 and modulation of EMT markers, highlighting its promise as a potent anti-metastatic therapeutic agent. Additionally, our use of molecular dynamics simulations provided a deeper understanding of the compound's specific binding interactions with FGFR1.

Cryo-EM structure of the dopamine transporter with a novel atypical non-competitive inhibitor bound to the orthosteric site

The regulation of dopamine (DA) removal from the synaptic cleft is a crucial process in neurotransmission and is facilitated by the sodium- and chloride-coupled dopamine transporter DAT. Psychostimulant drugs, cocaine, and amphetamine, both block the uptake of DA, while amphetamine also triggers the release of DA. As a result, they prolong or even amplify neurotransmitter signaling. Atypical inhibitors of DAT lack cocaine-like rewarding effects and offer a promising strategy for the treatment of drug use disorders. Here, we present the 3.2 Å resolution cryo-electron microscopy structure of the Drosophila melanogaster dopamine transporter (dDAT) in complex with the atypical non-competitive inhibitor AC-4-248. The inhibitor partially binds at the central binding site, extending into the extracellular vestibule, and locks the transporter in an outward open conformation. Our findings propose mechanisms for the non-competitive inhibition of DAT and attenuation of cocaine potency by AC-4-248 and provide a basis for the rational design of more efficacious atypical inhibitors.

Caught between a ROCK and a hard place: current challenges in structure-based drug design

The discipline of structure-based drug design (SBDD) is several decades old and it is tempting to think that the proliferation of experimental structures for many drug targets might make computer-aided drug design (CADD) straightforward. However, this is far from true. In this review, we illustrate some of the challenges that CADD scientists face every day in their work, even now. We use Rho-associated protein kinase (ROCK), and public domain structures and data, as an example to illustrate some of the challenges we have experienced during our project targeting this protein. We hope that this will help to prevent unrealistic expectations of what CADD can accomplish and to educate non-CADD scientists regarding the challenges still facing their CADD colleagues.

Computational analysis of human gut microbial prolyl oligopeptidases (POPs) reveal candidate genes as therapeutics for celiac disease

Celiac disease (CD) is a common autoimmune disorder in which the patients are unable to digest gluten, which is present in foods made up of wheat, barley and rye. Whilst diagnosis happens late in 80% of the cases, avoidance of such foods appears to be the common solution. Alternative management strategies are required for the patients and their families since CD is also genetically carried over. Probiotic therapeutics and the consumption of appropriate enzymes, such as prolyloligopeptidases (POPs), from gut-friendly bacteria could reduce the disease burden and provide a better lifestyle for CD patients. We have examined around 5000 gut bacterial genomes and identified nearly 4000 non-redundant putative POPs. A select set of 10 gut bacterial POP sequences were subject to three-dimensional modelling, ligand docking and molecular dynamics simulations where stable interactions were observed between the POPs and gluten peptides. Our study provides sequence and structural analysis of potential POP enzymes in gut bacterial genomes, which form a strong basis to offer probiotic solutions to CD patients. In particular, these enzymes could be lead future therapeutics for this disease.

QupKake: Integrating Machine Learning and Quantum Chemistry for Micro-pKa Predictions

Accurate prediction of micro-pKa values is crucial for understanding and modulating the acidity and basicity of organic molecules, with applications in drug discovery, materials science, and environmental chemistry. This work introduces QupKake, a novel method that combines graph neural network models with semiempirical quantum mechanical (QM) features to achieve exceptional accuracy and generalization in micro-pKa prediction. QupKake outperforms state-of-the-art models on a variety of benchmark data sets, with root-mean-square errors between 0.5 and 0.8 pKa units on five external test sets. Feature importance analysis reveals the crucial role of QM features in both the reaction site enumeration and micro-pKa prediction models. QupKake represents a significant advancement in micro-pKa prediction, offering a powerful tool for various applications in chemistry and beyond.

Presenting a new fluorescent probe, methyl(10-phenylphenanthren-9-yl)sulfane sensitive to the polarity and rigidity of the microenvironment: applications toward microheterogeneous systems

A molecule, methyl(10-phenylphenanthren-9-yl)sulfane (MPPS), with a straightforward structure, has been synthesized, characterized, and explored as a new fluorescent probe for microheterogeneous systems. The photophysical properties of MPPS have been studied through experimental and theoretical calculations using the range-separated hybrid functional CAM-B3LYP in conjunction with a 6-311++g(d,p) basis set. Theoretical calculations show that the freely rotating phenyl ring forms a 94° dihedral angle with the phenanthrene ring in the ground state. Experimentally found two absorption bands correspond to the n → π* and π → π* transitions supported by the frontier molecular orbital calculations. Two excited singlet states, E-1 and E-2 (the former being more stable than the latter in the gas phase), exist with dihedral angles between the phenyl and phenanthrene rings as 142° and 133°, respectively, in the gas phase. Two emitting states in a condensed medium of varying polarities are supported by the steady-state fluorescence and fluorescence intensity decay data. Emission energies, fluorescence intensities, and excited singlet state lifetimes change with the polarity of the solvents. To support that the free rotation in the molecule is responsible for these changes, the fluorescence properties of another molecule, methyl(10-(o-tolyl)phenanthren-9-yl)sulfane (MTPS), with restricted rotation of the substituted benzene, i.e., o-tolyl ring have been studied. The fast-intensity decay component of MPPS is ascribed to the conformer in the E-1 state. The molecule has proved to be an excellent polarity probe explored to determine the critical micelle concentrations (cmc) values of different surfactants, which agree well with the literature reports. Different regions of binding isotherm (specific, non-cooperative, cooperative, and massive binding) of a gemini surfactant, 12-6-12,2Br- with bovine serum albumin (BSA) have been successfully demonstrated by the steady-state and time-resolved fluorescence and fluorescence anisotropic properties of MPPS. Docking results show that MPPS resides in the hydrophobic pocket of BSA. The fluorescence quenching of BSA by MPPS reveals the location of Trp residues of BSA. Thus, a polarity and molecular rigidity-sensitive fluorescent molecule, MPPS has been presented here that can potentially be used to monitor the changes in the microenvironment of biomolecules in different processes.

Quantum chemical package Jaguar: A survey of recent developments and unique features

This paper is dedicated to the quantum chemical package Jaguar, which is commercial software developed and distributed by Schrödinger, Inc. We discuss Jaguar's scientific features that are relevant to chemical research as well as describe those aspects of the program that are pertinent to the user interface, the organization of the computer code, and its maintenance and testing. Among the scientific topics that feature prominently in this paper are the quantum chemical methods grounded in the pseudospectral approach. A number of multistep workflows dependent on Jaguar are covered: prediction of protonation equilibria in aqueous solutions (particularly calculations of tautomeric stability and pKa), reactivity predictions based on automated transition state search, assembly of Boltzmann-averaged spectra such as vibrational and electronic circular dichroism, as well as nuclear magnetic resonance. Discussed also are quantum chemical calculations that are oriented toward materials science applications, in particular, prediction of properties of optoelectronic materials and organic semiconductors, and molecular catalyst design. The topic of treatment of conformations inevitably comes up in real world research projects and is considered as part of all the workflows mentioned above. In addition, we examine the role of machine learning methods in quantum chemical calculations performed by Jaguar, from auxiliary functions that return the approximate calculation runtime in a user interface, to prediction of actual molecular properties. The current work is second in a series of reviews of Jaguar, the first having been published more than ten years ago. Thus, this paper serves as a rare milestone on the path that is being traversed by Jaguar's development in more than thirty years of its existence.

Structure-based design of multitargeting ChEs-MAO B inhibitors based on phenyl ring bioisosteres: AChE/BChE selectivity switch and drug-like characterization

A structure-based drug design approach was focused on incorporating phenyl ring heterocyclic bioisosteres into coumarin derivative 1, previously reported as potent dual AChE-MAO B inhibitor, with the aim of improving drug-like features. Structure-activity relationships highlighted that bioisosteric rings were tolerated by hMAO B enzymatic cleft more than hAChE. Interestingly, linker homologation at the basic nitrogen enabled selectivity to switch from hAChE to hBChE. In the present work, we identified thiophene-based isosteres 7 and 15 as dual AChE-MAO B (IC50 = 261 and 15 nM, respectively) and BChE-MAO B (IC50 = 375 and 20 nM, respectively) inhibitors, respectively. Both 7 and 15 were moderately water-soluble and membrane-permeant agents by passive diffusion (PAMPA-HDM). Moreover, they were able to counteract oxidative damage induced by both H2O2 and 6-OHDA in SH-SY5Y cells and predicted to penetrate into CNS in a cell-based model mimicking blood-brain barrier. Molecular dynamics (MD) simulations shed light on key differences in AChE and BChE recognition processes promoted by the basic chain homologation from 7 to 15.

Computational Insights into Papaveroline as an In Silico Drug Candidate for Alzheimer's Disease via Fyn Tyrosine Kinase Inhibition

Alzheimer's disease (AD) poses a significant global health challenge, necessitating the exploration of novel therapeutic strategies. Fyn Tyrosine Kinase has emerged as a key player in AD pathogenesis, making it an attractive target for drug development. This study focuses on investigating the potential of Papaveroline as a drug candidate for AD by targeting Fyn Tyrosine Kinase. The research employed high-throughput virtual screening and QSAR analysis were conducted to identify compounds with optimal drug-like properties, emphasizing adherence to ADMET parameters for further evaluation. Molecular dynamics simulations to analyze the binding interactions between Papaveroline and Staurosporine with Fyn Tyrosine Kinase over a 200-ns period. The study revealed detailed insights into the binding mechanisms and stability of the Papaveroline-Fyn complex, showcasing the compound's potential as an inhibitor of Fyn Tyrosine Kinase. Comparative analysis with natural compounds and a reference compound highlighted Papaveroline's unique characteristics and promising therapeutic implications for AD treatment. Overall, the findings underscore Papaveroline's potential as a valuable drug candidate for targeting Fyn Tyrosine Kinase in AD therapy, offering new avenues for drug discovery in neurodegenerative diseases. This study contributes to advancing our understanding of molecular interactions in AD pathogenesis and paves the way for further research and development in this critical area.

Discovery of Potent, Orally Bioavailable Sphingosine-1-Phosphate Transporter (Spns2) Inhibitors

Targeting the S1P pathway has resulted in the development of S1P1 receptor modulators for the treatment of multiple sclerosis and ulcerative colitis. We hypothesize that targeting an upstream node of the S1P pathway may provide an improved adverse event profile. In this report, we performed a structure-activity relationship study focusing on the benzoxazole scaffold in SLB1122168, which lead to the discovery of 11i (SLF80821178) as a potent inhibitor of S1P release from HeLa cells (IC50: 51 ± 3 nM). Administration of SLF80821178 to mice induced ∼50% reduction in circulating lymphocyte counts, recapitulating the lymphopenia characteristic of Spns2 null animals. Molecular modeling studies suggest that SLF80821178 binds Spns2 in its occluded inward-facing state and forms hydrogen bonds with Asn112 and Ser211 and π stacking with Phe234. Taken together, SLF80821178 can serve as a scaffold for future inhibitor development and represents a chemical tool to study the therapeutic implication of inhibiting Spns2.

Molecular docking aided machine learning for the identification of potential VEGFR inhibitors against renal cell carcinoma

Renal cell carcinoma is a highly vascular tumor associated with vascular endothelial growth factor (VEGF) expression. The Vascular Endothelial Growth Factor -2 (VEGF-2) and its receptor was identified as a potential anti-cancer target, and it plays a crucial role in physiology as well as pathology. Inhibition of angiogenesis via blocking the signaling pathway is considered an attractive target. In the present study, 150 FDA-approved drugs have been screened using the concept of drug repurposing against VEGFR-2 by employing the molecular docking, molecular dynamics, grouping data with Machine Learning algorithms, and density functional theory (DFT) approaches. The identified compounds such as Pazopanib, Atogepant, Drosperinone, Revefenacin and Zanubrutinib shown the binding energy - 7.0 to - 9.5 kcal/mol against VEGF receptor in the molecular docking studies and have been observed as stable in the molecular dynamic simulations performed for the period of 500 ns. The MM/GBSA analysis shows that the value ranging from - 44.816 to - 82.582 kcal/mol. Harnessing the machine learning approaches revealed that clustering with K = 10 exhibits the relevance through high binding energy and satisfactory logP values, setting them apart from compounds in distinct clusters. Therefore, the identified compounds are found to be potential to inhibit the VEGFR-2 and the present study will be a benchmark to validate the compounds experimentally.

Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN

The rational targeting of RNA with small molecules is hampered by our still limited understanding of RNA structural and dynamic properties. Most in silico tools for binding site identification rely on static structures and therefore cannot face the challenges posed by the dynamic nature of RNA molecules. Here, we present SHAMAN, a computational technique to identify potential small-molecule binding sites in RNA structural ensembles. SHAMAN enables exploring the conformational landscape of RNA with atomistic molecular dynamics simulations and at the same time identifying RNA pockets in an efficient way with the aid of probes and enhanced-sampling techniques. In our benchmark composed of large, structured riboswitches as well as small, flexible viral RNAs, SHAMAN successfully identifies all the experimentally resolved pockets and ranks them among the most favorite probe hotspots. Overall, SHAMAN sets a solid foundation for future drug design efforts targeting RNA with small molecules, effectively addressing the long-standing challenges in the field.

A comprehensive exploration of the druggable conformational space of protein kinases using AI-predicted structures

Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and ɑC-Helix motifs that are related to the catalytic activity of the kinase. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the active or inactive kinase conformation(s) they bind. Modern AI-based structural modeling methods have the potential to expand upon the limited availability of experimentally determined kinase structures in inactive states. Here, we first explored the conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) and ESMFold, two prominent AI-based protein structure prediction methods. Our investigation of AF2's ability to explore the conformational diversity of the kinome at various multiple sequence alignment (MSA) depths showed a bias within the predicted structures of kinases in DFG-in conformations, particularly those controlled by the DFG motif, based on their overabundance in the PDB. We demonstrate that predicting kinase structures using AF2 at lower MSA depths explored these alternative conformations more extensively, including identifying previously unobserved conformations for 398 kinases. Ligand enrichment analyses for 23 kinases showed that, on average, docked models distinguished between active molecules and decoys better than random (average AUC (avgAUC) of 64.58), but select models perform well (e.g., avgAUCs for PTK2 and JAK2 were 79.28 and 80.16, respectively). Further analysis explained the ligand enrichment discrepancy between low- and high-performing kinase models as binding site occlusions that would preclude docking. The overall results of our analyses suggested that, although AF2 explored previously uncharted regions of the kinase conformational space and select models exhibited enrichment scores suitable for rational drug discovery, rigorous refinement of AF2 models is likely still necessary for drug discovery campaigns.

The biosynthetic pathway of the hallucinogen mescaline and its heterologous reconstruction

Mescaline, among the earliest identified natural hallucinogens, holds great potential in psychotherapy treatment. Nonetheless, despite the existence of a postulated biosynthetic pathway for more than half a century, the specific enzymes involved in this process are yet to be identified. In this study, we investigated the cactus Lophophora williamsii (Peyote), the largest known natural producer of the phenethylamine mescaline. We employed a multi-faceted approach, combining de novo whole-genome and transcriptome sequencing with comprehensive chemical profiling, enzymatic assays, molecular modeling, and pathway engineering for pathway elucidation. We identified four groups of enzymes responsible for the six catalytic steps in the mescaline biosynthetic pathway, and an N-methyltransferase enzyme that N-methylates all phenethylamine intermediates, likely modulating mescaline levels in Peyote. Finally, we reconstructed the mescaline biosynthetic pathway in both Nicotiana benthamiana plants and yeast cells, providing novel insights into several challenges hindering complete heterologous mescaline production. Taken together, our study opens up avenues for exploration of sustainable production approaches and responsible utilization of mescaline, safeguarding this valuable natural resource for future generations.

Evaluation of lipophilicity and drug-likeness of donepezil-like compounds using reversed-phase thin-layer chromatography

Fourteen donepezil-like acetylcholinesterase (AChE) inhibitors from our library were analyzed using reversed-phase thin-layer chromatography to assess their lipophilicity and blood-brain barrier permeability. Compounds possessed N-benzylpiperidine and N,N-diarylpiperazine moieties connected via a short carboxamide or amine linker. Retention parameters RM 0, b, and C0 were considered as the measures of lipophilicity. Besides, logD of the investigated compounds was determined chromatographically using standard compounds with known logPow and logD values at pH 11. Experimentally obtained lipophilicity parameters correlated well with in silico generated results, and the effect of the nature of the linker between two pharmacophores and substituents on the arylpiperazine part of the molecule was observed. As a result of drug-likeness analysis, both Lipinski's rule of five and Veber's rule parameters were determined, suggesting that examined compounds could be potential candidates for further drug development. Principal component analysis was performed to obtain an insight into a grouping of compounds based on calculated structural descriptors, experimentally obtained values of lipophilicity, and AChE inhibitory activity.

A Second Drug Binding Site in P2X3

Purinergic P2X3 receptors form trimeric cation-gated channels, which are activated by extracellular ATP. P2X3 plays a crucial role in chronic cough and affects over 10% of the population. Despite considerable efforts to develop drugs targeting P2X3, the highly conserved structure within the P2X receptor family presents obstacles for achieving selectivity. Camlipixant, a potent and selective P2X3 antagonist, is currently in phase III clinical trials. However, the mechanisms underlying receptor desensitization, ion permeation, principles governing antagonism, and the structure of P2X3 when bound to camlipixant remain elusive. In this study, we established a stable cell line expressing homotrimeric P2X3 and utilized a peptide scaffold to purify the complex and determine its structure using cryo-electron microscopy (cryo-EM). P2X3 binds to camlipixant at a previously unidentified drug-binding site and functions as an allosteric inhibitor. Structure-activity studies combined with modeling and simulations have shed light on the mechanisms underlying the selective targeting and inhibition of P2X3 by camlipixant, distinguishing it from other members of the P2X receptor family.

Assessment of Nucleobase Protomeric and Tautomeric States in Nucleic Acid Structures for Interaction Analysis and Structure-Based Ligand Design

With increasing interest in RNA as a therapeutic and a potential target, the role of RNA structures has become more important. Even slight changes in nucleobases, such as modifications or protomeric and tautomeric states, can have a large impact on RNA structure and function, while local environments in turn affect protonation and tautomerization. In this work, the application of empirical tools for pKa and tautomer prediction for RNA modifications was elucidated and compared with ab initio quantum mechanics (QM) methods and expanded toward macromolecular RNA structures, where QM is no longer feasible. In this regard, the Protonate3D functionality within the molecular operating environment (MOE) was expanded for nucleobase protomer and tautomer predictions and applied to reported examples of altered protonation states depending on the local environment. Overall, observations of nonstandard protomers and tautomers were well reproduced, including structural C+G:C(A) and A+GG motifs, several mismatches, and protonation of adenosine or cytidine as the general acid in nucleolytic ribozymes. Special cases, such as cobalt hexamine-soaked complexes or the deprotonation of guanosine as the general base in nucleolytic ribozymes, proved to be challenging. The collected set of examples shall serve as a starting point for the development of further RNA protonation prediction tools, while the presented Protonate3D implementation already delivers reasonable protonation predictions for RNA and DNA macromolecules. For cases where higher accuracy is needed, like following catalytic pathways of ribozymes, incorporation of QM-based methods can build upon the Protonate3D-generated starting structures. Likewise, this protonation prediction can be used for structure-based RNA-ligand design approaches.

In-Silico and in vitro Studies Revealed that Rosmarinic Acid Inhibited Methanogenesis via Regulating Composition and Function of Rumen Microbiota

Inhibition of methyl-coenzyme M reductase can suppress the activity of ruminal methanogens, thereby reducing enteric methane emissions of ruminants. However, developing specific and environmentally friendly inhibitors is a challenging endeavor. To identify a natural and effective methane inhibitor that specifically targets methyl-coenzyme M reductase, molecular docking technology was employed to screen a library of phytogenic compounds. A total of 52 candidate compounds were obtained through molecular docking technique. Rosmarinic acid (RA) was one of the compounds that could traverse a narrow channel and bind to the active sites of methyl-coenzyme M reductase, with a calculated binding free energy of -9.355 kcal/mol. Furthermore, the effects of rosmarinic acid supplementation on methane production, rumen fermentation, and the microorganism's community in dairy cows were investigated through in vitro rumen fermentation simulations according to a random design. Supplementation of RA resulted in a 15% decrease in methane production compared with the control. In addition, RA increased the molar proportion of acetate and propionate, whereas the sum of acetate and butyrate divided by propionate was decreased. At the bacterial level, the relative abundance of Rikenellaceae RC9 gut group, Christensenellaceae R7 group, Candidatus Saccharimonas, Desulfovibrio, and Lachnospiraceae FE2018 group decreased with RA supplementation. Conversely, the addition of RA significantly increased the relative abundance of DNF00809 (a genus from Eggerthellaceae), Denitrobacterium, an unclassified genus from Eggerthellaceae, an unclassified genus from Bacteroidales, and an unclassified genus from Atopobiaceae. At the archaeal level, the relative abundance of Methanobrevibacter decreased, while that of Methanosphaera increased with the RA supplementation. These findings suggested that RA has the potential to be used as a novel natural additive for inhibiting ruminal methane production.

Development of phenyl-urea-based small molecules that target penicillin-binding protein 4

Staphylococcus aureus has the ability to invade cortical bone osteocyte lacuno-canalicular networks (OLCNs) and cause osteomyelitis. It was recently established that the cell wall transpeptidase, penicillin-binding protein 4 (PBP4), is crucial for this function, with pbp4 deletion strains unable to invade OLCNs and cause bone pathogenesis in a murine model of S. aureus osteomyelitis. Moreover, PBP4 has recently been found to modulate S. aureus resistance to β-lactam antibiotics. As such, small molecule inhibitors of S. aureus PBP4 may represent dual functional antimicrobial agents that limit osteomyelitis and/or reverse antibiotic resistance. A high throughput screen recently revealed that the phenyl-urea 1 targets PBP4. Herein, we describe a structure-activity relationship (SAR) study on 1. Leveraging in silico docking and modeling, a set of analogs was synthesized and assessed for PBP4 inhibitory activities. Results revealed a preliminary SAR and identified lead compounds with enhanced binding to PBP4, more potent antibiotic resistance reversal, and diminished PBP4 cell wall transpeptidase activity in comparison to 1.

CNSMolGen: A Bidirectional Recurrent Neural Network-Based Generative Model for De Novo Central Nervous System Drug Design

Central nervous system (CNS) drugs have had a significant impact on treating a wide range of neurodegenerative and psychiatric disorders. In recent years, deep learning-based generative models have shown great potential for accelerating drug discovery and improving efficacy. However, specific applications of these techniques in CNS drug discovery have not been widely reported. In this study, we developed the CNSMolGen model, which uses a framework of bidirectional recurrent neural networks (Bi-RNNs) for de novo molecular design of CNS drugs. Results showed that the pretrained model was able to generate more than 90% of completely new molecular structures, which possessed the properties of CNS drug molecules and were synthesizable. In addition, transfer learning was performed on small data sets with specific biological activities to evaluate the potential application of the model for CNS drug optimization. Here, we used drugs against the classical CNS disease target serotonin transporter (SERT) as a fine-tuned data set and generated a focused database against the target protein. The potential biological activities of the generated molecules were verified by using the physics-based induced-fit docking study. The success of this model demonstrates its potential in CNS drug design and optimization, which provides a new impetus for future CNS drug development.

Reversible male contraception by targeted inhibition of serine/threonine kinase 33

Men or mice with homozygous serine/threonine kinase 33 (STK33) mutations are sterile owing to defective sperm morphology and motility. To chemically evaluate STK33 for male contraception with STK33-specific inhibitors, we screened our multibillion-compound collection of DNA-encoded chemical libraries, uncovered potent STK33-specific inhibitors, determined the STK33 kinase domain structure bound with a truncated hit CDD-2211, and generated an optimized hit CDD-2807 that demonstrates nanomolar cellular potency (half-maximal inhibitory concentration = 9.2 nanomolar) and favorable metabolic stability. In mice, CDD-2807 exhibited no toxicity, efficiently crossed the blood-testis barrier, did not accumulate in brain, and induced a reversible contraceptive effect that phenocopied genetic STK33 perturbations without altering testis size. Thus, STK33 is a chemically validated, nonhormonal contraceptive target, and CDD-2807 is an effective tool compound.

Structural basis of Δ9-THC analog activity at the Cannabinoid 1 receptor

Δ9-tetrahydrocannabinol (THC) is the principal psychoactive compound derived from the cannabis plant Cannabis sativa and approved for emetic conditions, appetite stimulation and sleep apnea relief. THC's psychoactive actions are mediated primarily by the cannabinoid receptor CB1. Here, we determine the cryo-EM structure of HU210, a THC analog and widely used tool compound, bound to CB1 and its primary transducer, Gi1. We leverage this structure for docking and 1,000 ns molecular dynamics simulations of THC and 10 structural analogs delineating their spatiotemporal interactions at the molecular level. Furthermore, we pharmacologically profile their recruitment of Gi and β-arrestins and reversibility of binding from an active complex. By combining detailed CB1 structural information with molecular models and signaling data we uncover the differential spatiotemporal interactions these ligands make to receptors governing potency, efficacy, bias and kinetics. This may help explain the actions of abused substances, advance fundamental receptor activation studies and design better medicines.