Molecules

Head and neck squamous cell carcinoma (HNSCC) is a therapeutically challenging cancer what underscoring the need for new chemical agents that selectively induce programmed cell death. Fisetin, a naturally occurring flavonoid, exhibits promising anticancer activity but displays limited proapoptotic efficacy and selectivity. Here, we examined whether alkoxylated modification of fisetin enhances its ability to induce apoptosis in HNSCC cells. Fisetin derivatives bearing four-carbon substituents were synthesized and evaluated in multiple HNSCC cell lines. Two derivatives, MKT218 and MKT257, markedly reduced HNSCC cell viability at low micromolar concentrations with low toxicity towards normal human fibroblasts. Notably, the observed cytotoxicity was not associated with activation of a canonical DNA damage response, as neither {gamma}H2AX accumulation nor p53 activation was detected. Furthermore, PARP1 cleavage and live-cell imaging combined with annexin V/EthD-III staining revealed a significantly higher proportion of apoptotic cells. The effect was stronger following treatment with MKT218 and MKT257 compared with fisetin. Time-lapse microscopy further demonstrated that fisetin derivatives, particularly MKT218, promote mitosis-associated apoptosis, in contrast to the predominantly cytostatic effect of fisetin. Moreover, in silico docking suggested that MKT218 exerts its pro-apoptotic activity through a multi-target interaction profile involving key regulators of cell survival and apoptosis rather than a single dominant target. To sum up, our findings suggest that alkoxylated fisetin derivatives may be constituted as new non-genotoxic inducers of apoptosis in HNSCC cells.

BackgroundQN-302 is a tetra-substituted naphthalene diimide (NDI) compound designed to interact with G-quadruplex (G4) DNA. QN-302 is currently being evaluated in a phase 1 clinical trial on patients with advanced pancreatic ductal adenocarcinoma (PDAC) and other solid tumors. However, the mechanistic origin(s) of its anticancer activity remains to be fully understood. ResultsWe report herein the ability of QN-302 to damage DNA at G4 sites in cancer cells. To this end, we implemented a series of in vitro assays (FQA and FRET-melting) and cell-based techniques (in situ click imaging and immunodetection) that concurred in demonstrating both the DNA damaging properties of QN-302 and its ability to engage G4s in human cancer cells. Then, we investigate its anticancer effects in PDAC (MIA PaCa-2 cells) and show that it can be efficiently potentiated upon combination with Olaparib, an inhibitor of DNA repair, in an approach referred to as chemically induced synthetic lethality. ConclusionThis study not only confirms the excellent anticancer properties of QN-302 in human cancer cells but also provides insights into its mechanism of action. The optimization of this therapeutic activity by combination with Olaparib opens a promising new avenue for improving its clinical efficacy.

The importance of human aldehyde oxidase (hAOX1) has increased over the last decades due to its involvement in drug metabolism. Inhibition studies concerning hAOX1 are extensive and a common reducing agent, dithiothreitol (DTT), was recently found to inactivate the enzyme. However, in previous crystallographic studies of hAOX1, DTT was found to be essential for crystallization. To surpass this concern another reducing agent used in crystallization trials. Using tris(2-carboxyethyl)phosphine (TCEP), a sulphur-free reducing agent, it was possible to obtain well-ordered crystals from hAOX1 wild type and variant, hAOX1_6A, which diffracted beyond 2.3 [A]. Instead of the typical star-shaped crystals of hAOX1, at pH 4.7, plates are obtained in the orthorhombic space group (P22121) with two molecules in the asymmetric unit. Activity assays with the enzyme incubated with both reducing agents show that contrary to DTT, TCEP does not lead to irreversible inactivation of the enzyme. The replacement of DTT with TCEP in crystallization of hAOX1 provides a strategy to circumvent enzyme inactivation during crystallographic studies, allowing future applications of new assays, such as time-resolved crystallography.

IntroductionDrug repurposing offers a cost-effective and time-efficient strategy for cancer therapy by leveraging existing drugs with established safety profiles, thus functioning as an alternative therapeutic strategy in demanding diseases such as cancer. Antidiabetic agents, in particular, have demonstrated encouraging anticancer potential. Among them, the non-sulfonylurea insulin secretagogue repaglinide (RPG) has shown emerging anticancer potential, yet its effects on breast and lung cancers remain largely unexplored. Thus, this study investigates the anticancer activity of repaglinide in human breast (MCF-7) and lung (A549) cancer cell lines, focusing on its cytotoxic, pro-apoptotic, anti-proliferative, and anti-migratory effects and the underlying possible molecular mechanisms. Methodology and ResultsMTT cytotoxic assay revealed that RPG reduced cell viability in a dose-/time-dependent manner, with an IC (48h) of 100.8 {+/-} 3.98 {micro}M for MCF-7 and 104 {+/-} 3 {micro}M for A549. Further, the apoptotic effect of RPG on both cell lines was evidenced by double staining assays, comet assay, and western blotting analysis, suggesting that RPG explicitly caused DNA damage and activated intrinsic and extrinsic apoptosis pathways. Additionally, RPG suppressed clonogenicity and enforced G1 arrest in MCF7 and A549 cells by modulating cell cycle regulations as well as cell proliferation pathways. Moreover, RPG markedly suppressed cell motility, as demonstrated by scratch and Transwell migration/invasion assays, which is correlated with reduced MMP-2 and MMP-9 expression, confirmed by gelatin zymography and western blotting. ConclusionConclusively, Repaglinide exerts potent anticancer effects in breast and lung cancer cells by modulating key oncogenic signaling pathways, and thus can be considered a promising candidate for repurposing in cancer therapy.

Colorectal cancer is the third most common cancer across the world. Acquired resistance to therapeutics is one of the major challenges in cancer cure. With the development of resistance to organometallic drugs there was switch for the usage of inhibitors of kinases which play a crucial role in cellular activities. Regorafenib is a multi-kinase inhibitor used as an oral anti-cancer drug for treating advanced colorectal cancer (CRC). With increasing reports of acquired resistance to regorafenib in long-term use it is inevitable to understand the mechanisms underlying in the development of resistance. To understand the molecular mechanism of acquired drug resistance towards regorafenib we have developed a regorafenib resistant HCT116 cell line (Reg-R-HCT116) and an integrated quantitative proteomic and phospho-proteomics approach is used to elucidate the molecular signaling mechanisms that help in drug tolerance. Proteome and Phosphoproteome analysis revealed an extensive remodelling of signaling pathways associated with metabolism, protein synthesis and stress adaptations. This also revealed a large set of phosphorylated proteins as well as proteins that might be associated with aberrant activation or differential alteration of PI3K-AKT-mTOR, EIF2, HIF-1, Apoptosis inhibition, Glucose metabolism, amino acid metabolism and DNA-repair-associated signaling. Differential phosphorylation of downstream molecules of the mentioned pathways NDRG1, ACINUS and RICTOR and enhanced cell survival further confirmed their role in drug tolerance. Targeted inhibition of both the mTOR complexes using Torin1 resensitized the cells to regorafenib. Combination treatment of regorafenib with torin1 showed a synergistic cytotoxicity and attenuated the expression of key survival proteins. These findings provide mechanistic insight into acquired resistance to regorafenib in colorectal cancer and identified mTOR/eIF2 signaling as one of the critical drivers of resistance phenotype. The results suggest combinatorial targeting of these pathways could be an effective strategy to overcome regorafenib resistance and improve clinical outcomes in CRC.

Resveratrol, a naturally occurring polyphenol, exhibits anticancer, anti-inflammatory, and antioxidant properties. However, its molecular mechanisms in pancreatic cancer remain incompletely understood, necessitating integrative approaches such as network pharmacology and molecular docking. Potential resveratrol targets were identified using Swiss Target Prediction, while pancreatic cancer-related genes were retrieved from GeneCards and NCBI Gene databases. Overlapping targets were obtained through Venn analysis, followed by protein- protein interaction network construction, hub gene selection, and enrichment analysis. Molecular docking validated compound-target interactions. A total of 100 predicted resveratrol targets and 1,447 pancreatic cancer- associated genes were screened, yielding 39 overlapping genes. Network analysis identified hub genes including EGFR, SRC, MTOR, PIK3CA, PIK3CB, BCL2, and PTGS2. Gene Ontology enrichment indicated roles in cell proliferation, apoptosis regulation, inflammatory response and metabolic regulation while KEGG pathway analysis highlighted the PI3K-Akt, ErbB, and EGFR inhibitor resistance signaling as being closely associated with pancreatic cancer pathway. Docking analysis revealed strong binding of resveratrol with KRAS (-8.2 kcal/mol), EGFR (-7.9 kcal/mol), and MTOR (-7.7 kcal/mol), stabilized by hydrogen bonding. The interaction with KRAS, although not among the predicted targets. Expression profiling validated upregulation of hub genes in tumor samples. Resveratrol exerts multi-targeted effects in pancreatic cancer by modulating oncogenic pathways, particularly KRAS and PI3K/Akt/mTOR signaling. Its favourable safety profile and robust hub gene interactions highlight its potential as a supplementary therapeutic drug, necessitating further preclinical and clinical confirmation.

Asparagus racemosus commonly known as Shatamull, is a medicinal plant with pharmacological applications documented in both Indian and British Pharmacopoeias and various traditional medicinal practices. Previous studies have reported that A. racemosus reduces hyperglycemia by enhancing insulin secretion. The aim of the current study was to assess the antihyperglycemic actions and explore the underlying mechanisms of action of A. racemosus utilizing in vitro carbohydrate digestion, glucose diffusion, glucose uptake, 2,2-Diphenyl-1-picrylhydrazyl (DPPH) and preliminary phytochemical screening. The inhibition of carbohydrate digestion was assessed using -amylase and -glucosidase enzyme assays. The effect on glucose diffusion was evaluated using cellulose ester dialysis tube. Subsequently, glucose uptake was measured in a yeast cell model at different glucose concentrations, and the antioxidant potential was evaluated by measuring DPPH radical scavenging activity. A. racemosus notably reduced (p<0.05, 0.001) glucose release during in vitro starch digestion by 37.69%, whereas glucose absorption decreased significantly by 33.60% (p<0.01-0.001). Additionally, the most significant enhancement (p<0.05, 0.001) in glucose uptake by 67.53%, was observed at 5 mM glucose concentration. Furthermore, it showed significant antioxidant activity by scavenging DPPH (p<0.01-0.001) radicals by 55.06%. Preliminary phytoconstituent screening indicated the existence of flavonoids, tannins, steroids, glycosides and saponins. In conclusion, A. racemosus shows an inhibitory effect on carbohydrate digestion and absorption, enhances glucose uptake and demonstrates significant DPPH radical scavenging activity, potentially due to the presence of naturally occurring phytochemicals. Thus, A. racemosus may contribute as a promising antidiabetic drug for the treatment of diabetes mellitus. More investigations are needed to determine the active compounds in A. racemosus that contribute to its antidiabetic effects.

This study presents a phytochemical and pharmacological investigation of Aruncus dioicus, a medicinal plant collected from the northeastern coastal region of Vietnam. In light of the growing global prevalence of type 2 diabetes mellitus (T2DM), the search for natural compounds capable of modulating key enzymes involved in glucose metabolism, particularly Protein Tyrosine Phosphatase 1B (PTP1B) and -glucosidase, remains an important research objective. The experimental methods employed included: botanical identification, extraction, chromatographic separation, and biological activity evaluation. As a result, eleven pure compounds were isolated. Structural determination via 1H- and 13C-NMR spectroscopy revealed these constituents as phenylpropanoids, phenolic acids, nucleosides, and ester derivatives, thereby establishing a distinctive chemical profile for the Vietnamese population of A. dioicus. In vitro enzyme inhibition tests demonstrated significant biological activity. p-coumaric acid (Compound 3) and cinnamic acid (Compound 4) exhibited effects on PTP1B, with IC{square}{square} values of 0.25 {micro}M and 1.16 {micro}M, respectively, higher than the activity of the reference compound ursolic acid (IC{square}{square} = 3.5 {micro}M). Furthermore, ethylparaben (Compound 7) and cinnamic acid exhibited -glucosidase inhibition, with potencies approximately five- to six-fold greater than that of acarbose. These findings suggest that A. dioicus is a potentially valuable source of antidiabetic agents and emphasize the significance of phenylpropanoid derivatives in enzyme inhibition associated with glucose metabolism, thereby providing a scientific foundation for subsequent pharmacological investigations.

Chikungunya virus (CHIKV) infection is one of the major public health concerns in several countries around the world. CHIKV non-structural protein 2 (nsP2) is a promising drug design target due to the enzymes multifunctional properties that facilities viral replication and propagation. To date, there is an evident lack of preventative and therapeutic developments that can be used against CHIKV. Drug repurposing is a time saving and cost-effective method used for the development of new drugs. In this study, drug repurposing was implemented with the use of HIV/HCV protease inhibitors to inhibit the active site of nsP2. Molecular dynamics simulations and analysis revealed the stability of two drugs, Indinavir and Paritaprevir. Indinavir forms a hydrogen bond with a major residue, which closes the flexible loop, situated in close proximity to the active site. This conformational shift in the orientation of the enzyme prevents accessibility to the active site thus disrupting the nsP2 protein from functioning effectively in viral replication. In conclusion, the findings of this study identified Indinavir was identified as a promising CHIKV nsP2 inhibitor. This study will provide the basis to further facilitate the drug repurposing strategy as an alternative approach for drug design of CHIKV inhibitors as well as other viral families.

Antimicrobial resistance (AMR) in plant pathogenic bacteria poses a serious threat to global agriculture, necessitating the development of novel antibacterial agents targeting virulence mechanisms. This study presents an integrated bioinformatics-driven framework for the rational design and computational validation of Solres, a newly designed small molecule targeting key virulence proteins in phytopathogenic bacteria. Approximately 10,000 active compounds from PubChem BioAssay (AID: 588726) were analyzed using structural clustering and scaffold mining to identify conserved molecular motifs associated with antibacterial activity. Guided by high-frequency substructures, Solres was designed de novo and screened for structural novelty against PubChem, ChEMBL, and WIPO databases. Drug-likeness evaluation using Lipinskis Rule of Five confirmed favorable physicochemical properties. Molecular docking was performed against essential virulence regulators, including PhcA, PhcR, HrpB, PehA, and Egl from Ralstonia solanacearum and Xanthomonas spp., with active sites predicted using CaspFold. Docking analyses revealed strong binding affinities and stable interactions with key catalytic and regulatory residues. Complex stability and conformational integrity were further validated through molecular dynamics simulations. Quantum chemical descriptors, including HOMO-LUMO energy gap and dipole moment, supported the electronic suitability and reactivity profile of Solres. Collectively, this study demonstrates the effective integration of cheminformatics, structural bioinformatics, molecular simulations, and quantum chemical analyses for plant-focused antibacterial discovery. The compound Solres represents a promising lead candidate for mitigating bacterial wilt disease and provides a computational framework for future experimental validation and sustainable crop protection strategies against AMR-driven phytopathogens.

Marburg virus (MARV) is a highly pathogenic filovirus that causes hemorrhagic fever with a high mortality rate, with very limited treatment options. The urgent need for targeted antiviral agents emphasizes the importance of structure-based drug discovery approaches. The present study aimed to evaluate the antiviral potential of Withaferin A (PubChem CID-265237) against three key proteins of MARV: viral protein 35 (VP35), and nucleoproteins (NP). Three-dimensional structures of these proteins were retrieved from RCSB-Protein Data Bank and docked with Withaferin A using AutoDock Vina. The ligand demonstrated favourable binding affinities towards all three viral targets, indicating strong interaction potential at functionally relevant sites. Drug-likeness and pharmacokinetic properties predicted using SwissADME and pkCSM indicated acceptable ADMET profiles that comply with key drug-like criteria. To validate the stability of the docking, molecular dynamics simulations (GROMACS, 100 nanoseconds) were conducted. The protein-ligand complexes exhibited stable root mean square deviation (RMSD), root mean square fluctuation (RMSF), and consistent hydrogen bonding patterns throughout the simulation. The MM-GBSA binding free energy analysis further supported favorable binding energetics, predominantly driven by van der Waals and electrostatic interactions. Altogether, these findings demonstrate that Withaferin A exhibits promising multi-target inhibitory potential against key MARV proteins. This study provides molecular insights into ligand-protein interactions and supports further experimental validation of Withaferin A as a potential therapeutic candidate against Marburg virus.

Postprandial hyperglycemia is a major concern in type 2 diabetes, and inhibition of intestinal -glucosidases [maltase-glucoamylase (MGAM)] is an established method for controlling post-meal glucose excursions. In this study, we conducted an in-silico screening of phytochemicals from different well-known medicinal plants (Withania somnifera, Rauwolfia serpentina, Curcuma longa, and Camellia sinensis) against MGAM, using the clinically approved inhibitor miglitol as reference for docking protocol validation. Molecular docking revealed that miglitol binds to MGAM with a binding energy of -6.86kcal/mol and an RMSD of 1.04 (with co-crystal structure; PBD ID:3L4W); however, several phytochemicals exhibited binding affinities equal to or stronger than miglitol. Among these, Withanolide B (-9.25kcal/mol) and Withanone (-7.57kcal/mol) from Withania somnifera showed the highest predicted affinities, indicating robust engagement of the MGAM catalytic pocket. Rauwolfia serpentina alkaloids such as yohimbine (-8.50kcal/mol) and raubasine (-8.46kcal/mol) also displayed strong binding energies, whereas curcuminoids (curcumin -6.36kcal/mol; deoxycurcumin -6.35kcal/mol) and tea catechins (e.g., epicatechin gallate -6.85 kcal/mol) demonstrated moderate affinity. Interaction analysis showed that top-ranking compounds formed extensive hydrogen-bonding and hydrophobic interactions with key catalytic residues of MGAM, suggesting stable occupancy of the active site. In-silico ADME profiling predicted favorable gastrointestinal absorption for lead phytochemicals, supporting their potential for oral intestinal action. Collectively, these results identify plant-derived ligands with binding energies comparable to or exceeding that of miglitol, highlighting Withania somnifera withanolides as priority candidates for experimental validation in enzyme inhibition assays and glucose tolerance models, and providing a focused set of natural MGAM inhibitors for further translational investigation in postprandial glucose control.

Ribociclib, a selective cyclin-dependent kinase (CDK) 4/6 inhibitor, is approved as a first-line therapy for HR-positive/HER2-negative advanced breast cancer. Emerging evidence suggests that Ribociclib may exert immunomodulatory effects. However, its role in cytokine regulation remains largely unexplored. This study presents a comprehensive in silico investigation of Ribociclibs interactions with eight key pro-inflammatory cytokines--IL-6, TNF-, IL-17A, IL-17F, IL-17A/F, IL-1{beta}, MCP-1, and IFN-{gamma}. Computational assessments included molecular docking, molecular dynamics (MD) simulations, MM-GBSA binding free energy calculations, principal component analysis (PCA), and dynamic cross-correlation matrix (DCCM) analyses. Molecular docking and MD simulations indicated strong and stable complex formation with TNF-, IL-6, MCP-1, IL-1{beta}, and IL-17A/F. MM-GBSA results further showed that Ribociclib formed the most stable complexes with IL-17A/F ({Delta}Gbind = -25.94 kcal/mol) and MCP-1 ({Delta}Gbind = -25.88 kcal/mol), comparable to binding with the CDK-6 ({Delta}Gbind = -36.23 kcal/mol) control protein. PCA and DCCM analyses further supported the stabilizing influence of Ribociclib on these cytokine conformations. Moderate interactions were observed with TNF-, IL-6, and IFN-{gamma}. Collectively, these findings suggest that Ribociclib may function as a multi-target inhibitor capable of modulating diverse inflammatory pathways, providing a computational foundation for its repurposing as a cost-effective anti-inflammatory therapeutic candidate.

Recently developed deep learning-based tools can effectively generate structural models of complexes of proteins and non-proteinaceous compounds. While some of their predictive capabilities are truly exciting, others remain to be thoroughly tested. Here, we probe whether the ligand input format (Chemical Component Dictionary, CCD, or Simplified Molecular Input Line Entry System, SMILES) and charge (which depends on protonation) will affect the results of the predictions by four popular algorithms: AlphaFold 3, Boltz-2, Chai-1, and Protenix-v1. We chose methylamine and acetic acid as two of the simplest titratable chemicals that are omnipresent in proteins as amino and carboxy moieties, and are consequently ubiquitous in the Protein Data Bank models that are most commonly used for training. Unexpectedly, we found that for both molecules, in many cases the input format affected the prediction results, and did it much stronger compared to protonation, whereas changes in the formally specified charge of the molecules did not lead to changes in binding expected from experiments. We conclude that (i) ensuring identical results irrespective of input formats and (ii) inclusion of protonation-related steps into training and prediction pipelines are the two available paths for improvement of protein-ligand structure prediction algorithms.

Oral medications can be bioaccumulated or metabolised by gastrointestinal bacteria in a process collectively termed drug depletion. The precise biological mechanisms governing strain-specific depletion remain poorly understood, and systematic experimental classification of drug-strain interactions via in vitro studies is both costly and time-consuming. In this study, artificial intelligence (AI) methodologies combining machine learning (ML) and natural language processing (NLP) were applied to predict strain-specific drug depletion. The dataset comprised 16,802 drug-strain interaction pairs, with drugs represented by physicochemical descriptors and bacterial strains represented by whole-genome sequences. NLP techniques were used to transform genomic data into feature representations suitable for ML model training. The resulting models achieved strong predictive performance, with a balanced accuracy of 0.90 {+/-} 0.02 and Matthews correlation coefficient of 0.54 {+/-} 0.10. Feature importance analysis revealed that both drug properties and genomic features contributed to model predictions. Among the highest-ranking genomic features, BLASTX annotation identified several enzymes with known or plausible roles in drug metabolism. To further explore the mechanistic relevance of these features, two candidate enzymes were selected for molecular docking against drugs experimentally observed to be depleted. Glycosidase was found to possess binding energies of -8.69 and -7.88 kcal/mol for the two cardiac glycoside drugs digitoxin and digoxin, respectively; whereas acetyl-CoA carboxylase biotin carboxylase presented with binding energies for between -7.09 and -7.74 kcal/mol at one of its druggable sites. Collectively, these findings establish a proof-of-concept AI-driven framework that integrates predictive performance with mechanistic interpretability in the study of drug-microbiome interactions. The broader implications and limitations of applying AI in this context are also discussed. These preliminary findings offer a promising strategy for accelerating drug developments through using AI to rapidly highlight potential drug interactions. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/701358v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@16cf66corg.highwire.dtl.DTLVardef@a640acorg.highwire.dtl.DTLVardef@e00ecdorg.highwire.dtl.DTLVardef@1ebbd62_HPS_FORMAT_FIGEXP M_FIG C_FIG

Current in silico drug discovery protocols ubiquitously depend on lead generation using a ligand-based approach in which novel leads are generated by fragment-signature matching or by a structure-based search involving molecular docking and conformational dynamics. None of them incorporates cellular contexts in which these drugs ultimately operate, leaving the task to a later stage of optimization leading to a high failure rate. Incorporating systems-level responses of drugs in an early stage of lead generation can significantly address this concern but has not been sufficiently explored. In this work, we employ a systems-level approach using connectivity map (CMAP) library to generate leads against a challenging system of a TLR pathway. Starting with gene expression data of TLR5 activation by its natural ligand, we generated molecular leads using CMAP and rigorously analyzed their validity using ligand and structure-based approaches, and helping to prioritize top hits. Experimental validation using ELISA-based antibody assay confirmed the activation of TLR5 by each of the top nine prioritized leads with their dose-dependent patterns suggesting that some of them may actually interact with the TLR signaling pathway in a complex manner. Although, demonstrated on TLR5, the proposed framework is intuitively scalable to other lead generation and optimization tasks.

Intrinsically disordered proteins (IDP) or proteins with intrinsically disordered regions (IDR) perform a plethora of functions mostly in a cellular environment. As unfolded proteins, IDPs can adopt molten globule or coil ensembles of conformations. Regarding the latter the question arises whether they are describable as a self-avoiding random coil. Locally, this requires that amino acid residues sample the entire sterically allowed region of the Ramachandran plot with very similar probabilities and independent on the conformational dynamics of their neighbours. However, various lines of experimental and bioinformatic evidence suggest a more restricted, side chain and nearest neighbor dependent conformational space for individual residues. Over the last 25 years short peptides and coil libraries were employed to determine conformational propensities of amino acid residues in unfolded states. The question arises whether conformational ensembles obtained from these two sources are comparable. In this paper, a variety of metrics were used to compare Ramachandran plots of a limited number of GXYG peptides (X,Y: guest residues) with XY dimers in the coil library of Ting et al.(PLOS 6, e1000763, 2010). The results reveal major differences between corresponding plots, which might in part due to the fact that solely the influence of one of the two neighbours of a given residue is probed by the above coil library while averages were taken over the respective opposite neighbours. The presented results suggest that coil libraries alone might not be a sufficient tool for determining the characteristics of statistical coils of IDPS and IDRs alike.

Sanguinarine (SNG) is a natural component belonging to the benzophenanthridine alkaloids. In recent years, due to its remarkable biological activities, it has gained wide interest in the pharmaceutical industry. Various studies have reported its potential as a therapeutic agent in treating chronic human diseases such as cancer. SNG is widely reported to cause programmed cell death in various cancer cell lines. The mechanism by which SNG triggers apoptosis remains poorly elucidated, especially in vivo. Previous studies reported that sanguinarine induces apoptosis by increasing reactive oxygen species (ROS). In this study, we aimed to characterize the effects of SNG using an in vivo Caenorhabditis elegans (C. elegans) model. Treating C. elegans with various SNG concentrations resulted in apoptotic cell death in the proliferative germline. Interestingly, SNG-induced apoptosis depends on the core apoptotic machinery initiated by the DNA-damage-induced activity of the p53/CEP-1 protein. We have also demonstrated that the increase in germ cell apoptosis is caused by elevated levels of reactive oxygen species (ROS) following SNG treatment. Notably, the apoptotic phenotype induced by SNG was resolved upon treatment with the ROS scavenger. Altogether, our study demonstrates that SNG increases ROS, leading to activation of DNA damage-induced apoptosis in the proliferative germline of C. elegans.

Prasugrel is a prodrug, widely used in antiplatelet strategy for secondary prevention after acute coronary syndrome. The metabolism of prasugrel leads to the formation of the Prasugrel Active Metabolite (PAM), an irreversible P2Y12 receptor antagonist. Its mode of binding has not yet been fully established, although it is known that it binds covalently to P2Y12 by forming a disulfide bridge with cysteines and its sulfur moiety. PAM is a molecule with two chiral centers, resulting in four stereoisomers which appear to be stereoselective upon binding. A combination of different molecular modeling methods, such as molecular dynamics, ensemble docking, and Density Functional Theory (DFT), were used to rationalize these differences in antagonism observed in vitro and to elucidate the mode of binding of PAM to P2Y12. PAM is found to bind to the closed P2Y12 conformation in a preferential way. Although the four stereoisomers have comparable affinity, the location of the RS stereoisomer makes the formation of a disulfide bond with cysteines more favorable, particularly with cysteine 175. Compared to the RR stereoisomer, the RS stereoisomer interacts less deeply with the P2Y12 receptor, interacting in particular with the second and third extracellular loops, explaining the competition observed with cangrelor and an intermediate metabolite of prasugrel. Furthermore, DFT calculations have shown that the formation of a disulfide bridge is energetically more favorable with the RS stereoisomer than with the RR stereoisomer. The physical interactions and chemical reaction between the RS stereoisomer and the P2Y12 receptor are key factors in explaining the stereoselective binding of PAM to P2Y12.

A methodology for computationally unstructuring proteins is described and the results of its application to a variety of proteins analyzed and discussed. Some proteins prove more susceptible than others, and fold topology plays a part in this. Alpha helical structure is found to be generally somewhat robust, and, perhaps unsurprisingly, unstructuring often begins at exposed chain termini. Phosphofructokinase-1 and phosphofructokinase-2, which have similar sizes but different fold topologies, are found to differ markedly in their unstructuring behaviour.