The glucagon-like peptide-1 receptor (GLP-1R) is a class B G protein-coupled receptor (GPCR) central to metabolic regulation, and its potential modulation by dietary phytochemicals is increasingly recognized as physiologically relevant. Understanding how such compounds interact with GLP-1R is important for clarifying mechanisms that may contribute to gut-to-brain signaling. In this study, we examined three structurally related dietary ginsenosides, Rg1, Rg2, and Rg3, as potential modulators of GLP-1R using luciferase reporter assays and computational analyses. Despite sharing similar molecular weights, a common dammarane scaffold, and comparable sugar moieties, the three ginsenosides displayed distinct effects on GLP-1R activity: Rg2 and Rg3 potently reduced receptor activation in a dose-dependent manner when co-administered with Exendin-4, whereas Rg1 had minimal effect. Computational screening of the GLP-1R structure for binding sites identified a putative extracellular pocket on the protein that can accommodate these compounds, while molecular docking and binding free energy calculations provided predicted affinities qualitatively reflecting the phytochemicals’ experimental activities. These findings point to a plausible extracellular mechanism through which dietary ginsenosides may influence GLP-1R responsiveness at the intestinal interface. Our results point to the possibility that non-absorbed phytochemicals can differentially modulate gut-expressed receptors, suggesting a novel pathway for dietary signaling relevant to ethnopharmacology and metabolic health.

The journal retracts the article titled “Structure–Activity Relationship of Synthetic Linear KTS-Peptides Containing Meta-Aminobenzoic Acid as Antagonists of α1β1 Integrin with Anti-Angiogenic and Melanoma Anti-Tumor Activities” [...]

Cystic fibrosis (CF) is an autosomal recessive genetic disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) chloride channel. The first nucleotide-binding domain (NBD1) of the CFTR is considered to be a hotspot for CF-causing mutations, and some of these mutations compromise the domain's thermal stability as well as its interactions with other domains. The mechanisms by which such mutations exert their deleterious effects are important in the basic research of this complex disease as well as for the development of mutation-specific therapies. With this in mind, we studied two class-II, severe, CF-causing mutations, L467P and A559T, known to destabilize the domain by 19.3 and 10.7 °C, respectively, and to lead to a misfolded, nonfunctioning CFTR, by conducting microsecond-long molecular dynamics (MD) simulations at an elevated temperature of 410 K on L467P-NBD1 and A559T-NBD1 constructs. For comparison, similar simulations were also performed on the wild-type (WT) construct and on the 6SS-NBD1 and 2PT/M470V-NBD1 constructs, both bearing sets of stabilizing mutations that stabilize the domain by 17.5 and 8.2 °C, respectively. The resulting trajectories were analyzed using multiple metrics, leading to a good correlation between the experimental ΔTm values and the results of the simulations, as well as multiple experimental observations and results of previous modeling efforts. Specifically, our analyses point to specific regions within NBD1 that are substantially affected by the L467P and A559T mutations and, therefore, may play some role in their pathogenesis. Many of these regions are also known to be important for the proper folding and function of the full-length CFTR. Using time-dependent assignment of DSSP elements, we also found that the two mutants follow different disintegration pathways, that of L467P-NBD1 starting in region 464-471 which resides within the F1-like ATP-binding core subdomain and continues in regions 550-562 and 514-523 within the ABCα subdomain whereas that of A559T-NBD1 simultaneously starting at the 550-562 and 514-523 regions. We propose that the analyses presented in this work may pave the way toward the development of L467P and A559T-specific CF therapies and by extension to other mutation-specific therapies for CF and for other diseases involving mutations in NBDs of other proteins.

Ignition cases involving arsons are typically handled by forensic experts who examine spectra of samples collected from scenes of fire to test for the existence or absence of ignitable liquids. This is tedious work, since many cases do not involve such liquids. To facilitate this process, we have developed in this work a Machine Learning (ML)-based workflow for samples' classification based on their gas chromatography (GC) chromatograms (i.e., spectra). To this end, annotated spectra of 181 samples containing three groups of liquids (petroleum distillates, gasoline, and an assortment of other substances) collected from fire scenes as well as two reference databases were obtained from the Israeli Department of Identification and Forensic Sciences (DIFS). These spectra were used for the derivation of ML-based classification models using three algorithms, namely, kNN, representative spectrum, and random forest (RF) giving rise to reliable predictions. To increase the size of the dataset to a level that would enable the usage of more advanced ML algorithms, we have used the experimental spectra to develop a new spectra synthesis algorithm and utilized it to generate a large dataset of synthetic spectra. This dataset was used for the derivation of new kNN, RF, and representative spectrum models as well as deep learning (DL) models producing F1-scores over an independent test set composed entirely of "real" spectra ranging from 0.74-0.95, 0.86-0.95, 0.30-0.75, and 0.85-0.96 for kNN, RF, representative spectrum, and DL, respectively. Following the completion of the work, a second set of real spectra was provided to us by DIFS, and modeling it with the second set of models yielded F1-scores ranging from 0.92-0.96, 0.96-1.00, 0.71-0.82, and 0.95-0.98 for kNN, RF, representative spectrum, and DL, respectively. These results therefore suggest that for this dataset, performances depend more on the size of the dataset used for model training than on the ML algorithm. We propose that the workflow and spectra synthesis algorithm developed in this work could be readily applied to other forensic domains where samples are characterized by spectra, either solely or in combination with other parameters.

The mitochondrial E3 ubiquitin ligase membrane-associated RING-CH-type finger 5 (MARCH5) and the GTPase Mitofusin 2 (Mfn2) both play crucial roles in regulating mitochondrial dynamics, which are essential for cellular homeostasis. Dysregulation of the MARCH5/Mfn2 signaling has been implicated in mitochondrial dysfunction, a key factor in cardiovascular diseases (CVDs). To investigate the therapeutic potential of targeting this interaction, we developed a novel peptide, CVP-220, designed to specifically disrupt the MARCH5/Mfn2 protein interaction. Using a hypoxia-reoxygenation (H/R) injury model in rat cardiomyocyte cell lines, CVP-220 demonstrated significant cardioprotective effects. Treatment with CVP-220 enhanced cell viability by 30 % compared to untreated controls and reduced reactive oxygen species (ROS) production by 45 %, suggesting improved mitochondrial function. Notably, CVP-220 selectively modulated MARCH5-mediated ubiquitination of Mfn2 without affecting other MARCH5 interactions, thereby preserving mitochondrial fusion and preventing fragmentation under stress conditions. A plausible binding mode of CVP-220 on Mfn2 was suggested through a combination of molecular docking and molecular dynamics simulations and was experimentally validated by mutational analysis. These findings highlight CVP-220 as a promising tool for modulating mitochondrial dynamics and mitigating mitochondrial damage in cardiac cells, with potential implications for therapeutic strategies targeting mitochondrial dysfunction in CVDs. Further investigation into the role of MARCH5/Mfn2 signaling in cardiac pathology could pave the way for novel peptide-based treatments.

W1282X CFTR is the most prevalent CF-causing variant among cystic fibrosis patients of Ashkenazi descent and a mutational defect for which targeted drug therapy is not available. We show that administration of the potentiator VX-770 can augment levels of truncated W1282X CFTR in the plasma membrane, demonstrating that an established gating activator (i.e., "potentiator") also rescues W1282X protein expression and surface localization (i.e., "corrector" function). Additionally, acute in vitro treatments with approved modulators VX-809 or VX-661 result in immediate potentiation of W1282X-dependent ion transport, showing that F508del CFTR correctors also augment W1282X CFTR channel activity. To investigate the mechanism, we tested a CFTR variant (G551D) exhibiting higher levels of CFTR-dependent potentiation following corrector treatment. Clinically approved CFTR correctors VX-445, VX-121, and VX-809 elicited potentiation of G551D CFTR. Forskolin dose dependence and molecular dynamic simulations indicated that corrector molecules promote acute CFTR gating by modifying protein conformation and enhancing heterodimerization of nucleotide binding domains, leading to potentiator-like effects. Although W1282X is poorly responsive to "readthrough" agents such as G418, the drug unexpectedly increases W1282X mRNA, augments surface-localized (truncated) protein, and promotes CFTR function, even in the absence of detectable stop codon suppression. Moreover, unlike other CFTR mutations such as F508del, proteasome blockade using ALLN partially rescues W1282X at the plasma membrane. These results highlight ways in which detailed mechanistic analysis and modulator profiling are needed to characterize CFTR mutations such as W1282X and that modulator function in rare variants can be quite distinct from classical findings based strictly upon F508del CFTR.

Naringenin, a flavanone from citrus, was studied for its ability to reduce virulence in Pectobacterium, a phytopathogen causing soft rot disease in crop plants. Naringenin downregulated quorum sensing (QS) and suppressed critical virulence determinants in Pectobacterium brasiliense Pb1692, including plant cell wall-degrading enzymes, bacterial motility, and biofilm formation, consequently reducing disease symptoms in two host plants. Molecular docking simulations revealed a plausible binding mode for naringenin within the QS protein ExpI, which were maintained during microsecond-long Molecular Dynamics simulations. These simulations provided atomic-scale insight into specific interactions and estimated binding free energies, supporting naringenin's QS inhibition mode of action. In contrast, S-adenosyl methionine, the natural ligand of ExpI, was unable to maintain a stable binding mode in the ExpI site during simulations. Beyond QS disruption, naringenin induced reactive oxygen species accumulation and compromised DNA repair, indicating a multimodal mechanism of action. Despite these promising findings, naringenin's limited aqueous solubility challenges practical applications.

To develop peptide drugs targeting integrin receptors, synthetic peptide ligands endowed with well-defined selective binding motifs are necessary. The snake venom KTS-containing disintegrins, which selectively block collagen α1β1 integrin, were used as lead compounds for the synthesis and structure–activity relationship of a series of linear peptides containing the KTS-pharmacophore and alternating natural amino acids and 3-aminobenzoic acid (MABA). To ensure a better stiffness and metabolic stability, one, two and three MABA residues, were introduced around the KTS pharmacophore motif. Molecular dynamics simulations determined that the solution conformation of MABA peptide 4 is more compact, underwent larger conformational changes until convergence, and spent most of the time in a single cluster. The peptides’ binding affinity has been characterized by an enzyme linked immunosorbent assay in which the most potent peptide 4 inhibited with IC50 of 324 ± 8 µM and 550 ± 45 µM the binding of GST-α1-A domain to collagen IV fragment CB3, and the cell adhesion to collagen IV using α1-overexpressor cells, respectively. Docking studies and MM-GBSA calculations confirmed that peptide 4 binds a smaller region of the integrin near the collagen-binding site and penetrated deeper into the binding site near Trp1. Peptide 4 inhibited tube formation by endothelial cell migration in the Matrigel angiogenesis in vitro assay. Peptide 4 was acutely tolerated by mice, showed stability in human serum, decreased tumor volume and angiogenesis, and significantly increased the survival of mice injected with B16 melanoma cells. These findings propose that MABA-peptide 4 can further serve as an α1β1-integrin antagonist lead compound for further drug optimization in angiogenesis and cancer therapy.

Understanding protein-protein interactions (PPIs) at the molecular level may lead to innovations in medicine and biochemistry. The assumption that there are certain “hot spots” on protein surfaces that mediate their interactions with other proteins has led to a search for specific sequences involved in protein-protein contacts. In this work, we analyze sequential amino acid motifs, both at the single motif and at the motif-motif level, across a large and diverse dataset of biologically relevant protein-protein interfaces retrieved from the PDB, comparing their presence at interfaces and surfaces in a statistically rigorous manner. At the single motif level, our results indicate statistically significant over-presence of hydrophobic and in particular aromatic residues and under-presence of charged residues at protein-protein interfaces. Certain PPI-mediating motifs reported in the literature (e.g., the Tyrosine-based Motif YxxΦ and the PDZ-Binding Motif X-S/T-X-V/I) were confirmed to have a significant presence at interfaces. In addition, multiple PPI-mediating motifs were reported in the ELM database and from those present in our dataset, half were confirmed to have a statistically significant presence at interfaces whereas others were not. At the single residue, motif-motif level, Cysteine-Cysteine contacts were found to be the most abundant ones followed by interactions involving aromatic/hydrophobic residues. Top ranking, longer motif-motif pairs show predominance of Leucine and aromatic residues. Finally, preliminary energy calculations (using the MM/GBSA procedure) indicate a partial correlation between the probability of motifs-pair to be a part of a protein-protein interface and the strength of the interactions between the motifs. In conclusion, this study points to specific characteristics of motifs that have a higher probability to mediate protein-protein interactions. Prominent motifs identified in this study may be used in the future as possible components in protein engineering.

Introduction: The eons-long co-evolvement of plants and bacteria led to a plethora of interactions between the two kingdoms, in which bacterial pathogenicity is counteracted by plant- derived antimicrobial defense molecules. In return, efflux pumps (EP) form part of the resistance mechanism employed by bacteria to permit their survival in this hostile chemical environment. In this work we study the effect of combinations of efflux pump inhibitors (EPIs) and plant-derived phytochemicals on bacterial activity using Pectobacteriun brasiliense 1692 (Pb1692) as a model system. Methods: We measured the minimal inhibitory concentration (MIC) of two phytochemicals, phloretin (Pht) and naringenin (Nar), and of one common antibiotic ciprofloxacin (Cip), either alone or in combinations with two known inhibitors of the AcrB EP of Escherichia coli, a close homolog of the AcrAB-TolC EP of Pb1692. In addition, we also measured the expression of genes encoding for the EP, under similar conditions. Results: Using the FICI equation, we observed synergism between the EPIs and the phytochemicals, but not between the EPIs and the antibiotic, suggesting that EP inhibition potentiated the antimicrobial activity of the plant derived compounds, but not of Cip. Docking simulations were successfully used to rationalize these experimental results. Discussion: Our findings suggest that AcrAB-TolC plays an important role in survival and fitness of Pb1692 in the plant environment and that its inhibition is a viable strategy for controlling bacterial pathogenicity.

QSAR models are widely and successfully used in many research areas. The success of such models highly depends on molecular descriptors typically classified as 1D, 2D, 3D, or 4D. While 3D information is likely important, e. g., for modeling ligand-protein binding, previous comparisons between the performances of 2D and 3D descriptors were inconclusive. Yet in such comparisons the modeled ligands were not necessarily represented by their bioactive conformations. With this in mind, we mined the PDB for sets of protein-ligand complexes sharing the same protein for which uniform activity data were reported. The results, totaling 461 structures spread across six series were compiled into a carefully curated, first of its kind dataset in which each ligand is represented by its bioactive conformation. Next, each set was characterized by 2D, 3D and 2D + 3D descriptors and modeled using three machine learning algorithms, namely, k-Nearest Neighbors, Random Forest and Lasso Regression. Models' performances were evaluated on external test sets derived from the parent datasets either randomly or in a rational manner. We found that many more significant models were obtained when combining 2D and 3D descriptors. We attribute these improvements to the ability of 2D and 3D descriptors to code for different, yet complementary molecular properties.

Glass fragments found in crime scenes may constitute important forensic evidence when properly analyzed, for example, to determine their origin. This analysis could be greatly helped by having a large and diverse database of glass fragments and by using it for constructing reliable machine learning (ML)-based glass classification models. Ideally, the samples that make up this database should be analyzed by a single accurate and standardized analytical technique. However, due to differences in equipment across laboratories, this is not feasible. With this in mind, in this work, we investigated if and how measurement performed at different laboratories on the same set of glass fragments could be combined in the context of ML. First, we demonstrated that elemental analysis methods such as particle-induced X-ray emission (PIXE), laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), scanning electron microscopy with energy-dispersive X-ray spectrometry (SEM-EDS), particle-induced Gamma-ray emission (PIGE), instrumental neutron activation analysis (INAA), and prompt Gamma-ray neutron activation analysis (PGAA) could each produce lab-specific ML-based classification models. Next, we determined rules for the successful combinations of data from different laboratories and techniques and demonstrated that when followed, they give rise to improved models, and conversely, poor combinations will lead to poor-performing models. Thus, the combination of PIXE and LA-ICP-MS improves the performances by ∼10-15%, while combining PGAA with other techniques provides poorer performances in comparison with the lab-specific models. Finally, we demonstrated that the poor performances of the SEM-EDS technique, still in use by law enforcement agencies, could be greatly improved by replacing SEM-EDS measurements for Fe and Ca by PIXE measurements for these elements. These findings suggest a process whereby forensic laboratories using different elemental analysis techniques could upload their data into a unified database and get reliable classification based on lab-agnostic models. This in turn brings us closer to a more exhaustive extraction of information from glass fragment evidence and furthermore may form the basis for international-wide collaboration between law enforcement agencies.

Virtual screening (VS) is a well-established method in the initial stages of many drug and material design projects. VS is typically performed using structure-based approaches such as molecular docking, or various ligand-based approaches. Most docking tools were designed to be as global as possible, and consequently only require knowledge on the 3D structure of the biotarget. In contrast, many ligand-based approaches (e.g., 3D-QSAR and pharmacophore) require prior development of project-specific predictive models. Depending on the type of model (e.g., classification or regression), predictive ability is typically evaluated using metrics of performance on either the training set (e.g.,QCV2) or the test set (e.g., specificity, selectivity or QF1/F2/F32). However, none of these metrics were developed with VS in mind, and consequently, their ability to reliably assess the performances of a model in the context of VS is at best limited. With this in mind we have recently reported the development of the enrichment optimization algorithm (EOA). EOA derives QSAR models in the form of multiple linear regression (MLR) equations for VS by optimizing an enrichment-based metric in the space of the descriptors. Here we present an improved version of the algorithm which better handles active compounds and which also takes into account information on inactive (either known inactive or decoy) compounds. We compared the improved EOA in small-scale VS experiments with three common docking tools, namely, Glide-SP, GOLD and AutoDock Vina, employing five molecular targets (acetylcholinesterase, human immunodeficiency virus type 1 protease, MAP kinase p38 alpha, urokinase-type plasminogen activator, and trypsin I). We found that EOA consistently outperformed all docking tools in terms of the area under the ROC curve (AUC) and EF1% metrics that measured the overall and initial success of the VS process, respectively. This was the case when the docking metrics were calculated based on a consensus approach and when they were calculated based on two different sets of single crystal structures. Finally, we propose that EOA could be combined with molecular docking to derive target-specific scoring functions.

The International Atomic Energy Agency (IAEA) has coordinated a research project titled "Enhancing Nuclear Analytical Techniques to Meet the Needs of Forensics Sciences" (CRP F11021) with the aim of empowering accelerator and reactor based techniques for applications in forensic sciences. One of the key topics of this project was the analysis and classification of forensic glass specimens using Ion Beam Analysis (IBA) techniques and in particular, Particle Induced X-ray Emission (PIXE). To this end, glass fragments from car windows from different car models and manufacturers provided by the Israeli police force were subjected to PIXE measurements at three laboratories to determine their elemental compositions and possible glass corrosion. Major and trace elements were measured and given as an input to machine learning (ML) algorithms in order to develop classification models to determine the origin of the glass samples. First, we have developed ML models based on the results obtained at each lab. These models successfully classified glass fragments into different car models with an accuracy > 80% on external test sets. Next, we demonstrated that following an appropriate pre-processing step, results from different labs could be combined into a single unified database for the derivation of a classification model. This model demonstrates good performances that matches or surpasses the performances of models derived from the individual labs. This finding paves the way towards establishing an international database that is composed of measurements from various PIXE labs. We believe that using this methodology of combining various sources of measurements will improve models' performances and generality and will make the models accessible to law enforcement agencies around the world. (C) 2022 Elsevier B.V. All rights reserved.

Dye-sensitized solar cells (DSSCs) are cost-effective, sustainable, and versatile electricity producers, allowing them to be incorporated into a variety of devices. In this work, we explore the usage of pharmacophore modeling to identify metal-free dyes for DSSCs by means of virtual screening. Pharmacophore models were built based on experimentally tested sensitizers. Virtual screening was performed against a large dataset of commercially available compounds taken from the ZINC15 library and identified multiple virtual hits. A subset of these hits was subjected to DFT and time-dependent-DFT calculations leading to the identification of two compounds, TSC6 and ASC5 , with appropriate molecular orbitals energies, favorable localization, and reasonable absorption UV–vis spectra. These results suggest that pharmacophore models, traditionally used in drug discovery and lead optimization, successfully predicted electronic properties, which are in agreement with the theoretical requirements for sensitizers. Such models may therefore find additional usages as modeling tools in materials sciences.

The effects of phloretin a phytoalexin from apple, was tested on Pectobacterium brasiliense (Pb1692), an emerging soft-rot pathogen of potato. Exposure of Pb1692 to 0.2 mM phloretin a concentration that does not affect growth, or to 0.4 mM a 50% growth inhibiting concentration (50% MIC), reduced motility, biofilm formation, secretion of plant cell wall-degrading enzymes, production of acyl–homoserine lactone (AHL) signaling molecules and infection, phenotypes that are associated with bacterial population density-dependent system known as quorum sensing (QS). To analyze the effect of growth inhibition on QS, the activity of ciprofloxacin, an antibiotic that impairs cell division, was compared to that of phloretin at 50% MIC. Unlike phloretin, the antibiotic hardly affected the tested phenotypes. The use of DH5α, a QS-negative Escherichia coli strain, transformed with an AHL synthase (ExpI) from Pb1692, allowed to validate direct inhibition of AHL production by phloretin, as demonstrated by two biosensor strains, Chromobacterium violaceaum (CV026) and E. coli (pSB401). Expression analysis of virulence-related genes revealed downregulation of QS-regulated genes ( expI , expR , luxS, rsmB ), plant cell wall degrading enzymes genes ( pel, peh and prt ) and motility genes ( motA, fim, fliA, flhC and flhD ) following exposure to both phloretin concentrations. The results support the inhibition of ExpI activity by phloretin. Docking simulations were used to predict the molecular associations between phloretin and the active site of ExpI, to suggest a likely mode of action for the compound’s inhibition of virulence.

Patients with cystic fibrosis (CF) harboring the P67L variant in the cystic fibrosis transmembrane conductance regulator (CFTR) often exhibit a typical CF phenotype, including severe respiratory compromise. This rare mutation (reported in <300 patients worldwide) responds robustly to CFTR correctors, such as lumacaftor and tezacaftor, with rescue in model systems that far exceed what can be achieved for the archetypical CFTR mutant F508del. However, the specific molecular consequences of the P67L mutation are poorly characterized. In this study, we conducted biochemical measurements following low-temperature growth and/or intragenic suppression, which suggest a mechanism underlying P67L that (1) shares key pathogenic features with F508del, including off-pathway (non-native) folding intermediates, (2) is linked to folding stability of nucleotide-binding domains 1 and 2, and (3) demonstrates pharmacologic rescue that requires domains in the carboxyl half of the protein. We also investigated the "lasso" helices 1 and 2, which occur immediately upstream of P67. Based on limited proteolysis, pulse chase, and molecular dynamics analysis of full-length CFTR and a series of deletion constructs, we argue that P67L and other maturational processing (class 2) defects impair the integrity of the lasso motif and confer misfolding of downstream domains. Thus, amino-terminal missense variants elicit a conformational change throughout CFTR that abrogates maturation while providing a robust substrate for pharmacologic repair.

This paper presents a structured workflow for glass fragment analysis based on a combination of Elemental Analysis using PIXE and Machine Learning tools, with the ultimate goal of standardizing and helping forensic efforts. The proposed workflow was implemented on glass fragments received from the Israeli DIFS (Israeli Police Force's Division of Identification and Forensic Sciences) that were collected from various vehicles, including glass fragments from different manufacturers and years of production. We demonstrate that this workflow can produce models with high (>80%) accuracy in identifying glass fragment's origins and provide a test-case demonstrating how the model can be applied in real-life forensic events. We provide a standard, reproducible methodology that can be used in many forensic domains beyond glass fragments, for example, Gun Shot Residue, flammable liquids, illegal substances, and more.