Analysis of AlphaFold and molecular dynamics structure predictions of mutations in serpins.

Serine protease inhibitors (serpins) include thousands of structurally conserved proteins playing key roles in many organisms. Mutations affecting serpins may disturb their conformation, leading to inactive forms. Unfortunately, conformational consequences of serpin mutations are difficult to predict. In this study, we integrate experimental data of patients with mutations affecting one serpin with the predictions obtained by AlphaFold and molecular dynamics. Five SERPINC1 mutations causing antithrombin deficiency, the strongest congenital thrombophilia were selected from a cohort of 350 unrelated patients based on functional, biochemical, and crystallographic evidence supporting a folding defect. AlphaFold gave an accurate prediction for the wild-type structure. However, it also produced native structures for all variants, regardless of complexity or conformational consequences in vivo. Similarly, molecular dynamics of up to 1000 ns at temperatures causing conformational transitions did not show significant changes in the native structure of wild-type and variants. In conclusion, AlphaFold and molecular dynamics force predictions into the native conformation at conditions with experimental evidence supporting a conformational change to other structures. It is necessary to improve predictive strategies for serpins that consider the conformational sensitivity of these molecules.

Phage display identification of high-affinity ligands for human TSG101-UEV: A structural and thermodynamic study of PTAP recognition.

The ubiquitin E2 variant domain of TSG101 (TSG101-UEV) plays a pivotal role in protein sorting and virus budding by recognizing PTAP motifs within ubiquitinated proteins. Disrupting TSG101-UEV/PTAP interactions has emerged as a promising strategy for the development of novel host-oriented antivirals with a broad spectrum of action. Nonetheless, finding inhibitors with good properties as therapeutic agents remains a challenge since the key determinants of binding affinity and specificity are still poorly understood. Here we present a detailed thermodynamic, structural, and dynamic characterization viral PTAP Late domain recognition by TSG101-UEV, combining isothermal titration calorimetry, X-ray diffraction structural studies, molecular dynamics simulations, and computational analysis of intramolecular communication pathways. Our analysis highlights key contributions from conserved hydrophobic contacts and water-mediated hydrogen bonds at the PTAP binding interface. We have identified additional electrostatic hotspots adjacent to the core motif that modulate affinity. Using competitive phage display screening we have improved affinity by 1-2 orders of magnitude, producing novel peptides with low micromolar affinities that combine critical elements found in the best natural binders. Molecular dynamics simulations revealed that optimized peptides engage new pockets on the UEV domain surface. This study provides a comprehensive view of the molecular forces directing TSG101-UEV recognition of PTAP motifs, revealing that binding is governed by conserved structural elements yet tuneable through targeted optimization. These insights open new venues to design inhibitors targeting TSG101-dependent pathways with potential application as novel broad-spectrum antivirals.

Research into how carvacrol and metformin affect several human proteins in a hyperglycemic condition: A comparative study in silico and in vitro.

Carvacrol (CV) is an organic compound found in the essential oils of many aromatic herbs. It is nearly unfeasible to analyze all the current human proteins for a query ligand using in vitro and in vivo methods. This study aimed to clarify whether CV possesses an anti-diabetic feature via Docking-based inverse docking and molecular dynamic (MD) simulation and in vitro characterization against a set of novel human protein targets. Herein, the best poses of CV docking simulations according to binding energy ranged from -7.9 to -3.5 (kcal/mol). After pathway analysis of the protein list through GeneMANIA and WebGestalt, eight interacting proteins (DPP4, FBP1, GCK, HSD11ß1, INSR, PYGL, PPARA, and PPARG) with CV were determined, and these proteins exhibited stable structures during the MD process with CV. In vitro application, statistically significant results were achieved only in combined doses with CV or metformin. Considering all these findings, PPARG and INSR, among these target proteins of CV, are FDA-approved targets for treating diabetes. Therefore, CV may be on its way to becoming a promising therapeutic compound for treating Diabetes Mellitus (DM). Our outcomes expose formerly unexplored potential target human proteins, whose association with diabetic disorders might guide new potential treatments for DM.

Geometric deep learning methods and applications in 3D structure-based drug design.

3D structure-based drug design (SBDD) is considered a challenging and rational way for innovative drug discovery. Geometric deep learning is a promising approach that solves the accurate model training of 3D SBDD through building neural network models to learn non-Euclidean data, such as 3D molecular graphs and manifold data. Here, we summarize geometric deep learning methods and applications that contain 3D molecular representations, equivariant graph neural networks (EGNNs), and six generative model methods [diffusion model, flow-based model, generative adversarial networks (GANs), variational autoencoder (VAE), autoregressive models, and energy-based models]. Our review provides insights into geometric deep learning methods and advanced applications of 3D SBDD that will be of relevance for the drug discovery community.

Monastrol suppresses invasion and metastasis in human colorectal cancer cells by targeting fascin independent of kinesin-Eg5 pathway.

Rearrangement of the actin cytoskeleton is a prerequisite for carcinoma cells to develop cellular protrusions, which are required for migration, invasion, and metastasis. Fascin is a key protein involved in actin bundling and is expressed in aggressive and invasive carcinomas. Additionally, fascin appears to be involved in tubulin-binding and microtubule rearrangement. Pharmacophoric-based in silico screening was performed to identify compounds with better fascin inhibitory properties than migrastatin, a gold-standard fascin inhibitor. We hypothesized that monastrol displays anti-migratory and anti-invasive properties via fascin blocking in colorectal cancer cell lines. Biophysical (thermofluor and ligand titration followed by fluorescence spectroscopy), biochemical (NMR), and cellular assays (MTT, invasion of human tissue), as well as animal model studies (zebrafish invasion) were performed to characterize the inhibitory effect of monastrol on fascin activity. In silico analysis revealed that monastrol is a potential fascin-binding compound. Biophysical and biochemical assays demonstrated that monastrol binds to fascin and interferes with its actin-bundling activity. Cell culture studies, including a 3D human myoma disc model, showed that monastrol inhibited fascin-driven cytoplasmic protrusions as well as invasion. In silico, confocal microscopy, and immunoprecipitation assays demonstrated that monastrol disrupted fascin-tubulin interactions. These anti-invasive effects were confirmed in vivo. In silico confocal microscopy and immunoprecipitation assays were carried out to test whether monastrol disrupted the fascin-tubulin interaction. This study reports, for the first time, the in vitro and in vivo anti-invasive properties of monastrol in colorectal tumor cells. The number and types of interactions suggest potential binding of monastrol across actin and tubulin sites on fascin, which could be valuable for the development of antitumor therapies.

Identification of potent inhibitors of kynurenine-3-monooxygenase from natural products: In silico and in vitro approaches.

Existing inhibitors of kynurenine-3-monooxygenase (KMO) have side effects and poorly cross the blood-brain barrier. Therefore, the discovery of new molecules targeting KMO isnecessary.This study aims to develop a novel therapeutic drug targeting KMO using computational methods and experimental validation of natural compounds.The results of our study show that the top four compounds, namely, 3'-Hydroxy-alpha-naphthoflavone exhibited the best docking scores with KMO (-10.0 kcal/mol), followed by 3'-Hydroxy-ss-naphthoflavone (-9.9 kcal/mol), genkwanin (-9.2 kcal/mol) and apigenin(-9.1 kcal/mol) respectively. Molecular dynamics was used to assess the stability of the primary target, KMO, and inhibitor complexes. We found stable interactions of 3'-Hydroxy-ss-naphthoflavone and apigenin with KMO up to 100 ns. Further, kinetic measurements showed that 3'-Hydroxy-alpha-naphthoflavone and 3'-Hydroxy-ss-naphthoflavone induce competitive inhibition with a good IC50 activity (15.85 ± 0.98 µM and 18.71 ± 0.78, respectively), while Genkwanin and Apigenin exhibit non-competitive inhibition mechanism (21.61 ± 0.97 µM and 24.14 ± 1.00 µM, respectively).Drug-likeness features and ADME analysis features also showed that the top four compounds could be used as potential candidates to replace the synthetic KMO inhibitor drugs with known side effects and poor brain-blood barrier penetration.

Enhancing MD simulations: ASGARD's automated analysis for GROMACS.

Molecular Dynamics (MD) simulations are essential in analyzing the physical movement of molecules, with GROMACS being a widely recognized open-source package for this purpose. However, conducting analyses individually in GROMACS can take excessive time and effort. Addressing this challenge, we introduce ASGARD, an innovative workflow designed to streamline and automate the analysis of MD simulation of protein or protein-ligand complex. Unlike the traditional, manual approach, ASGARD enables researchers to generate comprehensive analyses with a single command line, significantly accelerating the research process and avoiding the laborious task of manual report generation. This tool automatically performs a range of analyses post-simulation, including system stability and flexibility assessments through RMSD Fluctuation and Distribution calculations. It further provides dynamic analysis using SASA, DSSP method graphs, and various interaction analyses. A key feature of ASGARD is its user-friendly design; it requires no additional installations or dependencies, making it highly accessible for researchers. In conclusion, ASGARD simplifies the MD simulation analysis process and substantially enhances efficiency and productivity in molecular research by providing an integrated, one-command analysis solution.Communicated by Ramaswamy H. Sarma.

An Interpretable Machine Learning Approach to Predict Sensory Processing Sensitivity Trait in Nursing Students.

Sensory processing sensitivity (SPS) is a personality trait that makes certain individuals excessively sensitive to stimuli. People carrying this trait are defined as Highly Sensitive People (HSP). The SPS trait is notably prevalent among nursing students and nurse staff. Although there are HSP diagnostic tools, there is little information about early detection. Therefore, the aim of this work was to develop a prediction model to identify HSP and provide an individualized nursing assessment. A total of 672 nursing students completed all the evaluations. In addition to the HSP diagnosis, emotional intelligence, communication skills, and conflict styles were evaluated. An interpretable machine learning model was trained to predict the SPS trait. We observed a 33% prevalence of HSP, which was higher in women and people with previous health training. HSP were characterized by greater emotional repair (p = 0.033), empathy (p = 0.030), respect (p = 0.038), and global communication skills (p = 0.036). Overall, sex and emotional intelligence dimensions are important to detect this trait, although personal characteristics should be considered. The present individualized prediction model could help to predict the presence of the SPS trait in nursing students, which may be useful in conducting intervention strategies to avoid the negative consequences and reinforce the positive ones of this trait.

Comparative assessment of different anti-CD147/Basigin 2 antibodies as a potential therapeutic anticancer target by molecular modeling and dynamic simulation.

Cluster of differentiation 147 (CD147) is an attractive target for anticancer therapy since it is pivotal in developing and progressing several of malignant tumors in the context of its high expression levels. Although anti-CD147 antibodies by different laboratories are designed for the Ig-like domains of CD147, there is a demand to provide priority among these anti-CD147 antibodies for developing of therapeutic anti-CD147 antibody before experimental validations. This study uses molecular docking and dynamic simulation techniques to compare the binding modes and affinities of nine antibody models against the Ig-like domains of CD147. After obtaining the model antibodies by homology modeling via Robetta, we predicted the CDRs of nine antibodies and the epitopes of CD147 to reach more accurate results for antigen affinity in molecular docking. Next, from HADDOCK 2.4., we meticulously handpicked the most superior model clusters (Z-Score: - 2.5 to - 1.2) and identified that meplazumab had higher affinities according to the success rate as the percentage of a scoring scale. We achieved stable simulations of CD147-antibody interaction. Our outcomes hold hypothetical importance for further experimental cancer research on the design and development of the relevant model antibodies.

Vitamin-C-dependent downregulation of the citrate metabolism pathway potentiates pancreatic ductal adenocarcinoma growth arrest.

In pancreatic ductal adenocarcinoma (PDAC), metabolic rewiring and resistance to standard therapy are closely associated. PDAC cells show enormous requirements for glucose-derived citrate, the first rate-limiting metabolite in the synthesis of new lipids. Both the expression and activity of citrate synthase (CS) are extraordinarily upregulated in PDAC. However, no previous relationship between gemcitabine response and citrate metabolism has been documented in pancreatic cancer. Here, we report for the first time that pharmacological doses of vitamin C are capable of exerting an inhibitory action on the activity of CS, reducing glucose-derived citrate levels. Moreover, ascorbate targets citrate metabolism towards the de novo lipogenesis pathway, impairing fatty acid synthase (FASN) and ATP citrate lyase (ACLY) expression. Lowered citrate availability was found to be directly associated with diminished proliferation and, remarkably, enhanced gemcitabine response. Moreover, the deregulated citrate-derived lipogenic pathway correlated with a remarkable decrease in extracellular pH through inhibition of lactate dehydrogenase (LDH) and overall reduced glycolytic metabolism. Modulation of citric acid metabolism in highly chemoresistant pancreatic adenocarcinoma, through molecules such as vitamin C, could be considered as a future clinical option to improve patient response to standard chemotherapy regimens.

ESSENCE-Dock: A Consensus-Based Approach to Enhance Virtual Screening Enrichment in Drug Discovery.

Drug development is a complex, costly, and time-consuming endeavor. While high-throughput screening (HTS) plays a critical role in the discovery stage, it is one of many factors contributing to these challenges. In certain contexts, virtual screening can complement the HTS, potentially offering a more streamlined approach in the initial stages of drug discovery. Molecular docking is an example of a popular virtual screening technique that is often used for this purpose; however, its effectiveness can vary greatly. This has led to the use of consensus docking approaches that combine results from different docking methods to improve the identification of active compounds and reduce the occurrence of false positives. However, many of these methods do not fully leverage the latest advancements in molecular docking. In response, we present ESSENCE-Dock (Effective Structural Screening ENrichment ConsEnsus Dock), a new consensus docking workflow aimed at decreasing false positives and increasing the discovery of active compounds. By utilizing a combination of novel docking algorithms, we improve the selection process for potential active compounds. ESSENCE-Dock has been made to be user-friendly, requiring only a few simple commands to perform a complete screening while also being designed for use in high-performance computing (HPC) environments.

Identification of new potential candidates to inhibit EGF via machine learning algorithm.

One of the cost-effective alternative methods to find new inhibitors has been the repositioning approach of existing drugs. The advantage of computational drug repositioning method is saving time and cost to remove the pre-clinical step and accelerate the drug discovery process. Hence, an ensemble computational-experimental approach, consisting of three steps, a machine learning model, simulation of drug-target interaction and experimental characterization, was developed. The machine learning type used here was a different tree classification method, which is one of the best randomize machine learning model to identify potential inhibitors from weak inhibitors. This model was trained more than one-hundred times, and forty top trained models were extracted for the drug repositioning step. The machine learning step aimed to discover the approved drugs with the highest possible success rate in the experimental step. Therefore, among all the identified molecules with more than 0.9 probability in more than 70% of the models, nine compounds, were selected. Besides, out of the nine chosen drugs, seven compounds have been confirmed to inhibit EGF in the published articles since 2019. Hence, two identified compounds, in addition to gefitinib, as a positive control, five weak-inhibitors and one neutral, were considered via molecular docking study. Finally, the eight proposed drugs, including gefitinib, were investigated using MTT assay and In-Cell ELISA to characterize the drugs' effect on A431 cell growth and EGF-signaling. From our experiments, we could conclude that salicylic acid and piperazine could play an EGF-inhibitor role like gefitinib.

Suramin, a drug for the treatment of trypanosomiasis, reduces the prothrombotic and metastatic phenotypes of colorectal cancer cells by inhibiting hepsin.

Recently, our group identified serine-protease hepsin from primary tumor as a biomarker of metastasis and thrombosis in patients with localized colorectal cancer. We described hepsin promotes invasion and thrombin generation of colorectal cancer cells in vitro and in vivo and identified venetoclax as a hepsin inhibitor that suppresses these effects. Now, we aspire to identify additional hepsin inhibitors, aiming to broaden the therapeutic choices for targeted intervention in colorectal cancer. METHODS: We developed a virtual screening based on molecular docking between the hepsin active site and 2000 compounds from DrugBank. The most promising drug was validated in a hepsin activity assay. Subsequently, we measured the hepsin inhibitor effect on colorectal cancer cells with basal or overexpression of hepsin via wound-healing, gelatin matrix invasion, and plasma thrombin generation assays. Finally, a zebrafish model determined whether hepsin inhibition reduced the invasion of colorectal cancer cells overexpressing hepsin. RESULTS: Suramin was the most potent hepsin inhibitor (docking score: -11.9691 Kcal/mol), with an IC50 of 0.66 µM. In Caco-2 cells with basal or overexpression of hepsin, suramin decreased migration and significantly reduced invasion and thrombin generation. Suramin did not reduce the thrombotic phenotype in the hepsin-negative colorectal cancer cells HCT-116 and DLD-1. Finally, suramin significantly reduced the in vivo invasion of Caco-2 cells overexpressing hepsin. CONCLUSION: Suramin is a novel hepsin inhibitor that reduces its protumorigenic and prothrombotic effects in colorectal cancer cells. This suggests the possibility of repurposing suramin and its derivatives to augment the repertoire of molecular targeted therapies against colorectal cancer.

Antcin-B, a phytosterol-like compound from Taiwanofungus camphoratus inhibits SARS-CoV-2 3-chymotrypsin-like protease (3CLPro) activity in silico and in vitro.

Despite the remarkable development of highly effective vaccines, including mRNA-based vaccines, within a limited timeframe, coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is not been entirely eradicated. Thus, it is crucial to identify new effective anti-3CLPro compounds, pivotal for the replication of SARS-CoV-2. Here, we identified an antcin-B phytosterol-like compound from Taiwanofungus camphoratus that targets 3CLPro activity. MTT assay and ADMET prediction are employed for assessing potential cytotoxicity. Computational molecular modeling was used to screen various antcins and non-antcins for binding affinity and interaction type with 3CLPro. Further, these compounds were subjected to study their inhibitory effects on 3CLPro activity in vitro. Our results indicate that antcin-B has the best binding affinity by contacting residues like Leu141, Asn142, Glu166, and His163 via hydrogen bond and salt bridge and significantly inhibits 3CLPro activity, surpassing the positive control compound (GC376). The 100 ns molecular dynamics simulation studies showed that antcin-B formed consistent, long-lasting water bridges with Glu166 for their inhibitory activity. In summary, antcin-B could be useful to develop therapeutically viable drugs to inhibit SARS-CoV-2 replication alone or in combination with medications specific to other SARS-CoV-2 viral targets.

Exploring the Antioxidant Properties of Caffeoylquinic and Feruloylquinic Acids: A Computational Study on Hydroperoxyl Radical Scavenging and Xanthine Oxidase Inhibition.

Caffeoylquinic (5-CQA) and feruloylquinic (5-FQA) acids, found in coffee and other plant sources, are known to exhibit diverse biological activities, including potential antioxidant effects. However, the underlying mechanisms of these phenolic compounds remain elusive. This paper investigates the capacity and mode of action of 5-CQA and 5-FQA as natural antioxidants acting as hydroperoxyl radical scavengers and xanthine oxidase (XO) inhibitors. The hydroperoxyl radical scavenging potential was investigated using thermodynamic and kinetic calculations based on the DFT method, taking into account the influence of physiological conditions. Blind docking and molecular dynamics simulations were used to investigate the inhibition capacity toward the XO enzyme. The results showed that 5-CQA and 5-FQA exhibit potent hydroperoxyl radical scavenging capacity in both polar and lipidic physiological media, with rate constants higher than those of common antioxidants, such as Trolox and BHT. 5-CQA carrying catechol moiety was found to be more potent than 5-FQA in both physiological environments. Furthermore, both compounds show good affinity with the active site of the XO enzyme and form stable complexes. The hydrogen atom transfer (HAT) mechanism was found to be exclusive in lipid media, while both HAT and SET (single electron transfer) mechanisms are possible in water. 5-CQA and 5-FQA may, therefore, be considered potent natural antioxidants with potential health benefits.

Effects of In Vitro Digestion of Polyphenols from Coffee on Binding Parameters to Human Topoisomerase II α.

Type II topoisomerase (TOPII) is an enzyme that influences the topology of DNA. DNA breaks generated by TOPII may result in mutagenic or cytotoxic changes in cancer cells. In this study, we characterized interactions of TOPIIα with coffee extracts and individual chlorogenic acids (CHAs) from the extracts by performing isothermal titration calorimetry (ITC) and molecular docking (MD) simulations. The study showed that the highest affinity to TOPIIα was found in green coffee (ΔG = -38.23 kJ/mol) and monochlorogenic acids fraction of coffee extracts (ΔG = -35.80 kJ/mol), resulting from the high content of polyphenols, such as CHAs, which can bind to the enzyme in the active site. Coffee extracts and their fractions maintained a high affinity for TOPIIα after simulated digestion in the presence of probiotic bacteria. It can be concluded that coffee may be a potential TOPIIα inhibitor considered as a functional food for cancer prevention.

Venetoclax is a potent hepsin inhibitor that reduces the metastatic and prothrombotic phenotypes of hepsin-expressing colorectal cancer cells.

Introduction: Hepsin is a type II transmembrane serine protease and its expression has been linked to greater tumorigenicity and worse prognosis in different tumors. Recently, our group demonstrated that high hepsin levels from primary tumor were associated with a higher risk of metastasis and thrombosis in localized colorectal cancer patients. This study aims to explore the molecular role of hepsin in colorectal cancer. Methods: Hepsin levels in plasma from resected and metastatic colorectal cancer patients were analyzed by ELISA. The effect of hepsin levels on cell migration, invasion, and proliferation, as well as on the activation of crucial cancer signaling pathways, was performed in vitro using colorectal cancer cells. A thrombin generation assay determined the procoagulant function of hepsin from these cells. A virtual screening of a database containing more than 2000 FDA-approved compounds was performed to screen hepsin inhibitors, and selected compounds were tested in vitro for their ability to suppress hepsin effects in colorectal cancer cells. Xenotransplantation assays were done in zebrafish larvae to study the impact of venetoclax on invasion promoted by hepsin. Results: Our results showed higher plasma hepsin levels in metastatic patients, among which, hepsin was higher in those suffering thrombosis. Hepsin overexpression increased colorectal cancer cell invasion, Erk1/2 and STAT3 phosphorylation, and thrombin generation in plasma. In addition, we identified venetoclax as a potent hepsin inhibitor that reduced the metastatic and prothrombotic phenotypes of hepsin-expressing colorectal cancer cells. Interestingly, pretreatment with Venetoclax of cells overexpressing hepsin reduced their invasiveness in vivo. Discussion: Our results demonstrate that hepsin overexpression correlates with a more aggressive and prothrombotic tumor phenotype. Likewise, they demonstrate the antitumor role of venetoclax as a hepsin inhibitor, laying the groundwork for molecular-targeted therapy for colorectal cancer.

Effect of Inhibiting Butyrylcholinesterase Activity Using Fractionated Coffee Extracts Digested In Vitro in Gastrointestinal Tract: Docking Simulation and Calorimetric and Studies.

Butyrylcholinesterase (BChE) is a major enzyme from the alpha-glycoprotein family that catalyzes the hydrolysis of neurotransmitter acetylcholine (ACh), lowering the concentration of ACh in the nervous system, which could cause aggravation of Alzheimer's disease (AD). In select pathological conditions, it is beneficial to reduce the activity of this enzyme. The aim of this study was to evaluate the degree of BChE inhibition by coffee extracts fractionated into mono- and diesters of caffeic acid/caffeine, digested in vitro in the gastrointestinal tract. The bioactive compounds from coffee showed high affinity for BchE, -30.23--15.28 kJ/mol, and was the highest for the caffeine fraction from the green Arabica extract. The isolated fractions were highly effective in inhibiting BChE activity at all in vitro digestion phases. It has been shown that the fractionation of coffee extracts could be potentially used to obtain high prophylactic or even therapeutic effectiveness against AD.

2D finger-printing and molecular docking studies identified potent mosquito repellents targeting odorant binding protein 1.

Personal protection measures against the mosquitoes like the use of repellents constitute valuable tools in the effort to prevent the transmission of vector-borne diseases. Therefore, the discovery of novel repellent molecules which will be effective at lower concentrations and provide a longer duration of protection remains an urgent need. Mosquito Odorant-Binding Proteins (OBPs) involved in the initial steps of the olfactory signal transduction cascade have been recognized not only as passive carriers of odors and pheromones but also as the first molecular filter to discriminate semiochemicals, hence serving as molecular targets for the design of novel pest control agents. Among the three-dimensional structures of mosquito OBPs solved in the last decades, the OBP1 complexes with known repellents have been widely used as reference structures in docking analysis and molecular dynamics simulation studies for the structure-based discovery of new molecules with repellent activity. Herein, ten compounds known to be active against mosquitoes and/or displaying a binding affinity for Anopheles gambiae AgamOBP1 were used as queries in an in silico screening of over 96 million chemical samples in order to detect molecules with structural similarity. Further filtering of the acquired hits on the basis of toxicity, vapor pressure, and commercial availability resulted in 120 unique molecules that were subjected to molecular docking studies against OBP1. For seventeen potential OBP1-binders, the free energy of binding (FEB) and mode of interaction with the protein were further estimated by molecular docking simulations leading to the selection of eight molecules exhibiting the highest similarity with their parental compounds and favorable energy values. The in vitro determination of their binding affinity to AgamOBP1 and the evaluation of their repellent activity against female Aedes albopictus mosquitoes revealed that our combined ligand similarity screening and OBP1 structure-based molecular docking successfully detected three molecules with enhanced repellent properties. A novel DEET-like repellent with lower volatility (8.55 × 10-4 mmHg) but a higher binding affinity for OBP1 than DEET (1.35 × 10-3 mmHg). A highly active repellent molecule that is predicted to bind to the secondary Icaridin (sIC)-binding site of OBP1 with higher affinity than to the DEET-site and, therefore, represents a new scaffold to be exploited for the discovery of binders targeting multiple OBP sites. Finally, a third potent repellent exhibiting a high degree of volatility was found to be a strong DEET-site binder of OBP1 that could be used in slow-release formulations.

Fishing the Targets of Bioactive Compounds from Psidium guajava L. Leaves in the Context of Diabetes.

Psidium guajava L. (guava) leaves have demonstrated their in vitro and in vivo effect against diabetes mellitus (DM). However, there is a lack of literature concerning the effect of the individual phenolic compounds present in the leaves in DM disease. The aim of the present work was to identify the individual compounds in Spanish guava leaves and their potential contribution to the observed anti-diabetic effect. Seventy-three phenolic compounds were identified from an 80% ethanol extract of guava leaves by high performance liquid chromatography coupled to electrospray ionization and quadrupole time-of-flight mass spectrometry. The potential anti-diabetic activity of each compound was evaluated with the DIA-DB web server that uses a docking and molecular shape similarity approach. The DIA-DB web server revealed that aldose reductase was the target protein with heterogeneous affinity for compounds naringenin, avicularin, guaijaverin, quercetin, ellagic acid, morin, catechin and guavinoside C. Naringenin exhibited the highest number of interactions with target proteins dipeptidyl peptidase-4, hydroxysteroid 11-beta dehydrogenase 1, aldose reductase and peroxisome proliferator-activated receptor. Compounds catechin, quercetin and naringenin displayed similarities with the known antidiabetic drug tolrestat. In conclusion, the computational workflow showed that guava leaves contain several compounds acting in the DM mechanism by interacting with specific DM protein targets.