A Repurposing Pipeline to Candidate-Suitable Inhibitors of Tyrosinase: Computational and Bioassay Studies.

INTRODUCTION: Tyrosinase, a metalloprotein enzyme, plays a crucial role in melanin synthesis by hydroxylating L-tyrosine to L-dopa. However, the accumulation of melanin can lead to hyperpigmented spots, raising aesthetic concerns. METHODS: In this study, we developed a pipeline to repurpose FDA-approved drugs as potential tyrosinase inhibitors. A structure-based screening study was conducted using 1,650 drugs to identify probable inhibitors based on binding energies. RESULTS: From the cluster analysis of binding interaction profiles, 16 compounds were selected as candidates. Montelukast emerged as the final candidate due to its favorable ADME properties. Bioassay evaluation revealed an IC50 value of 14.79 ± 0.87 µM for Montelukast, compared to kojic acid (IC50 = 31.02 ± 2.01 µM). Molecular dynamics simulation and g_MMPBSA free energy calculation studies were performed for the Tyrosinase-Montelukast complex. CONCLUSION: These findings enhance our understanding of Tyrosinase-Montelukast interactions and underscore Montelukast's potential as a tyrosinase inhibitor. This could have implications in dermatological applications and beyond, suggesting Montelukast as a promising candidate for further development in this regard.

Molecular docking studies, DFT, and ADMET calculations of some flavonoids and their characteristic structural features involved in inhibition of pro-inflammatory enzymes.

Inflammation is an immune system response triggered by pathogens, damaged cells, or stimuli. Some regulatory enzymes, such as phosphodiesterase, hyaluronidase, collagenase, and lipoxygenase, play an essential role in the inflammatory process. Polyphenolic compounds, such as flavonoids, are active suppressors of inflammatory cytokines, modulators of transcription factors, and inflammation-related pathways. A set of flavonoid structures was screened and docked against inflammation pathway enzymes. Amentoflavone has been shown to cause interactions with phosphodiesterase enzymes, while Bilobetin and Silibinin demonstrated an increase in binding energy with collagenase enzymes. The retrieved compounds from the docking study were subjected to DFT theory. The results showed that the LUMO orbital is located on the flavonoid part. The thermochemical parameters indicated that Silibinin is more stable than other compounds. The ADMET profile predicted that Silibinin can be used orally among the compounds. Silibinin can be introduced as a promising anti-inflammatory agent demonstrating phosphodiesterase and collagenase inhibitory properties.

Decrypting lysine deacetylase inhibitor action and protein modifications by dose-resolved proteomics.

Lysine deacetylase inhibitors (KDACis) are approved drugs for cutaneous T cell lymphoma (CTCL), peripheral T cell lymphoma (PTCL), and multiple myeloma, but many aspects of their cellular mechanism of action (MoA) and substantial toxicity are not well understood. To shed more light on how KDACis elicit cellular responses, we systematically measured dose-dependent changes in acetylation, phosphorylation, and protein expression in response to 21 clinical and pre-clinical KDACis. The resulting 862,000 dose-response curves revealed, for instance, limited cellular specificity of histone deacetylase (HDAC) 1, 2, 3, and 6 inhibitors; strong cross-talk between acetylation and phosphorylation pathways; localization of most drug-responsive acetylation sites to intrinsically disordered regions (IDRs); an underappreciated role of acetylation in protein structure; and a shift in EP300 protein abundance between the cytoplasm and the nucleus. This comprehensive dataset serves as a resource for the investigation of the molecular mechanisms underlying KDACi action in cells and can be interactively explored online in ProteomicsDB.

Decrypting the molecular basis of cellular drug phenotypes by dose-resolved expression proteomics.

Proteomics is making important contributions to drug discovery, from target deconvolution to mechanism of action (MoA) elucidation and the identification of biomarkers of drug response. Here we introduce decryptE, a proteome-wide approach that measures the full dose-response characteristics of drug-induced protein expression changes that informs cellular drug MoA. Assaying 144 clinical drugs and research compounds against 8,000 proteins resulted in more than 1 million dose-response curves that can be interactively explored online in ProteomicsDB and a custom-built Shiny App. Analysis of the collective data provided molecular explanations for known phenotypic drug effects and uncovered new aspects of the MoA of human medicines. We found that histone deacetylase inhibitors potently and strongly down-regulated the T cell receptor complex resulting in impaired human T cell activation in vitro and ex vivo. This offers a rational explanation for the efficacy of histone deacetylase inhibitors in certain lymphomas and autoimmune diseases and explains their poor performance in treating solid tumors.

A combination of virtual screening, molecular dynamics simulation, MM/PBSA, ADMET, and DFT calculations to identify a potential DPP4 inhibitor.

DPP4 inhibitors can control glucose homeostasis by increasing the level of GLP-1 incretins hormone due to dipeptidase mimicking. Despite the potent effects of DPP4 inhibitors, these compounds cause unwanted toxicity attributable to their effect on other enzymes. As a result, it seems essential to find novel and DPP4 selective compounds. In this study, we introduce a potent and selective DPP4 inhibitor via structure-based virtual screening, molecular docking, molecular dynamics simulation, MM/PBSA calculations, DFT analysis, and ADMET profile. The screened compounds based on similarity with FDA-approved DPP4 inhibitors were docked towards the DPP4 enzyme. The compound with the highest docking score, ZINC000003015356, was selected. For further considerations, molecular docking studies were performed on selected ligands and FDA-approved drugs for DPP8 and DPP9 enzymes. Molecular dynamics simulation was run during 200 ns and the analysis of RMSD, RMSF, Rg, PCA, and hydrogen bonding were performed. The MD outputs showed stability of the ligand-protein complex compared to available drugs in the market. The total free binding energy obtained for the proposed DPP4 inhibitor was more negative than its co-crystal ligand (N7F). ZINC000003015356 confirmed the role of the five Lipinski rule and also, have low toxicity parameter according to properties. Finally, DFT calculations indicated that this compound is sufficiently soft.

Correlation of Myeloid-Derived Suppressor Cell Expansion with Upregulated Transposable Elements in Severe COVID-19 Unveiled in Single-Cell RNA Sequencing Reanalysis.

Some studies have investigated the potential role of transposable elements (TEs) in COVID-19 pathogenesis and complications. However, to the best of our knowledge, there is no study to examine the possible association of TE expression in cell functions and its potential role in COVID-19 immune response at the single-cell level. In this study, we reanalyzed single-cell RNA seq data of bronchoalveolar lavage (BAL) samples obtained from six severe COVID-19 patients and three healthy donors to assess the probable correlation of TE expression with the immune responses induced by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in COVID-19 patients. Our findings indicate that the expansion of myeloid-derived suppressor cells (MDSCs) may be a characteristic feature of COVID-19. Additionally, a significant increase in TE expression in MDSCs was observed. This upregulation of TEs in COVID-19 may be linked to the adaptability of these cells in response to their microenvironments. Furthermore, it appears that the identification of overexpressed TEs by pattern recognition receptors (PRRs) in MDSCs may enhance the suppressive capacity of these cells. Thus, this study emphasizes the crucial role of TEs in the functionality of MDSCs during COVID-19.

Illuminating phenotypic drug responses of sarcoma cells to kinase inhibitors by phosphoproteomics.

Kinase inhibitors (KIs) are important cancer drugs but often feature polypharmacology that is molecularly not understood. This disconnect is particularly apparent in cancer entities such as sarcomas for which the oncogenic drivers are often not clear. To investigate more systematically how the cellular proteotypes of sarcoma cells shape their response to molecularly targeted drugs, we profiled the proteomes and phosphoproteomes of 17 sarcoma cell lines and screened the same against 150 cancer drugs. The resulting 2550 phenotypic profiles revealed distinct drug responses and the cellular activity landscapes derived from deep (phospho)proteomes (9-10,000 proteins and 10-27,000 phosphorylation sites per cell line) enabled several lines of analysis. For instance, connecting the (phospho)proteomic data with drug responses revealed known and novel mechanisms of action (MoAs) of KIs and identified markers of drug sensitivity or resistance. All data is publicly accessible via an interactive web application that enables exploration of this rich molecular resource for a better understanding of active signalling pathways in sarcoma cells, identifying treatment response predictors and revealing novel MoA of clinical KIs.

A region-resolved proteomic map of the human brain enabled by high-throughput proteomics.

Substantial efforts are underway to deepen our understanding of human brain morphology, structure, and function using high-resolution imaging as well as high-content molecular profiling technologies. The current work adds to these approaches by providing a comprehensive and quantitative protein expression map of 13 anatomically distinct brain regions covering more than 11,000 proteins. This was enabled by the optimization, characterization, and implementation of a high-sensitivity and high-throughput microflow liquid chromatography timsTOF tandem mass spectrometry system (LC-MS/MS) capable of analyzing more than 2,000 consecutive samples prepared from formalin-fixed paraffin embedded (FFPE) material. Analysis of this proteomic resource highlighted brain region-enriched protein expression patterns and functional protein classes, protein localization differences between brain regions and individual markers for specific areas. To facilitate access to and ease further mining of the data by the scientific community, all data can be explored online in a purpose-built R Shiny app (https://brain-region-atlas.proteomics.ls.tum.de).

Structure-Selectivity Relationship Prediction of Tau Imaging Tracers Using Machine Learning-Assisted QSAR Models and Interaction Fingerprint Map.

Protein aggregates composed of tau fibrils are major pathologic findings in different tauopathies. An ideal agent for imaging tau fibrils must be highly selective. The molecular basis for the binding of current available compounds to tau aggregates is not well understood. Herein, we provide insights into previously studied positron emission tomography tracers using various computational methods, including machine learning-based quantitative structure-activity relationship (QSAR) classification, docking, and molecular dynamics (MD) simulations to investigate the structural basis of selective tau aggregate binding for potential compounds. The QSAR classification model based on the Random Forest algorithm with an accuracy of 96.6% for the selective and 97.6% for the nonselective class of compounds revealed essential selective moieties. The combination of molecular docking, MD simulations, and molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) binding free-energy calculation showed superior binding energy of ligand 63 toward tau and PHF6, a key hexapeptide in tau aggregation, as the most selective compound in the data set. Dissecting the binding properties of ligand 63 and ligand 8 (the least selective compound) within tau and Aß structures confirmed that these two compounds favor different binding sites of tau; however, the preferential binding site in Aß was similar for both with lower binding energies calculated for ligand 8. Results revealed that the number of N-heterocycles, the position of nitrogen atoms, and the presence of tertiary amine are important components of selective binding moieties, and they should be maintained in molecules for selective binding to tau aggregates. The predicted structure-selectivity relationship will facilitate the rational design and further development of selective tau imaging agents.

Getting Ready for Large-Scale Proteomics in Crop Plants.

Plants are an indispensable cornerstone of sustainable global food supply. While immense progress has been made in decoding the genomes of crops in recent decades, the composition of their proteomes, the entirety of all expressed proteins of a species, is virtually unknown. In contrast to the model plant Arabidopsis thaliana, proteomic analyses of crop plants have often been hindered by the presence of extreme concentrations of secondary metabolites such as pigments, phenolic compounds, lipids, carbohydrates or terpenes. As a consequence, crop proteomic experiments have, thus far, required individually optimized protein extraction protocols to obtain samples of acceptable quality for downstream analysis by liquid chromatography tandem mass spectrometry (LC-MS/MS). In this article, we present a universal protein extraction protocol originally developed for gel-based experiments and combined it with an automated single-pot solid-phase-enhanced sample preparation (SP3) protocol on a liquid handling robot to prepare high-quality samples for proteomic analysis of crop plants. We also report an automated offline peptide separation protocol and optimized micro-LC-MS/MS conditions that enables the identification and quantification of ~10,000 proteins from plant tissue within 6 h of instrument time. We illustrate the utility of the workflow by analyzing the proteomes of mature tomato fruits to an unprecedented depth. The data demonstrate the robustness of the approach which we propose for use in upcoming large-scale projects that aim to map crop tissue proteomes.

Structure-based virtual screening, molecular docking, molecular dynamics simulation and MM/PBSA calculations towards identification of steroidal and non-steroidal selective glucocorticoid receptor modulators.

Glucocorticoids have been used in the treatment of many diseases including inflammatory and autoimmune diseases. Despite the wide therapeutic effects of synthetic glucocorticoids, the use of these compounds has been limited due to side effects such as osteoporosis, immunodeficiency, and hyperglycaemia. To this end, extensive studies have been performed to discover new glucocorticoid modulators with the aim of increasing affinity for the receptor and thus less side effects. In the present work, structure-based virtual screening was used for the identification of novel potent compounds with glucocorticoid effects. The molecules derived from ZINC database were screened on account of structural similarity with some glucocorticoid agonists as the template. Subsequently, molecular docking was performed on 200 selected compounds to obtain the best steroidal and non-steroidal conformations. Three compounds, namely ZINC_000002083318, ZINC_000253697499 and ZINC_000003845653, were selected with the binding energies of -11.5, -10.5, and -9.5 kcal/mol, respectively. Molecular dynamic simulations on superior structures were accomplished with the glucocorticoid receptor. Additionally, root mean square deviations, root mean square fluctuation, radius of gyration, hydrogen bonds, and binding-free energy analysis showed the binding stability of the proposed compounds compared to budesonide as an approved drug. The results demonstrated that all the compounds had suitable binding stability compared to budesonide, while ZINC_000002083318 showed a tighter binding energy compared to the other compounds.Communicated by Ramaswamy H. Sarma.

Design, synthesis and evaluation of novel 1,2,4-triazole derivatives as promising anticancer agents.

Herein, we reported the synthesis of nineteen novel 1,2,4-triazole derivatives including 1,3-diphenyl-2-(1H-1,2,4-triazol-1-yl) propan-1-ones (7a-e), 1-(1,3-diphenylpropan-2-yl)-1H-1,2,4-triazole (8a-c) and 1,4-diphenyl-2-(1H-1,2,4-triazol-1-yl) butane-1,4-diones (10a-k). The structures of these derivatives were confirmed by spectroscopic techniques like IR, 1H-NMR, Mass spectroscopy and Elemental analysis. The cytotoxic activities of the synthesized compounds were evaluated against three human cancer cell lines including MCF-7, Hela and A549 using MTT assay. Compounds 7d, 7e, 10a and 10d showed a promising cytotoxic activity lower than 12 µM against Hela cell line. The safety of these compounds was also, evaluated on MRC-5 as a normal cell line and relieved that most of the synthesized compounds have proper selectivity against normal and cytotoxic cancerous cell lines. Finally, molecular docking studies were also, done to understand the mechanism and binding modes of these derivatives in the binding pocket of aromatase enzyme as a possible target.

Efficient synthesis of 1,3-naphtoxazine derivatives using reusable magnetic catalyst (GO-Fe3O4-Ti(IV)): anticonvulsant evaluation and computational studies.

A series of 2-aryl/alkyl-2,3-dihydro-1H-naphtho[1,2-e][1,3]oxazines (S1-S11) were synthesized with an eco-friendly and recoverable nanocatalyst (GO-Fe3O4-Ti(IV)) as an efficient magnetic composite. The new nanocatalyst was characterized by FT-IR, XRD and, EDS analysis. A conformable procedure, easy to work up and having a short reaction time with high yields are some advantages of this method. The new catalyst is also thermal-stable, reusable and, environment-friendly. The chemical structures of the synthesized 1,3-oxazine compounds were confirmed by comparing their melting points with those reported in literature. Then, the anticonvulsant activity of these compounds was assessed by the intraperitoneal pentylenetetrazole test (ipPTZ). Compounds S10 and S11 displayed considerable activity against chemically-induced seizure tests. The molecular simulation was also done to achieve their binding affinities as γ-aminobutyric acid A (GABA-A) receptor agonists as an assumptive mechanism of their anticonvulsant action. The result of molecular studies represented strongly matched with biological activity. Molecular docking simulation of the potent compound (S10) and diazepam as the positive control was performed and some critical residues like Thr262, Asn265, Met286, Phe289, and Val290 were identified. Based on the anticonvulsant results and also in silico ADME predictions, S11 can be to become a potential drug candidate as an anticonvulsant agent.

EDTox: an R Shiny application to predict the endocrine disruption potential of compounds.

PURPOSE: Endocrine disruptors are a rising concern due to the wide array of health issues that it can cause. Although there are tools for mode of action (MoA)-based prediction of endocrine disruption (e.g. QSAR Toolbox and iSafeRat), none of them is based on toxicogenomics data. Here, we present EDTox, an R Shiny application enabling users to explore and use a computational method that we have recently published to identify and prioritize endocrine disrupting (ED) chemicals based on toxicogenomic data. The EDTox pipeline utilizes previously trained toxicogenomic-driven classifiers to make predictions on new untested compounds by using their molecular initiating events. Furthermore, the proposed R Shiny app allows users to extend the prediction systems by training and adding new classifiers based on new available toxicogenomic data. This functionality helps users to explore the ED potential of chemicals in new, untested exposure scenarios. AVAILABILITY AND IMPLEMENTATION: This tool is available as web application (www.edtox.fi) and stand-alone software on GitHub and Zenodo (https://doi.org/10.5281/zenodo.5817093). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PyProtModel: An easy to use GUI for comparative protein modeling.

A deep insight into the 3D structures of the proteins is essential to understand their functions and will be helpful in drug design and making decisions for the treatment of diseases. The 3D structure for less than one-thousandth of the protein sequences has been determined so far due to the slow structure elucidation procedures with experimental methods, such as X-ray. For this reason, different computational methods were used to determine the structure of the proteins, including template-based and de novo approaches. Much software has been developed to facilitate the computational approaches in protein modeling, some of which need user expertise and extensive knowledge in bioinformatics. The present study attempted to provide a user-friendly environment to motivate the researchers in computational biology, a Python-based interface based on MODELLER software. PyProtModel can be used as a toolbox in structural bioinformatics for protein modeling from sequence to the prediction of 3D structure. In addition, the users can take benefits of PyProtModel in order to prepare and evaluate protein PDB files prior to other in silico experiments. PyProtModel allows the user to apply different MODELLER options, automates, and speeds up time-consuming homology modeling steps. Different aspects of PyProtModel and a comprehensive view of the application will be discussed in this paper. The application is available for download and use at: https://github.com/msisakht/pyprotmodel.git.

A highly potential cleavable linker for tumor targeting antibody-chemokines.

Chemokines are the large family of chemotactic cytokines that play an important role in leukocyte movement and migration stimulation. Until now, several antibody-cytokine (chemokine) fusion proteins have been investigated in clinical trials because of their ability to evoke the circulating leukocytes far from the tumor site. In this case, creating the concentration gradient regarding the chemokine is very important to recruit the circulating leukocytes with maximum performance to the tumor environment. To achieve a proper gradient, the chemokine separation from the tumor antigen-bounded antibody can be very crucial. Thus, we designed a novel linker that can be cleaved by enzymes presented around the tumor site including cathepsin B, urokinase-type plasminogen activator (uPA) and matrix metalloproteinases (MMPs). Also, it can inhibit tumor progression by competing with the native substrate of key proteases in the tumor microenvironment. The proposed linker was evaluated using some bioinformatics approaches. In silico results showed that the linker is structurally stable and could be detected and cleaved using the mentioned enzymes.Communicated by Ramaswamy H. Sarma.

Phytochemical Analysis and Biological Activity of Salvia compressa Vent.

Background: Salvia extracts have various biological activities and are rich sources of bioactive metabolites. Objectives: We identified five phytochemicals from S. compressa extract and assessed its biological properties. Methods: The plant's shoots were extracted using dichloromethane, and the constituents were isolated using column chromatography. High-resolution NMR spectroscopy characterized the chemical structures of the compounds (1 - 5). The cytotoxic effect of the extract was examined against MCF-7 cells by MTT reduction assay, while cisplatin was tested as a reference cytotoxic compound. The antibacterial activity was assessed using nutrient broth micro-dilution (NBMD), and chloramphenicol was used as the positive control. Results: Citrostadienol (1), ß-sitosterol (2), two glyceride esters of linolenic, linoleic, and palmitic acids (3, 4), and geraniol (5) were isolated from S. compressa for the first time. The extract showed activity against MCF-7 breast cancer cells and reduced cell viability to 68.2 ± 13.1% compared to the control drug at the concentration of 50 µg/mL, while it was not active against seven test bacteria. Conclusions: The anti-complementary activity of the isolated steroids suggests S. compressa for future anti-inflammatory research.

Synthesis, cytotoxicity assay, pharmacokinetics, biodistribution and modeling study of cabazitaxel-dextran nanoconjugates: targeted vs non targeted delivery.

Cabazitaxel (CTX) is an anti-neoplastic agent of second-generation taxane derivatives, characterized by very low water solubility. The currently marketed formulation of CTX contains high concentrations of surfactant and ethanol, which causes severe hypersensitivity reactions in patients. To deal with aforementioned side effects, our previous study attempted to develop the prodrugs of CTX with dextran. Here our approach differs through synthesizing folate containing prodrug and also investigating cytotoxicity and pharmacokinetics parameters obtained with dextran and dextran-folate nanoconjugates versus free CTX. MCF-7 with medium folate receptor expression and MDA-MB-231 as high folate receptor expression cell lines were selected for cytotoxicity assay. Pharmacokinetics properties were studied by injecting prodrugs and CTX to Wistar rats, analyzing serum and selected tissue samples and the obtained results were sibjected to data modeling study. The size of synthesized prodrugs was mostly less than 90 nm. Folate conjugates provided higher toxicity in comparison with dextran conjugates on both cell lines. In vivo non-compartmental pharmacokinetics analysis revealed enhanced area under the curve (about 3-5 fold for different samples) and longer half-life (approximately 1.3-1.8 fold higher) which led to increased serum residence time of prodrugs in comparison to free CTX. Tissue accumulation data showed that liver was the major organ with high accumulation of CTX. The accumulation of folate conjugates was remarkably higher than dextran samples (p < 0.05 in samples of 2, 10 and 24 h). Data modeling by Principal Component Analysis (PCA) and Hierarchical Cluster models showed a significant difference between pharmacokinetics properties of CTX and prodrugs. In summary, prodrugs seem to be proper and promising CTX delivery systems as substitution for the current market formulation.

De novo design of novel protease inhibitor candidates in the treatment of SARS-CoV-2 using deep learning, docking, and molecular dynamic simulations.

The main protease of SARS-CoV-2 is a critical target for the design and development of antiviral drugs. 2.5 M compounds were used in this study to train an LSTM generative network via transfer learning in order to identify the four best candidates capable of inhibiting the main proteases in SARS-CoV-2. The network was fine-tuned over ten generations, with each generation resulting in higher binding affinity scores. The binding affinities and interactions between the selected candidates and the SARS-CoV-2 main protease are predicted using a molecular docking simulation using AutoDock Vina. The compounds selected have a strong interaction with the key MET 165 and Cys145 residues. Molecular dynamics (MD) simulations were run for 150ns to validate the docking results on the top four ligands. Additionally, root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and hydrogen bond analysis strongly support these findings. Furthermore, the MM-PBSA free energy calculations revealed that these chemical molecules have stable and favorable energies, resulting in a strong binding with Mpro's binding site. This study's extensive computational and statistical analyses indicate that the selected candidates may be used as potential inhibitors against the SARS-CoV-2 in-silico environment. However, additional in-vitro, in-vivo, and clinical trials are required to demonstrate their true efficacy.

Design, synthesis, and biological evaluation of symmetrical azine derivatives as novel tyrosinase inhibitors.

A series of symmetrical azine derivatives containing different substituted benzyl moieties were designed, synthesized, and evaluated for their inhibitory activity against tyrosinase. The results showed that compounds 3e, 3f, 3h, 3i, 3j, and 3k possess effective tyrosinase inhibition with IC50 values ranging from 7.30 µM to 62.60 µM. Particularly, compounds 3f displayed around three-fold improvement in the potency (IC50 = 7.30 ± 1.15 µM) compared to that of kojic acid (IC50 = 20.24 ± 2.28 µM) as the positive control. Kinetic study of compound 3f confirmed uncompetitive inhibitory activity towards tyrosinase indicating that it can bind to enzyme-substrate complex. Next, molecular docking analysis was performed to study the interactions and binding mode of the most potent compound 3f in the tyrosinase active site. Besides, the cytotoxicity of 3f, as well as its potency to reduce the melanin content were also measured on invasive melanoma B16F10 cell line. Also, 3f exhibited above 82% cell viability in the A375 cell line at 10 µM. Consequently, compounds 3f could be introduced as a potent tyrosinase inhibitor that might be a promising candidate in the cosmetics, medicine, and food industry.